Coca-Cola, Toucans and Charles Sanders Peirce

The crowning achievement of the semantc Web is the simple use of URIs to identify data. Further, if the URI identifier can resolve to a representation of that data, it now becomes an integral part of the HTTP access protocol of the Web while providing a unique identifier for the data. These innovations provide the basis for distributed data at global scale, all accessible via Web devices such as browsers and smartphones that are now a ubiquitous part of our daily lives.

Yet, despite these profound and simple innovations, the semantic Web’s designers and early practitioners and advocates have been mired in a muddled, metaphysical argument of at least a decade over what these URIs mean, what they reference, and what their actual true identity is. These muddles about naming and identity, it might be argued, are due to computer scientists and programmers trying to grapple with issues more properly the domain of philosophers and linguists. But that would be unfair. For philosophers and linguists themselves have for centuries also grappled with these same conundrums [1].

As I argue in this piece, part of the muddle results from attempting to do too much with URIs while another part results from not doing enough. I am also not trying to directly enter the fray of current standards deliberations. (Despite a decade of controversy, I optimistically believe that the messy process of argument and consensus building will work itself out [2].) What I am trying to do in this piece, however, is to look to one of America’s pre-eminent philosophers and logicians, Charles Sanders Peirce (pronounced “purse”), to inform how these controversies of naming, identity and meaning may be dissected and resolved.

‘Identity Crisis’, httpRange-14, and Issue 57

The Web began as a way to hyperlink between documents, generally Web pages expressed in the HTML markup language. These initial links were called URLs (uniform resource locators), and each pointed to various kinds of electronic resources (documents) that could be accessed and retrieved on the Web. These resources could be documents written in HTML or other encodings (PDFs, other electronic formats), images, streaming media like audio or videos, and the like [3].

All was well and good until the idea of the semantic Web, which postulated that information about the real world — concepts, people and things — could also be referenced and made available for reasoning and discussion on the Web. With this idea, the scope of the Web was massively expanded from electronic resources that could be downloaded and accessed via the Web to now include virtually any topic of human discourse. The rub, of course, was that ideas such as abstract concepts or people or things could not be “dereferenced” nor downloaded from the Web.

One of the first things that needed to change was to define a broader concept of a URI “identifier” above the more limited concept of a URL “locator”, since many of these new things that could be referenced on the Web went beyond electronic resources that could be accessed and viewed [3]. But, since what the referent of the URI now actually might be became uncertain — was it a concept or a Web page that could be viewed or something else? — a number of commentators began to note this uncertainty as the “identity crisis” of the Web [4]. The topic took on much fervor and metaphysical argument, such that by 2003, Sandro Hawke, a staffer of the standards-setting W3C (World Wide Web Consortium), was able to say, “This is an old issue, and people are tired of it” [5].

Yet, for many of the reasons described more fully below, the issue refused to go away. The Technical Architecture Group (TAG) of the W3C took up the issue, under a rubric that came to be known as httpRange-14 [6]. The issue was first raised in March 2002 by Tim Berners-Lee, accepted for TAG deliberations in February 2003, with then a resolution offered in June 2005 [7]. (Refer to the original resolution and other information [6] to understand the nuances of this resolution, since particular commentary on that approach is not the focus of this article.) Suffice it to say here, however, that this resolution posited an entirely new distinction of Web content into “information resources” and “non-information resources”, and also recommended the use of the HTTP 303 redirect code for when agents requesting a URI should be directed to concepts versus viewable documents.

This “resolution” has been anything but. Not only can no one clearly distinguish these de novo classes of “information resources” [19], but the whole approach felt arbitrary and kludgy.

Meanwhile, the confusions caused by the “identity crisis” and httpRange-14 continued to perpetuate themselves. In 2006, a major workshop on “Identity, Reference and the Web” (IRW 2006) was held in conjunction with the Web’s major WWW2006 conference in Edinburgh, Scotland, on May 23, 2006 [8]. The various presentations and its summary (by Harry Halpin) are very useful to understand these issues. What was starting to jell at this time was the understanding that the basis of identity and meaning on the Web posed new questions, and ones that philosophers, logicians and linguists needed to be consulted to help inform.

The fiat of the TAG’s 2005 resolution has failed to take hold. Over the ensuing years, various eruptions have occurred on mailing lists and within the TAG itself (now expressed as Issue 57) to revisit these questions and bring the steps moving forward into some coherent new understanding. Though linked data has been premised on best-practice implementation of these resolutions [9], and has been a qualified success, many (myself included) would claim that the extra steps and inefficiencies required from the TAG’s httpRange-14 guidance have been hindrances, not facilitators, of the uptake of linked data (or the semantic Web).

Today, despite the efforts of some to claim the issue closed, it is not. Issue 57 and the periodic bursts from notable semantic Web advocates such as Ian Davis [10], Pat Hayes and Harry Halpin [11], Ed Summers [12], Xiaoshu Wang [13], David Booth [14] and TAG members themselves, such as Larry Masinter [15] and Jonathan Rees [16], point to continued irresolution and discontent within the advocate community. Issue 57 currently remains open. Meanwhile, I think, all of us interested in such matters can express concern that linked data, the semantic Web and interoperable structured data have seen less uptake than any of us had hoped or wanted over the past decade. As I have stated elsewhere, unclear semantics and muddled guidelines help to undercut potential use.

As each of the eruptions over these identity issues has occurred, the competing camps have often been characterized as “talking past one another”; that is, not communicating in such a way as to help resolve to consensus. While it is hardly my position to do so, I try to encapsulate below the various positions and prejudices as I see them in this decades-long debate. I also try to share my own learning that may help inform some common ground. Forgive me if I overly simplify these vexing issues by returning to what I see as some first principles . . . .

What’s in a Name?

One legacy of the initial document Web is the perception that Web addresses have meaning. We have all heard of the multi-million dollar purchasing of domains [17] and the adjudication that may occur when domains are hijacked from their known brands or trademark owners. This legacy has tended to imbue URIs with a perceived value. It is not by accident, I believe, that many within the semantic Web and linked data communities still refer to “minting” URIs. Some believe that ownership and control over URIs may be equivalent to grabbing up valuable real estate. It is also the case that many believe the “name” given to a URI acts to name the referent to which it refers.

This perception is partially true, partially false, but moreover incomplete in all cases. We can illustrate these points with the global icon, “Coca-Cola”.

As for the naming aspects, let’s dissect what we mean when we use the label “Coca-Cola” (in a URI or otherwise). Perhaps the first thing that comes to mind is “Coca-Cola,” the beverage (which has a description on Wikipedia, among other references). Because of its ubiquity, we may also recognize the image of the Coca-Cola bottle to the left as a symbol for this same beverage. (Though, in the hilarious movie, The Gods, They Must be Crazy, Kalahari Bushmen, who had no prior experience of Coca-Cola, took the bottle to be magical with evil powers [18].) Yet even as reference to the beverage, the naming aspects are a bit cloudy since we could also use the fully qualified synonyms of “Coke”, “Coca-cola” (small C), “Classic Coke” and the hundreds of language variants worldwide.

On the other hand, the label “Coca-Cola” could just as easily conjure The Coca-Cola Company itself. Indeed, the company web site is the location pointed to by the URI of http://www.thecoca-colacompany.com/. But, even that URI, which points to the home Web page of the company, does not do justice to conveying an understanding or description of the company. For that, additional URIs may need to be invoked, such as the description at Wikipedia, the company’s own company description page, plus perhaps the company’s similar heritage page.

Of course, even these links and references only begin to scratch the surface of what the company Coca-Cola actually is: headquarters, manufacturing facilities, 140,000 employees, shareholders, management, legal entities, patents and Coke recipe, and the like. Whether in human languages or URIs, in any attempt to signify something via symbols or words (themselves another form of symbol), we risk ambiguity and incompleteness.

URI shorteners also undercut the idea that a URI necessarily “names” something. Using the service bitly, we can shorten the link to the Wikipedia description of the Coke beverage to http://bit.ly/xnbA6 and we can shorten the link to The Coca-Cola Company Web site to http://bit.ly/9ojUpL. I think we can fairly say that neither of these shortened links “name” their referents. The most we can say about a URI is that it points to something. With the vagaries of meaning in human languages, we might also say that URIs refer to something, denote something or identify (but not in the sense of completely define) something.

From this discussion, we can assert with respect to the use of URIs as “names” that:

1. In all cases, URIs are pointers to a particular referent
2. In some cases, URIs do act to “name” some things
3. Yet, even when used as “names,” there can be ambiguity as to what exactly the referent is that is denoted by the name
4. Resolving what such “names” mean is a matter of context and reference to further information or links, and
5. Because URIs may act as “names”, it is appropriate to consider social conventions and contracts (e.g., trademarks, brands, legal status) in adjudicating who can own the URI.

In summary, I think we can say that URIs may act as names, but not in all or most cases, and when used as such are often ambiguous. Absolutely associating URIs as names is way too heavy a burden, and incorrect in most cases.

What is a Resource?

The “name” discussion above masks that in some cases we are talking about a readable Web document or image (such as the Wikipedia description of the Coke beverage or its image) versus the “actual” thing in the real world (the Coke beverage itself or even the company). This distinction is what led to the so-called “identity crisis”, for which Ian Davis has used a toucan as his illustrative thing [10].

As I note in the conclusion, I like Davis’ approach to the identity conundrum insofar as Web architecture and linked data guidance are concerned. But here my purpose is more subtle: I want to tease apart still further the apparent distinction between an electronic description of something on the Web and the “actual” something. Like Davis, let’s use the toucan.

In our strawman case, we too use a description of the toucan (on Wikipedia) to represent our “information resource” (the accessible, downloadable electronic document). We contrast to that a URI that we mean to convey the actual physical bird (a “non-information resource” in the jumbled jargon of httpRange-14), which we will designate via the URI of http://example.com/toucan.

Despite the tortured (and newly conjured) distinction between “information resource” and “non-information resource”, the first blush reaction is that, sure, there is a difference between an electronic representation that can be accessed and viewed on the Web and its true, “actual” thing. Of course people can not actually be rendered and downloaded on the Web, but their bios and descriptions and portrait images may. While in the abstract such distinctions appear true and obvious, in the specifics that get presented to experts, there is surprising disagreement as to what is actually an “information resource” v. a “non-information resource” [19]. Moreover, as we inspect the real toucan further, even that distinction is quite ambiguous.

When we inspect what might be a definitive description of “toucan” on Wikipedia, we see that the term more broadly represents the family of Ramphastidae, which contains five genera and forty different species. The picture we are showing to the right is but of one of those forty species, that of the keel-billed toucan (Ramphastos sulfuratus). Viewing the images of the full list of toucan species shows just how divergent these various “physical birds” are from one another. Across all species, average sizes vary by more than a factor of three with great variation in bill sizes, coloration and range. Further, if I assert that the picture to the right is actually that of my pet keel-billed toucan, Pretty Bird, then we can also understand that this representation is for a specific individual bird, and not the physical keel-billed toucan species as a whole.

The point of this diversion is not a lecture on toucans, but an affirmation that distinctions between “resources” occur at multiple levels and dimensions. Just as there is no self-evident criteria as to what constitutes an “information resource”, there is also not a self-evident and fully defining set of criteria as to what is the physical “toucan” bird. The meaning of what we call a “toucan” bird is not embodied in its label or even its name, but in the context and accompanying referential information that place the given referent into a context that can be communicated and understood. A URI points to (“refers to”) something that causes us to conjure up an understanding of that thing, be it a general description of a toucan, a picture of a toucan, an understanding of a species of toucan, or a specific toucan bird. Our understanding or interpretation results from the context and surrounding information accompanying the reference.

In other words, a “resource” may be anything, which is just the way the W3C has defined it. There is not a single dimension which, magically, like “information” and “non-information,” can cleanly and definitely place a referent into some state of absolute understanding. To assert that such magic distinctions exist is a flaw of Cartesian logic, which can only be reconciled by looking to more defensible bases in logic [20].

Peirce and the Logic of Signs

The logic behind these distinctions and nuances leads us to Charles Sanders Peirce (1839 – 1914). Peirce (pronounced “purse”) was an American logician, philosopher and polymath of the first rank. Along with Frege, he is acknowledged as the father of predicate calculus and the notation system that formed the basis of first-order logic. His symbology and approach arguably provide the logical basis for description logics and other aspects underlying the semantic Web building blocks of the RDF data model and, eventually, the OWL language. Peirce is the acknowledged founder of pragmatism, the philosophy of linking practice and theory in a process akin to the scientific method. He was also the first formulator of existential graphs, an essential basis to the whole field now known as model theory. Though often overlooked in the 20th century, Peirce has lately been enjoying a renaissance with his voluminous writings still being deciphered and published.

The core of Peirce’s world view is based in semiotics, the study and logic of signs. In his seminal writing on this, “What is in a Sign?” [21], he wrote that “every intellectual operation involves a triad of symbols” and “all reasoning is an interpretation of signs of some kind”. Peirce had a predilection for expressing his ideas in “threes” throughout his writings.

Semiotics is often split into three branches: 1) syntactics – relations among signs in formal structures; 2) semantics – relations between signs and the things to which they refer; and 3) pragmatics – relations between signs and the effects they have on the people or agents who use them.

Peirce’s logic of signs in fact is a taxonomy of sign relations, in which signs get reified and expanded via still further signs, ultimately leading to communication, understanding and an approximation of “canonical” truth. Peirce saw the scientific method as itself an example of this process.

A given sign is a representation amongst the triad of the sign itself (which Peirce called a representamen, the actual signifying item that stands in a well-defined kind of relation to the two other things), its object and its interpretant. The object is the actual thing itself. The interpretant is how the agent or the perceiver of the sign understands and interprets the sign. Depending on the context and use, a sign (or representamen) may be either an icon (a likeness), an indicator or index (a pointer or physical linkage to the object) or a symbol (understood convention that represents the object, such as a word or other meaningful signifier).

An interpretant in its barest form is a sign’s meaning, implication, or ramification. For a sign to be effective, it must represent an object in such a way that it is understood and used again. This makes the assignment and use of signs a community process of understanding and acceptance [20], as well as a truth-verifying exercise of testing and confirming accepted associations.

John Sowa has done much to help make some of Peirce’s obscure language and terminology more accessible to lay readers [22]. He has expressed Peirce’s basic triad of sign relations as follows, based around the Yojo animist cat figure used by the character Queequeg in Herman Melville’s Moby-Dick:

In this figure, object and symbol are the same as the Peirce triad; concept is the interpretant in this case. The use of the word ‘Yojo’ conjures the concept of cat.

This basic triad representation has been used in many contexts, with various replacements or terms at the nodes. Its basic form is known as the Meaning Triangle, as was popularized by Ogden and Richards in 1923 [23].

The key aspect of signs for Peirce, though, is the ongoing process of interpretation and reference to further signs, a process he called semiosis. A sign of an object leads to interpretants, which, as signs, then lead to further interpretants. In the Sowa example below, we show how meaning triangles can be linked to one another, in this case by abstracting that the triangles themselves are concepts of representation; we can abstract the ideas of both concept and symbol:

We can apply this same cascade of interpretation to the idea of the sign (or representamen), which in this case shows that a name can be related to a word symbol, which in itself is a combination of characters in a string called ‘Yojo’:

According to Sowa [22]:

Catherine Legg [20] notes that the semiotic process is really one of community involvement and consensus. Each understanding of a sign and each subsequent interpretation helps come to a consensus of what a sign means. It is a way of building a shared understanding that aids communication and effective interpretation. In Peirce’s own writings, the process of interpretation can lead to validation and an eventual “canonical” or normative interpretation. The scientific method itself is an extreme form of the semiotic process, leading ultimately to what might be called accepted “truths”.

Peircean Semiotics of URIs

So, how do Peircean semiotics help inform us about the role and use of URIs? Does this logic help provide guidance on the “identity crisis”?

The Peircean taxonomy of signs has three levels with three possible sign roles at each level, leading to a possible 27 combinations of sign representations. However, because not all sign roles are applicable at all levels, Peirce actually postulated only ten distinct sign representations.

Common to all roles, the URI “sign” is best seen as an index: the URI is a pointer to a representation of some form, be it electronic or otherwise. This representation bears a relation to the actual thing that this referent represents, as is true for all triadic sign relationships. However, in some contexts, again in keeping with additional signs interpreting signs in other roles, the URI “sign” may also play the role of a symbolic “name” or even as a signal that the resource can be downloaded or accessed in electronic form. In other words, by virtue of the conventions that we choose to assign to our signs, we can supply additional information that augments our understanding of what the URI is, what it means, and how it is accessed.

Of course, in these regards, a URI is no different than any other sign in the Peircean world view: it must reside in a triadic relationship to its actual object and an interpretation of that object, with further understanding only coming about by the addition of further signs and interpretations.

In shortened form, this means that a URI, acting alone, can at most play the role of a pointer between an object and its referent. A URI alone, without further signs (information), can not inform us well about names or even what type of resource may be at hand. For these interpretations to be reliable, more information must be layered on, either by accepted convention of the current signs or the addition of still further signs and their interpretations. Since the attempts to deal with the nature of a URI resource by fiat as stipulated by httpRange-14 neither meet the standards of consensus nor empirical validity, the attempt can not by definition become “canonical”. This does not mean that httpRange-14 and its recommended practices can not help in providing more information and aiding interpretation for what the nature of a resource may be. But it does mean that httpRange-14 acting alone is insufficient to resolve ambiguity.

Moreover, what we see in the general nature of Peirce’s logic of signs is the usefulness of adding more “triads” of representation as the process to increase understanding through further interpretation. Kind of sounds like adding on more RDF triples, does it not?

Global is Neither Indiscriminate Nor Unambiguous

Names, references, identity and meaning are not absolutes. They are not philosophically, and they are not in human language. To expect machine communications to hold to different standards and laws than human communications is naive. To effect machine communications our challenge is not to devise new rules, but to observe and apply the best rules and practices that human communications instruct.

There has been an unstated hope at the heart of the semantic Web enterprise that simply expressing statements in the right way (syntax) and in the right form (RDF) is sufficient to facilitate machine communications. But this hope, too, is naive and silly. Just as we do not accept all human utterances as truth, neither will we accept all machine transmissions as reliable. Some of the information will be posted in error; some will be wrong or ill-fitting to our world view; some will be malicious or intended to deceive. Spam and occasionally lousy search results on the Web tell us that Web documents are subject to these sources of unsuitability, why is not the same true of data?

Thus, global data access via the semantic Web is not — and can never be — indiscriminate nor unambiguous. We need to understand and come to trust sources and provenance; we need interpretation and context to decide appropriateness and validity; and we need testing and validation to ensure messages as received are indeed correct. Humans need to do these things in their normal courses of interaction and communication; our machine systems will need to do the same.

These confirmations and decisions as to whether the information we receive is actionable or not will come about via still more information. Some of this information may come about via shared conventions. But most will come about because we choose to provide more context and interpretation for the core messages we hope to communicate.

A Go-Forward Approach

Nearly five years ago Hayes and Halpin put forth a proposal to add ex:refersTo and ex:describedBy to the standard RDF vocabulary as a way for authors to provide context and explanation for what constituted a specific RDF resource [11]. In various ways, many of the other individuals cited in this article have come to similar conclusions. The simple redirect suggestions of both Ian Davis [10] and Ed Summers [12] appear particularly helpful.

Over time, we will likely need further representations about resources regarding such things as source, provenance, context and other interpretations that would help remove ambiguities as to how the information provided by that resource should be consumed or used. These additional interpretations can mechanically be provided via referenced ontologies or embedded RDFa (or similar). These additional interpretations can also be aided by judicious, limited additions of new predicates to basic language specifications for RDF (such as the Hayes and Halpin suggestions).

In the end, of course, any frameworks that achieve consensus and become widely adopted will be simple to use, easy to understand, and straightforward to deploy. The beauty of best practices in predicates and annotations is that failures to provide are easy to test. Parties that wish to have their data consumed have incentive to provide sufficient information so as to enable interpretation.

There is absolutely no reason that these additions can not co-exist with the current httpRange-14 approach. By adding a few other options and making clear the optional use of httpRange-14, we would be very Peirce-like in our go-forward approach: We are being both pragmatic while we add more means to improve our interpretations for what a Web resource is and is meant to be.

Contrary to Some Views, Google and Co.’s Microdata Effort will Also Boost RDF

In my opinion, perhaps the most important event for the structured Web since RDF was released a dozen years ago was today’s joint announcement by the search engine triumvirate of Google, Bing and Yahoo! releasing Schema.org. Schema.org is a vendor specification for nearly 300 mini-schema (or structured record definitions) that can be used to tag information in Web pages. These schema are organized into a clean little hierarchy and cover many of the leading things — from organizations to people to products and creative works — that can be written about and characterized on the Web.

These schema specifications are based on the microdata standard presently under review as part of the pending HTML5 specification. Microdata are set record descriptions of key-value pair attributes that can be embedded into the HTML Web page language. These microdata schema are similar to microformats, but broader in coverage and extensible. Microdata is also simpler than RDFa, another W3C specification that the Schema.org organizers call “. . . extensible and very expressive, but the substantial complexity of the language has contributed to slower adoption.”

Is the Initiative a Slap in RDF’s Face?

Various forums have been alive with howls and questions from many RDF and RDFa advocates that this initiative negates years of effort behind those formats. Yet I and my company, Structured Dynamics, which base our entire technology approach on semantics and RDF, do not see this announcement as a threat or rejection. What gives; what is the difference in perspective?

In our view, RDF and its triple representations in its data model, is the simplest and most expressive means to represent any data or any data relationship. As such, RDF, and its language extensions such as OWL and ontologies, provide a robust and flexible canonical data model for capturing any extant data or schema. No matter what the native form of the source information, we can boil it down to RDF and inter-relate it to any other information. It is for these reasons (and others) we have frequently termed RDF as the universal data solvent.

But, simple records and simple data need not be encumbered with the complexity of RDF. We have long argued for the importance of naive data structs. Many of these are simple key-value pairs where the subject is implied. The so-called little structured data records in Wikipedia, called infoboxes, are of this form. JSON and many simple data formats also have cleaner data formats.

The basic fact that RDF provides a universal data model for any kind of native data does not necessarily translate into its use as the actual data exchange format. Rather, winning data exchange formats are those that can be easily understood, easily expressed and therefore widely used. I think there is a real prospect that microdata, ready for ingest and expression by the Web’s leading search engines, may represent a real sea change in the availability and expression of structured data on the Web.

More structure — not less — is the real fuel that will promote greater adoption of RDF when it comes time to interoperate that data. The RDF community should rejoice that more structure will be coming to the Web from Google et al.’s announcement. We should also soon see an explosion of tools and utilities and services that make it easy to automatically add such structure to Web pages via single clicks. Then, once this structure is available, watch out!

So, while the backers of Schema.org also announced their continued support for microformats and RDFa as they presently exist, I rather suspect today’s announcement represents a denouement for these alternative formats. Though these formats may be creatively destroyed, I think the effect on RDF itself will be a profound and significant boost. I foresee clarity coming to the marketplace regarding RDF’s role:  as a canonical means for expressing data of any form, and not necessarily as a data exchange format.

The Initiative is No Surprise

This initiative, led by Google, should be no surprise. Google is the registered agent for the Schema.org Web site and has been the key proponent of microdata via its support of Ian Hickson in the WhatWG and HTML5 work groups. As I stated a couple of years back, Google has also not hidden its interests in structured data. Practically daily we see more structured data appear in Google search results and it has maintained a very active program in structured data extraction from text and tables for some years.

Google and its search engine partners recognize that search needs are evolving from keyword retrievals to structure, relationships, and filtered, targeted results. Those advances come from structure — as well as the semantic relationships between things that something like the Schema.org begins to represent.

Many within the W3C and elsewhere questioned why Google was pushing microdata when there were competing options such as microformats or RDFa (or even earlier variants). Of course, like Microsoft of a decade earlier, some ascribed Google’s microdata advocacy as arising from commercial interests or clout in advertising alone. Of course Google has an economic interest in the growth and usefulness of the Web. But I do not believe its advocacy to be premised on clout or “my way or the highway.”

Google and the search engine triumvirate understand well — much better than many of the researchers and academics that dominate mailing list discussions — that use and adoption trump elegance and sophistication. When one deconstructs the design of microdata and the nearly 300 schema now released behind it, I think the pragmatic observer can only come to one conclusion: Job well done!

Why This is Exciting

I have been a fervent RDF advocate for nearly a decade and have also been a vocal proponent of the structured Web as a necessary stepping stone to the semantic Web. In fact, here is a repeat of a diagram I have used many times over the past 5 years:

Document Web	Structured Web		Semantic Web
		Linked Data
Document-centric Document resources Unstructured data and semi-structured data HTML URL-centric circa 1993	Data-centric Structured data Semi-structured data and structured data XML, JSON, RDF, etc URI-centric circa 2003	Data-centric Linked data Semi-structured data and structured data RDF, RDF-S URI-centric circa 2007	Data-centric Linked data Semi-structured data and structured data RDF, RDF-S, OWL URI-centric circa ???

When one looks at the schema of schema that accompany today’s announcement, it is really clear just how encompassing and important these instant standards will become:

Today’s announcement is the best news I have heard in years regarding the structured Web, RDF, and the semantic Web. This announcement is — I believe — the signal event of the structured Web. With regard to my longstanding diagram above, I can go to bed tonight knowing we have now crossed the threshold into the semantic Web.

Exposing $4.7 Trillion Annually in Undervalued Information

Something strange began to happen with company valuations beginning twenty to thirty years ago. Book values increasingly began to diverge — go lower — from stock prices or acquisition prices. Between 1982 and 1992 the ratio of book value to market value decreased from 62% to 38% for public US companies [1]. The why of this mystery has largely been solved, but what to do about it has not. Significantly, semantic technologies and approaches offer both a rationale and an imperative for how to get the enterprises’ books back in order. In the process, semantics may also provide a basis for more productive management and increased valuations for enterprises as well.

The mystery of diverging value resides in the importance of information in an information economy. Unlike the historical and traditional ways of measuring a company’s assets — based on the tangible factors of labor, capital, land and equipment — information is an intangible asset. As such, it is harder to see, understand and evaluate than other assets. Conventionally, and still the more common accounting practice, intangible assets are divided into goodwill, legal (intellectual property and trade secrets) and competitive (know-how) intangibles. But — given that intangibles now equal or exceed the value of tangible assets in advanced economies — we will focus instead on the information component of these assets.

As used herein, information is taken to be any data that is presented in a form useful to recipients (as contrasted to the more technical definition of Shannon and Weaver [2]). While it is true that the there is always a question of whether the collection or development of information is a cost or represents an investment, that “information” is of growing importance and value to the enterprise is certain.

The importance of this information focus can be demonstrated by two telling facts, which I elaborate below. First, only five to seven percent of existing information is adequately used by most enterprises. And, second, the global value of this information amounts to perhaps a range of $2.0 trillion to $7.4 trillion annually (yes, trillions with a T)! It is frankly unbelievable that assets of such enormous magnitude are so poorly understood, exploited or managed.

Amongst all corporate resources and assets, information is surely the least understood and certainly the least managed. We value what we measure, and measure what we value. To say that we little measure information — its generation, its use (or lack thereof) or its value — means we are attempting to manage our enterprises with one eye closed and one arm tied behind our backs. Semantic approaches offer us one way, perhaps the best way, to bring understanding to this asset and then to leverage its value.

The Seven “Laws” of Information

More than a decade ago Moody and Walsh put forward a seminal paper on the seven “laws” of information [3]. Unlike other assets, information has some unique characteristics that make understanding its importance and valuing it much more difficult than other assets. Since I think it a shame that this excellent paper has received little attention and few citations, let me devote some space to covering these “laws”.

Like traditional factors of production — land, labor, capital — it is critical to understand the nature of this asset of “information”. As the laws below make clear, the nature of “information” is totally unique with respect to other factors of production. Note I have taken some liberty and done some updating on the wording and emphasis of the Moody and Walsh “laws” to accommodate recent learnings and understandings.

Law #1: Information Is (Infinitely) Shareable

Information is not friable and can not be depleted. Using or consuming it has no direct affect on others using or consuming it and only using portions of it does not undermine the whole of it. Using it does not cause a degradation or loss of function from its original state. Indeed, information is actually not “consumed” at all (in the conventional sense of the term); rather, it is “shared”. And, absent other barriers, information can be shared infinitely. The access and
use to information on the Web demonstrates this truth daily.

Thus, perhaps the most salient characteristic of information as an asset is that it can be shared between any number of people, business areas and organizations without loss of value to any party (absent the importance of confidentiality or secrecy, which is another information factor altogether). The sharability or maintenance of value irrespective of use makes information quite different to how other assets behave. There is no dilution from use. As Moody and Walsh put it, “from the firm’s perspective, value is therefore cumulative rather than apportioned across different users.”

In practice, however, this very uniqueness is also a cause of other organizational challenges. Both personal and institutional barriers get erected to limit sharing since “knowledge is power.” One perverse effect of information hoarding or lack of institutional support for sharing is to force the development anew of similar information. When not shared, existing information becomes a cost, and one that can get duplicated many times over.

Law #2: The Value of Information Increases With Use

Most resources degrade with use, such as equipment wearing out. In contrast, the per unit value of information increases with use. The major cost of information is in its capture, storage and maintenance. The actual variable costs of using the information (particularly digital information) is, in essence, zero. Thus, with greater use, the per unit cost of information drops.

There is a corollary to this that also goes to the heart of the question of information as an asset. From an accounting point of view, something can only be an asset if it provides future economic value. If information is not used, it cannot possibly result in such benefits and is therefore not an asset. Unused information is really a liability, because no value is extracted from it. In such cases the costs of capture, storage and maintenance are incurred, but with no realized
value. Without use, information is solely a cost to the enterprise.

The additional corollary is that awareness of the information’s existence is an essential requirement in order to obtain this value. As Moody and Walsh state, “information is at its highest ‘potential’ when everyone in the organization knows where it is, has access to it and knows how to use it. Information is at its lowest ‘potential’ when people don’t even know it is there.”

A still further corollary is the importance of information literacy. Awareness of information without an understanding of where it fits or how to take advantage of it also means its value is hidden to potential users. Thus, in addition to awareness, training and documentation are important factors to help ensure adequate use. Both of these factors
may seem like additional costs to the enterprise beyond capture, storage and maintenance, but — without them — no or little value will be leveraged and the information will remain a sunk cost.

Law #3: Information is Perishable

Like most other assets, the value of information tends to depreciate over time [4]. Some information has a short shelf life (such as Web visitations); other has a long shelf life (patents, contracts and many trade secrets). Proper valuation of information should take into account such differences in operational life, analysis or decision life, and statutory life. Operational shelf life tends to be the shortest.

In these regards, information is not too dissimilar from other asset types. The most important point is to be cognizant of use and shelf differences amongst different kinds of information. This consideration is also traded off against the declining costs of digital information storage.

Law #4: The Value of Information Increases With Accuracy

A standard dictum is that the value of information increases with accuracy. The caveat, however, is that some information, because it is not operationally dependent or critical to the strategic interests of the firm, actually can become a cost when capture costs exceed value. Understanding such Pareto principles is an important criterion in evaluating information approaches. Generally, information closest to the transactional or business purpose of the organization will demand higher accuracy.

Such statements may sound like platitudes — and are — in the absence of an understanding of information dependencies within the firm. But, when certain kinds of information are critical to the enterprise, it is just as important to know the accuracy of the information feeding that “engine” as it is for oil changes or maintenance schedules for physical engines. Thus an understanding of accuracy requirements in information should be a deliberate management focus for critical business functions. It is the rare firm that attends to such imperatives today.

Law #5: The Value of Information Increases in Combination

A unique contribution from semantic approaches — and perhaps the one resulting in the highest valuation benefit — arises from the increased value of connecting the information. We have come to understand this intimately as the “network effect” from interconnected nodes on a network. It also arises when existing information is connected as well.

Today’s enterprise information environment is often described by many as unconnected “silos”. Scattered databases and spreadsheets and other information repositories litter the information landscape. Not only are these sources unconnected and isolated, but they also may describe similar information in different and inconsistent ways.

As I have described previously in The Law of Linked Data [5], existing information can act as nodes that — once connected to one another — tend to produce a similar network effect to what physical networks exhibit with increasing numbers of users. Of course, the nature of the information that is being connected and its centrality to the mission of the enterprise will greatly affect the value of new connections. But, based on evidence to date, the value of information appears to go up somewhere between a quadratic and exponential function for the number of new connections. This value is especially evident in know-how and competitive areas.

Law #6: More Is Not Necessarily Better

Information overload is a well-known problem. On the other hand, lack of appropriate information is also a compelling problem. The question of information is thus one of relevancy. Too much irrelevant information is a bad thing, as is too little relevant information.

These observations lead to two use considerations. First, means to understand and focus information capture on relevant information is critical. And, second, information management systems should be purposefully designed with user interfaces for easy filtering of irrelevant information.

The latter point sounds straightforward, but, in actuality, requires a semantic underpinning to the enterprise’s information assets. This requirement is because relevancy is in the eye of the beholder, and different users have different terms, perspectives, and world views by which information evaluation occurs. In order for useful filtering, information must be presented in similar terms and perspectives relevant to those users. Since multiple studies affirm that information decision-makers seek more information beyond their overload points [3], it is thus more useful to provide relevant access and filtering methods that can be tailored by user rather than “top down” information restrictions.

Law #7: Information is Self-propagating

With access and connections, information tends to beget more information. This propagation results from summations, analysis, unique combinations and other ways that basic datum get recombined into new datum. Thus, while the first law noted that information can not be consumed (or depleted) by virtue of its use, we can also say that information tends to reproduce and expand itself via use and inspection.

Indeed, knowledge itself is the result of how information in its native state can be combined and re-organized to derive new insights. From a valuation standpoint, it is this very understanding that leads to such things as competitive intelligence or new know-how. In combination with insights from connections, this propagating factor of information is the other leading source of intangible asset valuations.

This law also points to the fact that information per se is not a scarce resource. (Though its availability may be scarce.) Once available, techniques like data mining, analysis, visualization and so forth can be rich sources for generating new information from existing holdings of data.

Information as an Asset and How to Value

These “laws” — or perspectives — help to frame the imperatives for how to judge information as an asset and its resulting value. The methodological and conceptual issues of how to explicitly account for information on a company’s books are, of course, matters best left to economists and professional accountants. With the growing share of information in relation to intangible assets, this would appear to be a matter of great importance to national policy. Accounting for R&D efforts as an asset versus a cost, for example, has been estimated to add on the order of 11 percent to US national GDP estimates [9].

The mere generation of information is not necessarily an asset, as the “laws” above indicate. Some of the information has no value and some indeed represents a net sunk cost. What we can say, however, is that valuable information that is created by the enterprise but remains unused or is duplicated means that what was potentially an asset has now been turned into a cost — sometimes a cost repeated many-fold.

Information that is used is an asset, intangible or not. Here, depending on the nature of the information and its use, it can be valued on the basis of cost (historical cost or what it cost to develop it), market value (what others will pay for it), or utility (what is its present value as benefits accrue into the future). Traditionally the historical cost method has been applied to information. Yet, since information can both be sold and still retained by the organization, it may have both market value and utility value, with its total value being the sum.

In looking at these factors, Moody and Walsh propose a number of new guidelines in keeping with the “laws” noted above [3]:

• Operational information should be measured as the cost of collection using data entry costs
• Management information should be valued based on what it cost to extract the data from operational systems
• Redundant data should be considered to have zero value (Law #1)
• Unused data should be considered to have zero value (Law #2)
• The number of users and number of accesses to the data should be used to multiply the value of the information (Law #2). When information is used for the first time, it should be valued at its cost of collection; subsequent uses should add to this value (perhaps on a depreciated basis; see below)
• The value of information should be depreciated based on its “shelf life” (Law #3)
• The value of information should be discounted by its accuracy relative to what is considered to be acceptable (Law #4)
• And, as an added factor, information that is effectively linked or combined should have its value multiplied (Law #5), though the actual multiplier may be uncertain [5].

The net result of thinking about information in this more purposeful way is to encourage more accurate valuation methods, and to provide incentives for more use and re-use, particularly in combined ways. Such methods can also help distinguish what information is of more value to the organization, and therefore worthy of more attention and investment.

The Growing Importance of Intangible Information

The emerging discrepancy between market capitalizations and book values began to get concerted academic attention in the 1990s. To be sure, perceptions by the market and of future earnings potential can always  color these differences. The simple occurrence of a discrepancy is not itself proof of erroneous or inaccurate valuations. (And, the corollary is that the degree of the discrepancy is not sufficient alone to estimate the intangible asset “gap”, a logical error made by many proponents.) But, the fact that these discrepancies had been growing and appeared to be based (in part) on structural changes linked to intangibles was creating attention.

Leonard Nakamura, an economist with the Federal Reserve Board in Philadelphia, published a working paper in 2001 entitled, “What is the U.S. Gross investment in Intangibles?  (At Least) One Trillion Dollars a Year!” [6]. This was one of the first attempts to measure intangible investments, which he defined as private expenditures on assets that are intangible and necessary to the creation and sale of new or improved products and processes, including designs, software, blueprints, ideas, artistic expressions, recipes, and the like. Nakamura acknowledged his work as being preliminary. But he did find direct and indirect empirical evidence to show that US private firms were investing at least $1 trillion annually (as of 2000, the basis year for the data) in intangible assets.  Private expenditures, labor and corporate operating margins were the three measurement methods.  The study also suggested that the capital stock of intangibles in the US has an equilibrium market value of at least $5 trillion.

Another key group — Carol Corrado, Charles Hulten, and Daniel Sichel, known as “CHS” across their many studies — also began to systematically evaluate the extent and basis for intangible assets and its discrepancy [7].  They estimated that spending on long-lasting knowledge capital — not just intangibles broadly — grew relative to other major components of aggregate demand during the 1990s. CHS was the first to show that by the turn of the millenium that fixed US investment in intangibles was at least as large as business investment in traditional, tangible capital.

By later in the decade, Nakamura was able to gather and analyze time series data that showed the steady increase in the contributions of intangibles [8]:

One can see the cross-over point late in the decade. Investment in intangibles he now estimates to be on the order of 8% to 10% of GDP annually in the US.

Roughly at the same time the National Academies in the US was commissioned to investigate the policy questions of intangible assets. The resulting major study [9] contains much relevant information. But it, too, contained an update by CHS on their slightly different approach to analyzing the growing role of intangible assets:

This CHS analysis shows similar trends to what Nakamura found, though the degree of intangible contributions is estimated as higher (~14% of annual GDP today), with investments in intangibles exceeding tangible assets somewhat earlier.

Surveys of more than 5,000 companies in 25 companies confirmed these trends from a different perspective, and also showed that most of these assets did not get reflected in financial statements. A large portion of this value was due to “brands” and other market intangibles [10]. The total “undisclosed” portion appeared to equal or exceed total
reported assets. Figures for the US indicated there might be a cumulative basis of intangible assets of $9.2 trillion [11].

In parallel, these groups and others began to decompose the intangible asset growth by country, sector, or asset type. The specific component of “information” received a great deal of attention. Uday Apte, Uday Karmarkar and Hiranya Nath, in particular, conducted a couple of important studies during this decade [12,13]. For example, they found nearly two-thirds of recent US GDP was due to information or knowledge industry contributions, a percentage that had been growing over time. They also found that a secondary sector of information internal to firms itself constituted well over 40% of the information economy, or some 28% of the entire economy. So the information activities internal to organizations and institutions represent a very large part of the economy.

The specific components that can constitute the informational portion of intangible assets has also been looked at by many investigators, importantly including key accounting groups. FASB, for example, has specific guidance on treatment of intangible assets in SFAS 141 [14]. Two-thirds of the 90 specific intangible items listed by the American Institute of Certified Public Accountants are directly related to information (as opposed to contracts, brands or goodwill), as shown in [15]. There has also been some good analysis by CHS on breakdowns by intangible assets categories [16]. There are also considerable differences by country on various aspects of these measures (for example, [10]). For example, according to OECD figures from 2002, expenditures for knowledge (R&D, education and software) ranged from nearly 7 percent (Sweden) to below 2 percent (Greece) in OECD countries, with the average of about 4 percent and the US at over 6 percent [17].

. . . Plus Too Much Information Goes Unused

The common view is that a typical organization only uses 5 to 7 percent of the information it already has on hand [18], and 20% to 25% of a knowledge worker’s time is spent simply trying to find information [19]. To probe these issues more deeply, I began a series of analyses in 2004 looking at how much money was being spent on preparing documents within US companies, and how much of that investment was being wasted or not re-used [20]. One key finding from that study was that the information within documents in the US represent about a third of total gross domestic product, or an amount equal at the time of the study to about $3.3 trillion annually (in 2010 figures, that would be closer to $4.7 trillion). This level of investment is consistent with the results of Apte et al. and others as noted above.

However, for various reasons — mostly due to lack of awareness and re-use — some 25% of those trillions of dollar spent annually on document creation costs are wasted. If we could just find the information and re-use it, massive benefits could accrue, as these breakdowns in key areas show:

Table. Mid-range Estimates for the Annual Value of Documents, U.S. Firms, 2002 [20]

The total benefit from improved document access and use to the U.S economy is on the order of 8% of GDP. For the 1,000 largest U.S. firms, benefits from these improvements can approach nearly $250 million annually per firm (2002 basis). About three-quarters of these benefits arise from not re-creating the intellectual capital already invested in prior document creation. About one-quarter of the benefits are due to reduced regulatory non-compliance or paperwork, or better competitiveness in obtaining solicited grants and contracts.

This overall value of document use and creation is quite in line with the analyses of intangible assets noted above, and which arose from totally different analytical bases and data. This triangulation brings confidence that true trends in the growing importance of information have been identified.

How Big is the Information Asset Gap?

These various estimates can now be combined to provide an assessment of just how large the “gap” is for the overlooked accounting and use of information assets:

Depending, these estimates can either be viewed as being too optimistic about the importance of information assets [25] or too conservative [26]. The breadth of the ranges of these values is itself an expression of the uncertainty in the numbers and the analysis.

The analysis shows that, globally, the value of unused and unaccounted information assets may be on the order of  $2.0 trillion to $7.4 trillion annually, with a mid-range value of $4.7 trillion. Even considering uncertainties, these are huge, huge numbers by any account. For the US alone, this range is $750 billion to $2.6 trillion annually. The analysis from the prior studies [20] would strongly suggest the higher end of this range is more likely than the lower. Similarly large gaps likely occur within the European Union and within other advanced nations. For individual firms, depending on size, the benefits of understanding and closing these gaps can readily be measured in the millions to billions [27].

At the high end, these estimates suggest that perhaps as much as 10% of global expenditures is wasted and unaccounted for due to information-related activities. This is roughly equivalent to adding a half of the US economy to the global picture.

In the concluding section, we touch on why such huge holes may appear in the world’s financial books. Clearly, though, even with uncertain and heroic assumptions, the magnitude of this gap is huge, with compelling needs to understand and close it as soon as possible.

The Relationship to Semantic Technologies

The seven Moody and Walsh information “laws” provide the clues to the reasons why we are not properly accounting for information and why we inadequately use it:

• We don’t know what information we have and can not find it
• What we have we don’t connect
• We misallocate resources for generating, capturing and storing information, because we don’t understand its value and potential
• We don’t manage the use of information or its re-use
• We duplicate efforts
• We inadequately leverage what information we have and miss valuable (that is, can be “valuated”) insights that could be gained.

Fundamentally, because information is not understood in our bones as central to the well-being of our enterprises, we continue to view the generation, capture and maintenance of information as a “cost” and not an “asset”.

I have maintained for some time an interactive information timeline [28] that attempts to encompass the entire human history of information innovations. For tens of thousands of years steady — yet slow — progress in the ways to express and manage information can be seen in this timeline. But, then, beginning with electricity and then digitization, the pace of innovation explodes.

The same timeframe that sees the importance of intangible assets appear on national and firm accounts is when we see the full digitization of information and its ability to be communicated and linked over digital networks. A very insightful figure by Rama Hoetzlein for his thesis in 2007, which I have modified and enhanced, captures this evolution with some estimated dates as is shown below (click to expand) [29]:

The first insight this figure provides is that all forms of information are now available in digital form. This includes unstructured (images and documents), semi-structured (mark-up and “tagged” information) and structured (database and spreadsheet) information. This information can now be stored and communicated over digital networks with broadly accepted protocols.

But the most salient insight is that we now have the means through semantic technologies and approaches to interrelate all of this information together. Tagging and extraction methods enable us to generate metadata for unstructured documents and content. Data models based on predicate logic and semantic logics give us the flexible means to express the relationships and connections between information. And all of this can be stored and manipulated through graph-based datastores and languages such that we can draw inferences and gain insights. Plus, since all of this is now accessible via the Web and browsers, virtually any user can access, use and leverage this information.

This figure and its dates not only shows where we have come as a species in our use and sophistication with information, but how we need to bring it all together using semantics to complete our transition to a knowledge economy.

The very same metadata and semantic tagging capabilities that enable us to interrelate the information itself also provides the techniques by which we can monitor and track usage and provenance. It is through these additional semantic methods that we can finally begin to gain insight as to what information is of what value and to whom. Tapping this information will complete the circle for how we can also begin to properly valuate and then manage and optimize our information assets.

Conclusion

With our transition to an information economy, we now see that intangible assets exceed the value of tangible ones. We see that the information component of these intangibles represent one-third to two-thirds of these intangibles. In other words, information makes up from 17% to more than one-third of an individual firm’s value in modern economies. Further, we see that at least 25% of firm expenditures on information is wasted, keeping it as a cost and negating its value as an asset.

The “factories” of the modern information economy no longer produce pins with the fixed inputs of labor and capital as in the time of Adam Smith. They rather produce information and knowledge and know-how. Yet our management and accounting systems seem fixed in the techniques of yesteryear. The quaint idea of total factor productivity as a “residual” merely belies our ignorance about the causes of economic growth and firm value. These are issues that should rightly occupy the attention of practitioners in the disciplines of accounting and management.

Accounting methods grounded in the early 1800s that are premised on only capital assets as the means to increase the productivity of labor no longer work. Our engines of innovation are not physical devices, but ideas, innovation and knowledge; in short, information. Capable executives recognize these trends, but have yet to change management practices to address them [31].

As managers and executives of firms we need not await wholesale modernization of accounting practices to begin to make a difference. The first step is to understand the role, use and importance of information to our organizations. Looking clearly at the seven information “laws” and what that means about tracking and monitoring is an immediate way to take this step. The second step is to understand and evaluate seriously the prospects for semantic approaches to make a difference today.

We have now sufficiently climbed the data federation pyramid [32] to where all of our information assets are digital; we have network protocols to link it; we have natural language and extraction techniques for making documents first-class citizens along side structured data; and we have logical data models and sound semantic technologies for tying it all together.

We need to reorganize our “factory” floors around these principles, just as prime movers and unit electric drives altered our factories of the past. We need to reorganize and re-think our work processes and what we measure and value to compete in the 21st century. It is time to treat information as seriously as it has become an integral part of our enterprises. Semantic technologies and approaches provide just the path to do so.

Writing and Sharing Data Can be Lightened Up

Ever since I first started to learn in earnest about ontology, something has been gnawing at me. The term seemed to be (shall I say?) an obtuse one whose obscurity was not the result of subtle precision or technicality, but rather one of fuzziness. As I introduced my Intrepid Guide to Ontology two years ago, I noted:

Since then, I have continued to find ontology one of the hardest concepts to communicate to clients and quite a muddled mess even as used by practitioners. I have come to the conclusion that this problem is not because I have failed to grasp some ephemeral nuance, but because the term as used in practice is indeed fuzzy and imprecise.

What Isn’t an Ontology?

Even two years ago, I noted more than 40 different types of information structure that have at one time or another been labelled as an example of an “ontology”:

Since then, I could add even more terms to this list.

Lack of precision as to what ontology means has meant that it has been sloppily defined. As I have harped upon many times regarding semantic Web terminology, this is a sad state of affairs for the semWeb endeavor that has meaning at the core of its purpose.

I’m pretty sure that the original intent in embracing the concept of ontology within the realm of knowledge representation was not to see this term so broadly misused or mis-applied. I suspect, as well, that if we could sharpen up our understanding and remove some of the fuzziness that we could improve communications with the lay public across many levels of the semWeb enterprise.

The Useful Distinction of the TBox and ABox

Recently, I have been looking to the semantic Web’s roots in description logics. One of my writings, Thinking ‘Inside the Box’ with Description Logics, looked at the conceptual distinctions between the so-called ‘TBox‘ and ‘ABox‘. That is, a knowledge base is a logical schema of roles and concepts and the relationships between them (the TBox), which is populated by the actual data (instances) asserting memberships and attributes (“facts”) (the ABox).

By analogy, in a conventional relational database system, the database or logical schema would correspond to the TBox; the actual data records or tables would correspond to the ABox. Often, the term ontology is used to cover both ABox and TBox statements (which, I argue, only makes the understanding of the ‘ontology’ concept more difficult).

My recent writing, Back to the Future with Description Logics, discussed at some length the advantages of keeping the TBox and ABox separate. This current article now expands on those thoughts, particularly with respect to the definition and understanding of ontology.

The starting point for this new mindset is to return to the ideas of data records or data tables v. the logical schema that is prevalent in relational databases.

So Many Structs, So Little Time

The last time I took a census, about a year ago, there were more than 100 converters of various record and data structure types to RDF [2]. These converters — also sometimes known as translators or ‘RDFizers’ — generally take some input data records with varying formats or serializations and convert them to a form of RDF serialization (such as RDF/XML or N3), often with some ontology matching or characterizations. That last census listed these converters:

This wealth of formats shows the robustness of the RDF data model to capture structure and data relationships from virtually any input form. This is what makes RDF so exciting as a canonical target for getting data to interoperate.

Let’s Make this Elementary, Dr. Watson

However — and this is crucial — standard users for decades have preferred simple, text-based and human readable formats for writing and transferring their structured data.

These various forms, sometimes well specified with APIs and sometimes almost ad hoc as in spreadsheet listings, are what we call ‘structs‘. Structs can all be displayed as text and have, at minimum, explicit or inferrable key-value pairs to convey data relationships and attributes, with data types and values often noted by various white space, delimiter or other text conventions.

There is no doubt that the vast majority of extant data is found in such formats, including the results of data or information extraction from unstructured text. Indeed, even HTML and many markup languages with their angle bracket-delimited fields fall into this category.

There have literally been hundreds of various formats proposed over decades for conveying lightweight data structures. Most have been proprietary or limited to specific domains or users. Some, such as fielded text, structured text, simple declarative language (SDL), or more recently YAML or its simpler cousin JSON, have become more widely adopted and supported by formal specifications, tools or APIs. JSON, especially, is a preferred form for Web 2.0 applications.

Some, like microformats or this example BibTeX record below (with some non-standard extensions), rely less on syntax conventions and may use reserved keywords (such as AUTHOR or TITLE as shown) to signal the key type for the key-value pair:

ID_LOCAL arXiv:0711.3808
AUTHOR <a href="#Schramm_O">Oded Schramm</a>
BIBTYPE ARTICLE
ID arXiv:0711.3808
JOURNAL Electron. Res. Announc. Math. Sci.
PAGES 17--23
SUBJECTS geom
TITLE Hyperfinite graph limits
URL http://www.aimsciences.org/journals/doIpChk.jsp?paperID=3117&mode=full
URL http://www.aimsciences.org/journals/displayPapers0.jsp?comments=&pubID=221&journID=14&pubString_num=Volume:
15, 2008 Journal Issue
VOLUME 15
YEAR 2008

Some of these simple formats have been more successful than others, though none have achieved market dominance. There also appear to be few universal principles that have emerged as to syntax or format. Nonetheless, any of these various struct forms are easy for casual readers to understand and easy for domain experts to write.

For modeling and interoperability purposes, many of these forms are patently inadequate. That is why many of these simpler forms might be called “naïve”: they achieve their immediate purpose of simple relationships and communication, but require understood or explicit context in order to be meaningfully (semantically) related to other forms or data.

Yet, if we have learned nothing else with the phenomenal success of the Web it is this: simplicity trumps elegance or expressivity.

RDF and the Skinny ABox

The RDF (Resource Description Framework) data model is expressed as simple subject-predicate-object “triple” statements. That sounds fancy, but just substitute verb for predicate and noun for subject and object. In other words: Dick sees Jane; or, the ball is round. It may sound like a kindergartner reader, but it is how data can be easily represented and built up into more complex structures and stories.

RDF triples can be applied equally to all structured, semi-structured and unstructured content. RDF is clearly a most capable data model that — through its ability to be extended with further concepts and relationships (vocabulary) — can create elegant and logical structures to represent comprehensive domains and knowledge bases. Finding such a model has been a quest in my professional life; I believe we finally have a winner to facilitate data interoperability using RDF.

But RDF has not achieved the market acceptance that its suitability as a data representation model might suggest. I think there are three reasons for this:

• First, RDF was first presented and “sold” as an XML serialization. This failing has been well understood for some time. This unfortunate early linkage of RDF caused confusion between data model and the XML syntax. The rather simple and incremental building blocks of triple RDF statements when presented in the nested XML syntax led to lengthy and hard-to-read specifications (for easier reading and use, see either the N3 or Turtle syntaxes)
• Second, triples by definition are 50% more complicated than a key-value pair. While the basic RDF statement might be simple like a Dick-and-Jane reader, as a data specification format it is still more complex than my personal attributes of sex:Male and hair:Red and born:California. Those three “facts” can not be said nearly so quickly in RDF. And, if we also adhere to linked data, each one of these items requires a URI unique identifier to boot! It is important not to ignore the desire for simple and human readable data-specification formats
• Third, as this entry began and as we will conclude, RDF and its fuzzy relationship to ontology has led to over-specification of what needs to be included in the data record. What could simply be a record specification of an object and its attributes presented as simple key-value pairs has become burdened with “ontology” and “conceptual” relationships.

Canonical forms embody all of the specification that the canon guiding them requires. What we may have failed to see in embracing RDF, however, is that getting useful data into the system need not carry all of this burden.

Lightening Up and Shifting Work to the TBox

Click for full-size

So, what does all of this have to do with my starting diatribe about the term ontology?

Whether a single database or the federation across all information known to human kind, we have data records (structs of instances) and a logical schema (ontology of concepts and relationships) by which we try to relate this information. This is a natural and meaningful split: structure and relationships v. the instances that populate that structure.

Stated this way, particularly for anyone with a relational database background, the split between schema and data is clear and obvious. Yet, the RDF, semantic Web and linked data communities have done an abysmal job of recognizing this fundamental separation of concerns.

We create “ontologies” that mix instances and schema. We insist on simple data record conversions that are burdened with relationship specifications as well. We tout a “linked data” infrastructure that is based solely on the same identity of instances without respect or attention to structure or conceptual relationships. We dismiss communities that work to express their data with useful local structures. We insist on standards and practices up and down the data staging and preparation chain that turns off the general market and makes us seem arrogant and dismissive. Frankly, in so many ways, we just don’t get it [3].

What has struck me personally over the past few months as these realizations have unfolded has been how much our own mindsets and language may be trapping us.

• Does existing structured data need to be expressed as RDF in order to be useful and integrated?
• Exposing linked and instance data is great, but to what end; what are the conceptual or structural schema?
• Why is our standards process so inward looking and parochial (often petty)? What purpose or who does this serve?

At least for this diatribe, my essential conclusion is that we need to shift the burden of the schema and conceptual relations and (yes) world views to the TBox. We need to skinny down the ABox and make it a warm and welcoming environment by which any structured data (including the most naïve) can join.

So, ultimately, the bottom line is this: the burden of the semantic Web rests on us, not the providers of structured data.

It is time to streamline the ABox to smooth data contributions, assume as publishers the responsibility for the TBox, and keep those concerns separate. As for instance-related stuff, I now intend to refer to them as structs governed by a controlled vocabulary (at most). I intend to reserve ontology as a means to describe a given world view, a TBox, the schema and its relations of the domain at hand. And, frankly, this definition of ontology brings it back in balance with its roots in ontos and the nature of the world.

It’s a good time to lighten up!

This Friday brown bag leftover was first placed into the AI3 refrigerator on January 22, 2009, and is one of the more popular historical posts of this blog.  This reprise is unchanged since its original posting, though we have continued to make progress on constructs such as irON to capture this idea. Microdata in HTML5 is also an important contribution, to which we will devote some attention in the near future.

There’s an Endless Variety of World Views, and Almost as Many Ways to Organize and Describe Them

Ontology is one of the more daunting terms for those exposed for the first time to the semantic Web. Not only is the word long and without many common antecedents, but it is also a term that has widely divergent use and understanding within the community. It can be argued that this not-so-little word is one of the barriers to mainstream understanding of the semantic Web.

The root of the term is the Greek ontos, or being or the nature of things. Literally — and in classical philosophy — ontology was used in relation to the study of the nature of being or the world, the nature of existence. Tom Gruber, among others, made the term popular in relation to computer science and artificial intelligence about 15 years ago when he defined ontology as a “formal specification of a conceptualization.”

While there have been attempts to strap on more or less formal understandings or machinery around ontology, it still has very much the sense of a world view, a means of viewing and organizing and conceptualizing and defining a domain of interest. As is made clear below, I personally prefer a loose and embracing understanding of the term (consistent with Deborah McGuinness’s 2003 paper, Ontologies Come of Age [1]).

There has been a resurgence of interest in ontologies of late. Two reasons have been the emergence of Web 2.0 and tagging and folksonomies, as well as the nascent emergence of the structured Web. In fact, on April 23-24 one of the noted communities of practice around ontologies, Ontolog, sponsored the Ontology Summit 2007 ,”Ontology, Taxonomy, Folksonomy: Understanding the Distinctions.”

These events have sparked my preparing this guide to ontologies. I have to admit this is a somewhat intrepid endeavor given the wealth of material and diversity of opinions.

This Friday brown bag leftover was first placed into the AI3 refrigerator more than three years ago on May 16, 2007. This reprise is unchanged since its original posting, though there is a more recent executive-level intro to ontologies on the OpenStructs‘ TechWiki.

Overview and Role of Ontologies

Of course, a fancy name is not sufficient alone to warrant an interest in ontologies. There are reasons why understanding, using and manipulating ontologies can bring practical benefit:

• Depending on their degree of formalism (an important dimension), ontologies help make explicit the scope, definition, and language and meaning (semantics) of a given domain or world view
• Ontologies may provide the power to generalize about their domains
• Ontologies, if hierarchically structured in part (and not all are), can provide the power of inheritance
• Ontologies provide guidance for how to correctly “place” information in relation to other information in that domain
• Ontologies may provide the basis to reason or infer over its domain (again as a function of its formalism)
• Ontologies can provide a more effective basis for information extraction or content clustering
• Ontologies, again depending on their formalism, may be a source of structure and controlled vocabularies helpful for disambiguating context; they can inform and provide structure to the “lexicons” in particular domains
• Ontologies can provide guiding structure for browsing or discovery within a domain, and
• Ontologies can help relate and “place” other ontologies or world views in relation to one another; in other words, ontologies can organize ontologies from the most specific to the most abstract.

Both structure and formalism are dimensions for classifying ontologies, which combined are often referred to as an ontology’s “expressiveness.” How one describes this structure and formality differs. One recent attempt is this figure from the Ontology Summit 2007‘s wrap-up communique:

Note the bridging role that an ontology plays between a domain and its content. (By its nature, every ontology attempts to “define” and bound a domain.) Also note that the Summit’s 50 or so participants were focused on the trade-off between semantics v. pragmatic considerations. This was a result of the ongoing attempts within the community to understand, embrace and (possibly) legitimize “less formal” Web 2.0 efforts such as tagging and the folksonomies that can result from them.

There is an M.C. Escher-like recursion of the lizard eating its tail when one observes ontologists creating an ontology to describe the ontological domain. The above diagram, which itself would be different with a slight change in Summit participation or editorship, is, of course, but one representative view of the world. Indeed, a tremendous variety of scientific and research disciplines concern themselves with classifying and organizing the “nature of things.” Those disciplines go by such names as logicians, taxonomists, philosophers, information architects, computer scientists, librarians, operations researchers, systematicists, statisticians, historians, and so forth. (In short, given our ontos, every area of human endeavor has the urge to classify, to organize.) In each of these areas not only do their domains differ, but so do the adopted structures and classification schemes often used.

There are at least 40 terms or concepts across these various disciplines, most related to Web and general knowledge content, that have organizational or classificatory aspects that — loosely defined — could be called an “ontology” framework or approach:

Actual domains or subject coverage are then mostly orthogonal to these approaches.

Loosely defined, the number of possible ontologies is therefore close to infinite: domain X perspective X schema. (Just kidding — sort of! In fact, UMBC’s Swoogle ontology search service claims 10,000 ontologies presently on the Web; the actual data from August 2006 ranges from about 16,000 to 92,000 ontologies, depending on how “formal” the definition. These counts are also limited to OWL-based ontologies.)

Many have misunderstood the semantic Web because of this diversity and the slipperiness of the concept of an ontology. This misunderstanding becomes flat wrong when people claim the semantic Web implies one single grand ontology or organizational schema, One Ring to Rule Them All. Human and domain diversities makes this viewpoint patently false.

Diversity, ‘Naturalness’ and Change

The choice of an ontological approach to organize Web and structured content can be contentious. Publishers and authors perhaps have too many choices: from straight Atom or RSS feeds and feeds with tags to informal folksonomies and then Outline Processor Markup Language or microformats. From there, the formalism increases further to include the standard RDF ontologies such as SIOC (Semantically-Interlinked Online Communities), SKOS (Simple Knowledge Organizing System), DOAP (Description of a Project), and FOAF (Friend of a Friend) and the still greater formalism of OWL’s various dialects.

Arguing which of these is the theoretical best method is doomed to failure, except possibly in a bounded enterprise environment. We live in the real world, where multiple options will always have their advocates and their applications. All of us should welcome whatever structure we can add to our information base, no matter where it comes from or how it’s done. The sooner we can embrace content in any of these formats and convert it to a canonical form, we can then move on to needed developments in semantic mediation, the threshold condition for the semantic Web.

So, diversity is inevitable and should be accepted. But that observation need not also embrace chaos.

In my early training in biological systematics, Ernst Haeckel’s recapitulation theory that “ontogeny recapitulates phylogeny” (note the same ontos root, the difference from ontology being growth v. study) was losing favor fast. The theory was that the development of an organism through its embryological phases mirrors its evolutionary history. Today, modern biologists recognize numerous connections between ontogeny and phylogeny, explain them using evolutionary theory, or view them as supporting evidence for that theory.

Yet, like the construction of phylogenetic trees, systematicists strive for their classifications of the relatedness of organisms to be “natural”, to reflect the true nature of the relationship. Thus, over time, that understanding of a “natural” system has progressed from appearance → embryology → embryology + detailed morphology → species and interbreeding → DNA. While details continue to be worked out, the degree of genetic relatedness is now widely accepted by biologists as a “natural” basis for organizing the Tree of Life.

It is not unrealistic to also seek “naturalness” in the organization of other knowledge domains, to seek “naturalness” in the organization of their underlying ontologies. Like natural systems in biology, this naturalness should emerge from the shared understandings and perceptions of the domain’s participants. While subject matter expertise and general and domain knowledge are essential to this development, they are not the only factors. As tagging systems on the Web are showing, common usage and broad acceptance by the community at hand is important as well.

While it may appear that a domain such as the biological relatedness of organisms is more empirical than the concepts and ambiguous words in most domains of human endeavor, these attempts at naturalness are still not foolish. The phylogeny example shows that understanding changes over time as knowledge is gained. We now accept DNA over the recapitulation theory.

As the formal SKOS organizational schema for knowledge systems recognizes (see below), the ideas of narrower and broader concepts can be readily embraced, as well as concepts of relatedness and aliases (synonyms). These simple constructs, I would argue, plus the application of knowledge being gained in related domains, will enable tomorrow’s understandings to be more “natural” than today’s, no matter the particular domain at hand.

So, in seeking a “naturalness” within our organizational schema, we can also see that change is a constant. We also see that the tools and ideas underlying the seemingly abstract cause of merging and relating existing ontologies to one another will further a greater “naturalness” within our organizations of the world.

A Spectrum of Formalisms

According to the Summit, expressiveness is the extent and ease by which an ontology can describe domain semantics. Structure they define as the degree of organization or hierarchical extent of the ontology. They further define granularity as the level of detail in the ontology. And, as the diagram above alludes, they define other dimensions of use, logical basis, purpose and so forth of an ontology.

The over fifty respondents from 42 communities submitted some 70 different ontologies under about 40 terms to a survey that was used by the Summit to construct their diagram. These submissions included:

I think the simplest spectrum for such distinctions is the formalism of the ontology and its approach (and language or syntax, not further discussed here). More formal ontologies have greater expressiveness and structure and inferential power, less formal ones the opposite. Constructing more formal ontologies is more demanding, and takes more effort and rigor, resulting in an approach that is more powerful but also more rigid and less flexible. Like anything else, there are always trade-offs.

Based on work by Leo Obrst of Mitre as interpreted by Dan McCreary, we can view this as a trade-off as one of semantic clarity v. the time and money required to construct the formalism [12, 13]:

[Click on image for full-size pop-up]

Note this diagram reflects the more conventional, practitioner’s view of the “formal” ontology, which does not include taxonomies or controlled vocabularies (for example) in the definition. This represents the more “closely defined” end of the ontology (semantic) spectrum.

However, since we are speaking here of ontologies and the structured Web or the semantic Web, I believe we need to embrace a concept of ontology aligned to actual practice. Not all content providers can or want to employ ontology engineers to enable formal inferencing of their content. Yet, on the other hand, their content in its various forms does have some meaningful structure, some organization. The trick is to extract this structure for more meaningful use such as data exchange or data merging.

Ontology Approaches on the Web

Under such “loosely defined” bases we can thus see a spectrum of ontology approaches on the Web, proceeding from less structure and formalism to more so:

As a rule of thumb, items that are less “formal” can be converted to a more formal expression, but the most formal forms can generally not be expressed in less formal forms.

As latter sections elaborate, I see RDF as the universal data model for representing this structure into a common, canonical format, with RDF Schema (specifically SKOS, but also supplemented by FOAF, DOAP and SIOC) as the organizing ontology knowledge representation language (KRL).

This is not to say that the various dialects of OWL should be neglected. In bounded environments, they can provide superior reasoning power and are warranted if they can be sufficiently mandated or enforced. But the RDF and RDF-S systems represent the most tractable “meeting place” or “middle ground,” IMHO.

Still-Another “Level” of Ontologies

As if the formalism dimension were not complicated enough, there is also the practice within the ontology community to characterize ontologies by “levels”, specifically upper, middle and lower levels. For example, chances are that you have heard particularly of “upper-level” ontologies.

The following figure helps illustrate this “level” dimension. This diagram is also from Leo Obrst of Mitre [12], and was also used in another 2006 talk by Jack Park and Patrick Durusau (discussed further below for other reasons):

Examples of upper-level ontologies include the Suggested Upper Merged Ontology (SUMO), the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE), PROTON, Cyc and BFO (Basic Formal Ontology). Most of the content in their upper-levels is akin to broad, abstract relations or concepts (similar to the primary classes, for example, in a Roget’s Thesaurus — that is, real ontos stuff) than to “generic common knowledge.” Most all of them have both a hierarchical and networked structure, though their actual subject structure relating to concrete things is generally pretty weak [2].

The above diagram conveys a sense of how multiple ontologies can relate to one another both in terms of narrower and broader topic matter and at the same “levels” of generalization. Such “meta-structure” (if you will) can provide a reference structure for relating multiple ontologies to one another.

It resides exactly in such bindings or relationships that we can foresee the promise of querying and relating multiple endpoints on the Web with accurate semantics in order to connect dots and combine knowledge bases. Thus, the understanding of the relationships and mappings amongst ontologies becomes a critical infrastructural component of the semantic Web.

The SUMO Example

We can better understand these mapping and inter-relationship concepts by using a concrete example with a formal ontology. We’ll choose to use the Suggested Upper Merged Ontology simply because it is one of the best known. We could have also selected another upper-level system such as PROTON [3] or Cyc [4] or one of the many with narrower concept or subject coverage.

SUMO is one of the formal ontologies that has been mapped to the WordNet lexicon, which adds to its semantic richness. SUMO is written in the SUO-KIF language. SUMO is free and owned by the IEEE. The ontologies that extend SUMO are available under GNU General Public License.

The abstract, conceptual organization of SUMO is shown by this diagram, which also points to its related MILO (MId-Level Ontology), which is being developed as a bridge between the abstract content of the SUMO and the richer detail of various domain ontologies:

At this level, the structure is quite abstract. But one can easily browse the SUMO structure. A nifty tool to do so is the KSMSA (Knowledge Support for Modeling and Simulation) ontology browser. Using a hierarchical tree representation, you can navigate through SUMO, MILO, WordNet, and (with the locally installed version) Wikipedia.

The figure below shows the upper-level entity concept on the left; the right-hand panel shows a drill-down into the example atom entity:

[Click on image for full-size pop-up]

These views may be a bit misleading because the actual underlying structure, while it has hierarchical aspects as shown here, really is in the form of a directed acyclic graph (showing other relatedness options, not just hierarchical ones). So, alternate visualizations include traditional network graphs.

The other thing to note is that the “things” covered in the ontology, the entities, are also fairly abstract. That is because the intention of a standard “upper-level” ontology is to cover all relevant knowledge aspects of each entity’s domain. This approach results in a subject and topic coverage that feels less “concrete” than the coverage in, say, an encyclopedia, directory or card catalog.

Ontology Binding and Integration Mechanisms

According to Park and Durusau, upper ontologies are diverse, middle ontologies are even more diverse, and lower ontologies are more diverse still. A key observation is that ontological diversity is a given and increases as we approach real user interaction levels. Moreover, because of the “loose” nature of ontologies on the Web (now and into the future), diversity of approach is a further key factor.

Recall the initial discussion on the role and objectives of ontologies. About half of those roles involve effectively accessing or querying more than one ontology. The objective of “upper-level” ontologies, many with their own binding layers, is also expressly geared to ontology integration or federation. So, what are the possible mechanisms for such binding or integration?

A fundamental distinction within mechanisms to combine ontologies is whether it is a unified or centralized approach (often imposed or required by some party) or whether it is a schema mapping or binding approach. We can term this distinction centralized v. federated.

Centralized Approaches

Centralized approaches can take a number of forms. At the most extreme, adherence to a centralized approach can be contractual. At the other end are reference models or standards. For example, illustrative reference models include:

• the Data Reference Model (DRM), one of the five reference models of the Federal Enterprise Architecture (FEA)
• UDEF (Unified Data Element Framework), an approach toward a unified description framework, or
• the eXtended MetaData Registry (XMDR) project.

Though I have argued that One Ring to Rule them All is not appropriate to the general Web, there may be cases within certain enterprises or where through funding clout (such as government contracts), some form of centralized approach could be imposed [5]. And, frankly, even where compliance can not be assured, there are advantages in economy, efficiency and interoperability to attempt central ontologies. Certain industries — notably pharmaceuticals and petrochemicals — and certain disciplines — such as some areas of biology among others — have through trade associations or community consensus done admirable jobs in adopting centralized approaches.

Federated Approaches

However, combining ontologies in the context of the broader Internet is more likely through federated approaches. (Though federated approaches can also be improved when there are consensual standards within specific communities.) The key aspect of a federated approach is to acknowledge that multiple schema need to be brought together, and that each contributing data set and its schema will not be altered directly and will likely remain in place.

Thus, the key distinctions within this category are the mechanisms by which those linkages may take place An important goal in any federated approach is to achieve interoperability at the data or instance level without unacceptable loss of information or corruption of the semantics. Numerous specific approaches are possible, but three example areas in RDF-topic map interoperability, the use of “subject maps”, and binding layers can illustrate some of the issues at hand.

In 2006 the W3C set up a working group to look at the issue of RDF and topic maps interoperability. Topic maps have been embraced by the library and information architecture community for some time, and have standards that have been adopted under ISO. Somewhat later but also in parallel was the development of the RDF standard by W3C. The interesting thing was that the conceptual underpinnings and objectives between these two efforts were quite similar. Also, because of the substantive thrust of topic maps and the substantive needs of its community, quite a few topic maps had been developed and implemented.

One of the first efforts of the W3C work group was to evaluate and compare five or six extant proposals for how to relate RDF and topic maps [6]. That report is very interesting reading for any one desirous of learning more about specific issues in combining ontologies and their interoperability. The result of that evaluation then led to some guidelines for best practices in how to complete this mapping [7]. Evaluations such as these provide confidence that interoperability can be achieved between relatively formal schema definitions without unacceptable loss in meaning.

A different, “looser” approach, but one which also grew out of the topic map community, is the idea of “subject maps.” This effort, backed by Park and Durusau noted above, but also with the support of other topic map experts such as Steve Newcomb and Robert Barta via their proposed Topic Maps Reference Model (ISO 13250-5), seems to be one of the best attempts I’ve seen that both respects the reality of the actual Web while proposing a workable, effective scheme for federation.

The basic idea of a subject map is built around a set of subject “proxies.” A subject proxy is a computer representation of a subject that can be implemented as an object, must have an identity, and must be addressable (this point provides the URI connector to RDF). Each contributing schema thus defines its own subjects, with the mappings becoming meta-objects. These, in turn, would benefit from having some accepted subject reference schema (not specifically addressed by the proponents) to reduce the breadth of the ultimate mapped proxy “space.”

I don’t have the expertise to judge further the specifics, but I find the presentation and papers by Park and Durusau, Avoiding Hobson’s Choice In Choosing An Ontology and Towards Subject-centric Merging of Ontologies to be worthwhile reading in any case. I highly recommend these papers for further background and clarity.

As the third example, “binding layers” are a comparatively newer concept. Leading upper-level ontologies such as SUMO or PROTON propose their own binding protocols to their “lower” domains, but that approach takes place within the construct of the parent upper ontology and language. Such designs are not yet generalized solutions. By far the most promising generalized binding solution is the SKOS (Simple Knowledge Organization System). Because of its importance, the next section is devoted to it.

Finally, with respect to federated approaches, there are quite a few software tools that have been developed to aid or promote some of these specific methods. For, example, about twenty of the software applications in my Sweet Tools listing of 500+ semantic Web and -related tools could be interpreted as aiding ontology mapping or creation. You may want to check out some of these specific tools depending on your preferred approach [8].

The Role of SKOS – the Simple Knowledge Organization System

SKOS, or the Simple Knowledge Organization System, is a formal language and schema designed to represent such structured information domains as thesauri, classification schemes, taxonomies, subject-heading systems, controlled vocabularies, or others; in short, most all of the “loosely defined” ontology approaches discussed herein. It is a W3C initiative more fully defined in its SKOS Core Guide [9].

SKOS is built upon the RDF data model of the subject-predicate-object “triple.” The subjects and objects are akin to nouns, the predicate a verb, in a simple Dick-sees-Jane sentence. Subjects and predicates by convention are related to a URI that provides the definitive reference to the item. Objects may be either a URI resource or a literal (in which case it might be some indexed text, an actual image, number to be used in a calculation, etc.).

Being an RDF Schema simply means that SKOS adds some language and defined relationships to this RDF baseline. This is a bit of recursive understanding, since RDFS is itself defined in RDF by virtue of adding some controlled vocabulary and relations. The power, though, is that these schema additions are also easily expressed and referenced.

This RDFS combination can thus be shown as a standard RDF triple graph, but with the addition of the extended vocabulary and relations:

The power of the approach arises from the ability of the triple to express virtually any concept, further extended via the RDFS language defined for SKOS. SKOS includes concepts such as “broader” and “narrower”, which enable hierarchical relations to be modeled, as well as “related” and “member” to support networks and arrays, respectively [9].

We can visualize this transforming power by looking at how an “ontology” in a totally foreign scheme can be related to the canonical SKOS scheme. In the figure below the left-hand portion shows the native hierarchical taxonomy structure of the UK Archival Thesaurus (UKAT), next as converted to SKOS on the right (with the overlap of categories shown in dark purple). Note the hierarchical relationships visualize better via a taxonomy, but that the RDF graph model used by SKOS allows a richer set of additional relationships including related and alternative names:

[Click on image for full-size pop-up]

SKOS also has a rich set of annotation and labeling properties to enhance human readability of schema developed in it [9]. There is also a useful draft schema that the W3C’s SWEO (Semantic Web Education and Outreach) group is developing to organize semantic Web-related information [10].

Combined, these constructs provide powerful mechanisms for giving contributory ontologies a common conceptualization. When added to other sibling RDF schema such as FOAF or SIOC or DOAP, still additional concepts can be collated.

Conclusions

While not addressed directly in this piece, it is obviously of first importance to have content with structure before the questions of connecting that information can even arise. Then, that structure must also be available in a form suitable for merging or connection.

At that point, the subjects of this posting come into play.

We see that the daily Web has a diversity of schema or ontologies “loosely defined” for representing the structure of the content. These representations can be transferred to more complex schema, but not in the opposite direction. Moreover, the semantic basis for how to make these mappings also needs some common referents.

RDF provides the canonical data model for the data transfers and representations. RDFS, especially in the form of SKOS, appears to form one basis for the syntax and language for these transformations. And SKOS, with other schema, also appears to offer much of the appropriate “middle ground” for data relationships mapping.

However, lacking in this story is a referential structure for subject relationships [11]. (Also lacking are the ultimately critical domain specifics required for actual implementation.)

Abstract concepts of interest to philosophers and deep thinkers have been given much attention. Sadly, to date, concrete subject structures in which tangible things and tangible actions can be shared, is still very, very weak. We are stubbing our toes on the rocks while we gaze at the heavens.

Yet, despite this, simple and powerful infrastructures are well in-hand to address all foreseeable syntactic and semantic issues. There appear to be no substantive limits to needed next steps.

Lastly, many valuable resources for further reading and learning may be found within the Ontolog Community, W3C, TagCommons and Topics Maps groups. Enjoy! And be wary of ontology no longer.