Five Unique Advantages for the Enterprise

There have been some notable attempts of late to make elevator pitches [1] for semantic technologies, as well as Lee Feigenbaum’s recent series on Are We Asking the Wrong Question? about semantic technologies [2]. Some have attempted to downplay semantic Web connotations entirely and to replace the pitch with Linked Data (capitalized). These are part of a history of various ways to try to make a business case around semantic approaches [3].

What all of these attempts have in common is a view — an angst, if you will — that somehow semantic approaches have not fulfilled their promise. Marketing has failed semantic approaches. Killer apps have not appeared. The public has not embraced the semantic Web consonant with its destiny. Academics and researchers can not make the semantic argument like entrepreneurs can.

Such hand wringing, I believe, is misplaced on two grounds. First, if one looks to end user apps that solely distinguish themselves by the sizzle they offer, semantic technologies are clearly not essential. There are very effective mash-up and data-intensive sites such as many of the investment sites (Fidelity, TDAmeritrade, Morningstar, among many), real estate sites (Trulia, Zillow, among many), community data sites (American FactFinder, CensusScope, City-Data.com, among many), shopping sites (Amazon, Kayak, among many), data visualization sites (Tableau, Factual, among many), etc. , etc., that work well, are intuitive and integrate much disparate information. For the most part, these sites rely on conventional relational database backends and have little semantic grounding. Effective data-intensive sites do not require semantics per se [4].

Second, despite common perceptions, semantics are in fact becoming pervasive components of many common and conventional Web sites. We see natural language processing (NLP) and extraction technologies becoming common for most search services. Google and Bing sprinkle semantic results and characterizations across their standard search results. Recommendation engines and targeted ad technologies now routinely use semantic approaches. Ontologies are creeping into the commercial spaces once occupied by taxonomies and controlled vocabularies. Semantics-based suggestion systems are now the common technology used. A surprising number of smartphone apps have semantics at their core.

So, I agree with Lee Feigenbaum that we are asking the wrong question. But I would also add that we are not even looking in the right places when we try to understand the role and place of semantic technologies.

The unwise attempt to supplant the idea of semantic technologies with linked data is only furthering this confusion. Linked data is merely a means for publishing and exposing structured data. While linked data can lead to easier automatic consumption of data, it is not necessary to effective semantic approaches and is actually a burden on data publishers [5]. While that burden may be willingly taken by publishers because of its consumption advantages, linked data is by no means an essential precursor to semantic approaches. None of the unique advantages for semantic technologies noted below rely on or need to be preceded by linked data. In semantic speak, linked data is not the same as semantic technologies.

The essential thing to know about semantic technologies is that they are a conceptual and logical foundation to how information is modeled and interrelated. In these senses, semantic technologies are infrastructural and groundings, not applications per se. There is a mindset and worldview associated with the use of semantic technologies that is far more essential to understand than linked data techniques and is certainly more fundamental than elevator pitches or “killer apps.”

Five Unique Advantages

Thus, the argument for semantic technologies needs to be grounded in their foundations. It is within the five unique advantages of semantic technologies described below that the benefits to enterprises ultimately reside.

#1: Modern, Back-end Data Federation

The RDF data model — and its ability to represent the simplest of data up through complicated domain schema and vocabularies via the OWL ontology language — means that any existing schema or structure can be represented. Because of this expressiveness and flexibility, any extant data source or schema can be represented via RDF and its extensions. This breadth means that a common representation for any existing schema may be expressed. That expressiveness, in turn, means that any and all data representations can be described in a canonical way.

A shared, canonical representation of all existing schema and data types means that all of that information can now be federated and interrelated. The canonical means of federating information via the RDF data model is the foundational benefit of semantic technologies. Further, the practice of giving URIs as unique identifiers to all of the constituent items in this approach makes it perfectly suitable to today’s reality of distributed data accessible via the Web [6].

#2: Universal Solvent for Structure

I have stated many times that I have not met a form of structured data I did not like [7]. Any extant data structure or format can be represented as RDF. RDF can readily express information contained within structured (conventional databases), semi-structured (Web page or XML data streams), or unstructured (documents and images) information sources. Indeed, the use of ontologies and entity instance records in RDF is a powerful basis for driving the extraction systems now common for tagging unstructured sources.

(One of the disservices perpetuated by an insistence on linked data is to undercut this representational flexibility of RDF. Since most linked data is merely communicating value-attribute pairs for instance data, virtually any common data format can be used as the transmittal form.)

The ease of representing any existing data format or structure and the ability to extract meaningful structure from unstructured sources makes RDF a “universal solvent” for any and all information. Thus, with only minor conversion or extraction penalties, all information in its extant form can be staged and related together via RDF.

#3: Adaptive, Resilient Schema

A singular difference between semantic technologies (as we practice them) and conventional relational data systems is the use of an open world approach [8]. The relational model is a paradigm where the information must be complete and it must be described by a schema defined in advance. The relational model assumes that the only objects and relationships that exist in the domain are those that are explicitly represented in the database. This makes the closed world of relational systems a very poor choice when attempting to combine information from multiple sources, to deal with uncertainty or incompleteness in the world, or to try to integrate internal, proprietary information with external data.

Semantic technologies, on the other hand, allow domains to be captured and modeled in an incremental manner. As new knowledge is gained or new integrations occur, the underlying schema can be added to and modified without affecting the information that already exists in the system. This adaptability is generally the biggest source of economic benefits to the enterprise from semantic technologies. It is also a benefit that enables experimentation and lowers risk.

#4: Unmatched Productivity

Having all information in a canonical form means that generic tools and applications can be designed to work against that form. That, in turn, leads to user productivity and developer productivity. New datasets, structure and relationships can be added at any time to the system, but how the tools that manipulate that information behave remains unchanged.

User productivity arises from only needing to learn and master a limited number of toolsets. The relationships in the constituent datasets are modeled at the schema (that is, ontology) level. Since manipulation of the information at the user interface level consists of generic paradigms regarding the selection, view or modification of the simple constructs of datasets, types and instances, adding or changing out new data does not change the interface behavior whatsoever. The same bases for manipulating information can be applied no matter the datasets, the types of things within them, or the relationships between things. The behavior of semantic technology applications is very much akin to having generic mashups.

Developer productivity results from leveraging generic interfaces and APIs and not bespoke ones that change every time a new dataset is added to the system. In this regard, ontology-driven applications [9] arising from a properly designed semantic technology framework also work on the simple constructs of datasets, types and instances. The resulting generalization enables the developer to focus on creating logical “packages” of functionality (mapping, viewing, editing, filtering, etc.) designed to operate at the construct level, and not the level of the atomic data.

#5: Natural, Connected Knowledge Systems

All of these factors combine to enable more and disparate information to be assembled and related to one another. That, in turn, supports the idea of capturing entire knowledge domains, which can then be expanded and shifted in direction and emphasis at will. These combinations begin to finally achieve knowledge capture and representation in its desired form.

Any kind of information, any relationship between information, and any perspective on that information can be captured and modeled. When done, the information remains amenable to inspection and manipulation through a set of generic tools. Rather simple and direct converters can move that canonical information to other external forms for use by existing external tools. Similarly, external information in its various forms can be readily converted to the internal canonical form.

These capabilities are the direct opposite to today’s information silos. From its very foundations, semantic technologies are perfectly suited to capture the natural connections and nature of relevant knowledge systems.

A Summary of Advantages Greater than the Parts

There are no other IT approaches available to the enterprise that can come close to matching these unique advantages. The ideal of total information integration, both public and private, with the potential for incremental changes to how that information is captured, manipulated and combined, is exciting. And, it is achievable today.

With semantic technologies, more can be done with less and done faster. It can be done with less risk. And, it can be implemented on a pay-as-you-benefit basis [10] responsive to the current economic climate.

But awareness of this reality is not yet widespread. This lack of awareness is the result of a couple of factors. One factor is that semantic technologies are relatively new and embody a different mindset. Enterprises are only beginning to get acquainted with these potentials. Semantic technologies require both new concepts to be learned, and old prejudices and practices to be questioned.

A second factor is the semantic community itself. The early idea of autonomic agents and the heavy AI emphasis of the initial semantic Web advocacy now feels dated and premature at best. Then, the community hardly improved matters with its shift in emphasis to linked data, which is merely a technique and which completely overlooks the advantages noted above.

However, none of this likely matters. The five unique advantages for enterprises from semantic technologies are real and demonstrable today. While my crystal ball is cloudy as to how fast these realities will become understood and widely embraced, I have no question they will be. The foundational benefits of semantic technologies are compelling.

I think I’ll take this to the bank while others ride the elevator.

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.

Advances in How to Transfer Semantic Technologies to Enterprise Users

For some time, our mantra at Structured Dynamics has been, “We’re successful when we are not needed. [1]“

In support of this vision, we have been key developers of an entire stack of semantic technologies useful to the enterprise, the open semantic framework (OSF); we have formulated and contributed significant open source deployment guidance to the MIKE2.0 methodology for semantic technologies in the enterprise called Open SEAS; we have developed useful structured data standards and ontologies; and we have made massive numbers of free how-to documents and images available for download on our TechWiki. Today, we add further to these contributions with our workflows guidance. All of these pieces contribute to what we call the total open solution.

Prior documentation has described the overall architecture or layered approach of the open semantic framework (OSF). Those documents are useful, but lack a practical understanding of how the pieces fit together or how an OSF instance is developed and maintained.

This new summary overviews a series of seven different workflows for various aspects of developing and maintaining an OSF (based on Drupal) [2]. In addition, each workflow section also cross-references other key documentation on the TechWiki, as well as points to possible tools that might be used for conducting each specific workflow.

Overview

Seven different workflows are described, as shown in the diagram below. Each of the workflows is color-coded and related to the other workflows. The basic interaction with an OSF instance tends to occur from left-to-right in the diagram, though the individual parts are not absolutely sequential. As each of the seven specific workflows is described below, it is keyed by the same color-coded portion of the overall workflow.

Each of the component workflows is itself described as a series of inter-relating activities or tasks.

Installation Workflow

Installation is mostly a one-time effort and proceeds in a more-or-less sequential basis. As various components of the stack are installed, they are then configured and tested for proper installation.

The installation guide is the governing document for this process, with quite detailed scripts and configuration tests to follow. The blue bubbles in the diagram represent the major open source software components of Virtuoso (RDF triple store), Solr (full-text search) and Drupal (content management system).

Another portion of this workflow is to set up the tools for the backoffice access and management, such as PuTTY and WinSCP (among others).

Configure & Presentation Workflow

One of the most significant efforts in the overall OSF process is the configuration and theming of the host portal, generally based on Drupal.

The three major clusters of effort in this workflow are the design of the portal, including a determination of its intended functionality; the setting of the content structure (stubbing of the site map) for the portal; and determining user groups and access rights. Each of these, in turn, is dependent on one or more plug-in modules to the Drupal system.

Some of these modules are part of the conStruct series of OSF modules, and others are evaluated and drawn from the more than 8000 third-party plug-in modules to Drupal.

The Design aspect involves picking and then modifying a theme for the portal. These may start as one of the open source existing Drupal themes, as well as those more specifically recommended for OSF. If so, it will likely be necessary to do some minor layout modifications on the PHP code and some CSS (styling) changes. Theming (skinning) of the various semantic component widgets (see below) also occurs as part of this workflow.

The Content Structure aspect involves defining and then stubbing out placeholders for eventual content. Think of this step as creating a site map structure for the OSF site, including major Drupal definitions for blocks, Views and menus. Some of the entity types are derived from the named entity dictionaries used by a given project.

More complicated User assignments and groups are best handled through a module such as Drupal’s Organic Groups. In any event, determination of user groups (such as anonymous, admins, curators, editors, etc.) is a necessary early determination, though these may be changed or modified over time.

For site functionality, Modules must be evaluated and chosen to add to the core system. Some of these steps and their configuration settings are provided in the guidelines for setting up Drupal document.

None of the initial decisions “lock in” eventual design and functionality. These may be modified at any time moving forward.

Structured Data Workflow

Of course, a key aspect of any OSF instance is the access and management of structured data.

There are basically two paths for getting structured data into the system. The first, involving (generally) smaller datasets is the manual conversion of the source data to one of the pre-configured OSF import formats of RDF, JSON, XML or CSV. These are based on the irON notation; a good case study for using spreadsheets is also available.

The second path (bottom branch) is the conversion of internal structured data, often from a relational data store. Various converters and templates are available for these transformations. One excellent tool is FME from Safe Software (representing the example shown utilizing a spatial data infrastructure (SDI) data store), though a very large number of options exist for extract, transform and load.

In the latter case, procedures for polling for updates, triggering notice of updates, and only extracting the deltas for the specific information changed can help reduce network traffic and upload/conversion/indexing times.

Content Workflow

The structured data from the prior workflow process is then matched with the remaining necessary content for the site. This content may be of any form and media (since all are supported by various Drupal modules), but, in general, the major emphasis is on text content.

Existing text content may be imported to the portal or new content can be added via various WYSIWYG graphical editors for use within Drupal. (The excellent WYWIWYG Drupal module provides an access point to a variety of off-the-shelf, free WYSIWYG editors; we generally use TinyMCE but multiples can also be installed simultaneously).

The intent of this workflow component is to complete content entry for the stubs earlier created during the configuration phase. It is also the component used for ongoing content additions to the site.

Content that is tagged by the scones tagger is done so based on the concepts in the domain ontology (see below) and the named entities (as contained in “dictionaries”) used by a given project. Once tagged, this information can also now be related to the other structured data in the system.

Once all of this various content is entered into the system, it is then available for access and manipulation by the various conStruct modules (see figure above) and semantic component widgets (see below).

Ontologies Workflow

Though the next flowchart below appears rather complicated, there are really only three tasks that most OSF administrators need worry about with respect to ontologies:

1. Adding a concept to the domain ontology (a class) and setting its relationships to other concepts
2. Adding a dataset attribute (data characteristic) for various dataset records, or
3. Adding or changing an annotation for either of these things, such as the labels or descriptions of the thing.

In actuality, of course, editing, modifying or deleting existing information is also important, but they are easier subsets of activities and user interfaces to the basic add (“create”) functions.

The OSF interface provides three clean user interfaces to these three basic activities [3].

These basic activities may be applied to the three major governing ontologies in any OSF installation:

• The domain ontology, which captures the conceptual description of the instance’s domain space
• The semantic components ontology (SCO), which sets what widgets may display what kinds of data, and
• irON for the instance record attributes and metadata (annotations).

All of the OSF ontology tools work off of the OWLAPI as the intermediary access point. The ontologies themselves are indexed as structured data (RDF with Virtuoso) or full text (Solr) for various search, retrieval and reasoning activities.

Because of the central use of the OWLAPI, it is also possible to use the Protégé editor/IDE environment against the ontologies, which also provides reasoners and consistency checking.

Filter & Select Workflow

The filter and select activities are driven by user interaction, with no additional admin tools required. This workflow is actually the culmination of all of the previous sequences in that it exposes the structured data to users, enables them to slice-and-dice it, and then to view it with a choice of relevant widgets (semantic components).

For example, see this animation:

Considerable more detail and explanation is available for these semantic components.

Maintenance Workflow

The ongoing maintenance of an OSF instance is mostly a standard Drupal activity. Major activities that may occur include moderating comments; rotating or adding new content; managing users; and continued documentation of the site for internal tech transfer and training. If the portal embraces other aspects of community engagement (social media), these need to be handled as part of this workflow as well.

All aspects of the site and its constituent data may be changed, or added to at any time.

Moving from Here

When first introduced in our three-part series, we noted the interlocking pieces that constituted the total open solution of the open semantic framework (OSF) (see right). We also made the point — unfortunately still true today — that the relative maturity and completeness of all of these components still does not allow us to achieve fully, “We’re successful when we are not needed.”

As a small firm that is committed to self-funding via revenues, Structured Dynamics is only able to add to its stable of open source software and to develop methodologies and provide documentation based on our client support. Yet, despite our smallness, our superb client support has enabled us to aggressively and rapidly add to all four components of this total open solution. This newest series of ongoing workflow documents (plus some very significant expansions and refinements of the OSF code base) is merely the latest example of this dynamic.

Through judicious picking of clients (and vice versa), and our insistence that new work and documentation be open sourced because it itself has benefitted from prior open source, we and our client partners have been making steady progress to this vision of enterprises being able to adopt and install semantic solutions on their own. Inch-by-inch we are getting there.

The status of our vision today is that we are still needed in most cases to help formulate the implementation plan and then guide the initial set-up and configuration of the OSF. This support typically includes ontology development, data conversion and overall component integration. While it is true that some parties have embraced the OSF code and documentation and are implementing solutions on their own, this still requires considerable commitment and knowledge and skills in semantic technologies.

The great news about today’s status is that — after initial set-up and configuration — we are now able to transfer the technology to the client and walk away. Tools, documentation, procedures and workflows are now adequate for the client to extend and maintain their OSF instance on their own. This great news includes a certification process and program for transferring the technology to client staff and assessing their proficiency in using it.

We have been completely open about our plans and our status. In our commitment to our vision of success, much work is still needed on the initial install and configure steps and on the entire area of ontology creation, extension and mapping [4]. We are working hard to bridge these gaps. We welcome additional partners that share with us the vision of complete, turnkey frameworks — including all aspects of total open solutions. Inch-by-inch we are approaching the realization of a vision that will fundamentally change how every enterprise can leverage its existing information assets to deliver competitive advantage and greater value for all stakeholders. You are welcome aboard!

Self-service Information Management for Knowledge Workers

Though I have alluded to it numerous times in my past writings [1], I think one of the most pervasive and important benefits from semantic technologies in the enterprise will come from the democratization of information. These benefits will arise mostly from a fundamental change in how we manage and consume information. A new “system” of semantic technologies is now largely available that can put the collection, assembly, organization, analysis and presentation of information directly in the hands of those who need it most — the consumers of information.

The idea of “democratizing information” has been around for a couple of decades, and has accelerated in incidence since the dominance of the Internet. Most commonly, the idea is associated with developments and notions in such areas as citizen journalism, crowdsourcing, the wisdom of the crowd, social bookmarking (or collaborative tagging), and the democratic (small “d”) access to publishing via new channels such as blogs, microblogs (e.g., Twitter) and wikis. To be sure, these kinds of democratic information will (and are) benefiting from the use and application of semantics.

But the trend I’m focusing on here is much different and quite new. It is the idea that enterprise knowledge workers can now take ownership and control of their knowledge management functions. In the process, prior bottlenecks due to IT can be relieved and massive new benefits can open up to the enterprise.

Decades-long Mismatches Between KM and IT

It is no secret that IT has not served the enterprise knowledge management function well for decades.  Transaction systems and database systems geared to fast indexing and access to datum have not proved well suited to information or knowledge management. KM includes such applications as business intelligence, data warehousing, data integration and federation, enterprise information integration and management, competitive intelligence, knowledge representation, and so forth. Information management is a bit broader category, and adds such functions as document management, data management, enterprise content management, enterprise or controlled vocabularies, systems analysis, information standards and information assets management to the basic functions of KM. Since the purpose of this piece is not to get into the epistemological differences between information and knowledge, I use these terms more-or-less interchangeably herein.

Knowledge and information management is very big business. Given the breadth and differences in defining the KM and IM markets, let’s take as a proxy the business intelligence (BI) market, one of KM’s most important elements. Various estimates from IDC, Gartner and others place the current value of BI software sales somewhere in the range of $9 billion to $11 billion annually [3]. Further, BI ranked number five on the list of the top 10 technology priorities for chief information officers (CIOs) in 2011. And this pertains to the structured component of information alone.

Yet, at the same time, BI-related projects continue to have high failure rates, often cited as in the 65% to higher range [4]. These failure rates are consistent with KM projects in general [5]. These failures are merely one expression of a constant litany of issues and concerns regarding the enterprise KM function:

The seeming contradiction between continued growth and expenditures for information management coupled with continued high failure rates and disappointments is really an expression of the centrality of information to the modern enterprise. The funding and growth of the IT function is itself an expression of this centrality and perceived importance. These have been abiding trends in our transition to information or knowledge economies.

Bray [2] places the fault for wasted initiatives within the culture of IT. I believe there is some truth to this — variably, of course, depending on the specific enterprise. But the real culprit, I believe, has been the past need to “intermediate” a layer of software and IT expertise between knowledge workers and their source information. A progression of tasks has been necessary — conducted over decades with advances and learning — to get paper information into electronic form, get those forms to be understood and operate in some common ways, and then to develop tools, architectures and frameworks to make sense of it. Yet, as more tasks with required specialized skills have been added to this layer, the actual gulf between worker and information has increased. For example, enterprises still require the overhead and layers of IT to write SQL to get information out and then to prepare and fix reports.

On average, IT now consumes about 4% of all enterprise expenditures and employs about 6% of enterprise workers [6]. IT has become a very thick intermediary layer, indeed! Yet, because of the advances and learning that has occurred in growing and nurturing this layer, we also now have the basis to begin to “disintermediate” the IT layer. Many, if not all, of the challenges noted in the table above can be improved by doing so.

Early Attempts at Self-service and Semantics

One current buzzword in business intelligence is “self service”. By this term is meant giving knowledge workers the tools and systems for creating reports or doing analysis on their own without needing to work through (or be frustrated by) the IT layer. Self-service software was first postulated in the 1990s as a way for information consumers and authors (typically subject-matter experts) to automate some of their knowledge management tasks. Today, it is most commonly applied to self-service reporting or self-service analytics within the BI realm.

As a general proposition, self-service BI has been more myth than reality [7]. Forrester surveys, for example, indicate that IT still develops most BI applications. Of survey respondents in 2009, 70% responded that IT develops the enterprise’s reports and dashboards [8]. However, that figure is not 100%, as it was just a decade earlier, and there is also notable success to some open source providers such as BIRT that address a wide range of reporting needs within a typical application, ranging from operational or enterprise reporting to multi-dimensional online analytical processing (OLAP).

James Kobelius [8] is particularly bullish on the application of Web 2.0 “mashup” applications to knowledge worker purposes. Under this approach, Web-based applications are used and accessed directly by knowledge workers for charting and mapping purposes using Ajax or Flash widgets, such as Google Maps. The conventional BI and KM vendors have begun to more more aggressively into this area. Some notable new entrants — such as Tableau, Factual or Good Data — are also showing the way to more direct access, more flexible reporting and analysis widgets, and cleaner service or platform designs.

These initiatives reside at the display or reporting level. There is another group, including James Kobelius, Neil Raden or Seth Earley, that have addressed how to get disparate information to talk together using ontologies. They refer to “semanticizing” such traditional practices such as master data management (MDM), “ontologizing” taxonomies, or adding Web 2.0 mashups to business intelligence. While these thoughts are moving in the right direction, and will bring incremental benefits, they still are far short of the potentials at hand.

Self-service Information Management

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So far, in the KM realm, the application of semantics has tended to be limited to information extraction (tagging) of text documents and first attempts at using ontologies. The tagging component is essential to enable the 80% of information presently in textual documents to become first-class citizens within business intelligence or knowledge management. The ontology efforts to date appear to be more like thin veneers over traditional taxonomies. Rather than hierarchical structures, we now see graph-oriented ones, but still intended to fulfill the same tasks of enterprise metadata and vocabulary lookups.

The ontology efforts especially are just nibbling around the edges of what can be done with semantic technologies. Rather than looking upon ontologies as just another dictionary (though that role is true), if we re-orient our thinking to make ontologies central to the KM function, a wealth of new opportunities and benefits arises.

A bit more than a year ago, we formulated the Seven Pillars of the Open Semantic Enterprise, which included ontologies and related as some of the central components. In that article [9], we noted the particular applicability of semantic technologies to the information and knowledge management functions within enterprises. We asserted the benefits for embracing the open semantic enterprise as providing the organization greater insights with lower risk, lower cost, faster deployment, and more agile responsiveness. Since that time we have been deploying such systems and documenting those benefits.

Integral to the seven pillars are those aspects that lead to the democratization of information for the knowledge worker, what combined might be called “self-service information management”. As the figure to the right shows, three of the seven pillars are essential building blocks to this capability, two pillars are further foundations to it, with the remaining two pillars only tangentially important.

What the combination of these pieces means is a fundamental change in how knowledge work is done. Through this approach, we can largely disintermediate IT from the knowledge function, can bring knowledge management directly into the hands of those who need it in real time, and fundamentally alter how knowledge management apps are designed and deployed. The best thing is these benefits are an incremental evolution, and retain the use and value of existing information assets.

Building Block #1: Adaptive Ontologies

Rather than peripheral lookup structures or thin veneers, ontologies play the central role in the design of self-service information management. We use the plural on purpose here: what is deployed is actually a library of complementary and modular ontologies that play a variety of roles. Combined, we call these libraries with their representative functions adaptive ontologies.

This library contains the expected and conventional domain ontologies. These represent the actual knowledge space for the domain at hand, and may be comprised of multiple different ontologies representing different domain or knowledge spaces.  These standard semantic Web ontologies may range from the small and simple to the large and complex, and may perform the roles of defining relationships among concepts, integrating instance data, orienting to other knowledge and domains, or mapping to other schema.

From a best practices standpoint [10], we take special care in constructing these domain ontologies such that we provide labels and cues for user interfaces. Some of the user interface considerations that can be driven by adaptive ontologies include: attribute labels and tooltips; navigation and browsing structures and trees; menu structures; auto-completion of entered data; contextual dropdown list choices; spell checkers; online help systems; etc. We also include a variety of synonyms and aliases (the combination of which we call semsets) for referring to concepts and instances in multiple ways and for aiding information extraction and tagging functions. (In addition to organizing and helping to interoperate contributing information, these domain ontologies are also used for what is called ontology-based information extraction (OBIE) via our scones [11] system.)

In addition the library of adaptive ontologies includes some administrative ontologies that guide how instance data can be imported and inter-related (via the Instance Record Object Notation, or irON); what information types drive what widgets (via the Semantic Component Ontology, or SCO); data mapping vocabularies (UMBEL Vocabulary); how to characterize datasets; and other potential specialty functionality.

A forthcoming article will describe the composition and modularity typically found in a library of these adaptive ontologies.

In combination, these adaptive ontologies are, in effect, the “brains” of the self-service system. The best aspect of these ontologies is that they can be understood, created and maintained by knowledge workers. They constitute the only specification (other than theming, if desired) necessary to create self-service knowledge management environments.

Building Block #2: Ontology-driven Apps

The piece of the puzzle that implements the instruction sets within these adaptive ontologies are the ontology-driven apps, or ODapps. A recent article describes these structures in some detail [12].

ODapps are modular, generic software applications designed to operate in accordance with the specifications contained in the adaptive ontologies. ODapps fulfill specific generic tasks, consistent with their dedicated design to respond to adaptive ontologies. For example, current ontology-driven apps include imports and exports in various formats, dataset creation and management, data record creation and management, reporting, browsing, searching, data visualization and manipulation (through libraries of what we call semantic components), user access rights and permissions, and similar. These applications provide their specific functionality in response to the specifications in the ontologies fed to them.

ODapps are designed more similarly to widgets or API-based frameworks than to the dedicated software of the past, though the dedicated functionality (e.g., graphing, reporting, etc.) is obviously quite similar. The major change in these ontology-driven apps is to accommodate a relatively common abstraction layer that responds to the structure and conventions of the guiding ontologies. The major advantage is that single generic applications can supply shared functionality based on any properly constructed adaptive ontology.

Generic functionality included in these ODapps are things like filtering, setting value ranges, choosing the specific display view, and invoking or not various display templates (akin to the infoboxes on Wikipedia). By nature of the data and the ontologies submitted to them, the ODapp signals to the user or consumer what displays, views, filters or slices-and-dices might be available to them. Fed different data and different ontologies, the ODapp would signal the user differently.

Because of their generic design, driven by the ontologies, only a relatively small number of ODapps needs to be created. Once created with appropriate generic functionality, application development is essentially over. It is through the additions and changes to the adaptive ontologies — done by knowledge workers themselves — that new capability and structure gets exposed through these ontology-driven apps. This innovation shifts the locus from software and programming to data and knowledge structures.

This democratization of IT means that everything in the knowledge management realm can become self service. Users and consumers can create their own analyses; develop their own reports; and package and disseminate what they and their colleagues need, when they need it. Through ontology-driven apps and adaptive ontologies, we turn prior software engineering practice on its head.

Building Block #3: Open World Assumption

Integral to this design is the embrace of the open world assumption [13]. Though not a specific artifact, as are adaptive ontologies or ODapps, the open-world approach is the logical underpinning that allows consumers or knowledge workers to add new information to the system as it is discovered or scoped. This nuance may sound esoteric, but traditional KM systems have a very different underpinning that leads to some nasty implications.

Because the predominant share of KM systems are based on relational database systems, they embody a closed-world design. This works well for transaction systems or environments where the information domain is known and bounded, but does not apply to knowledge and changing information. Moreover, the schema that govern closed-world designs are brittle and hard to change and manage. It is this fact that has put KM squarely in the bailiwick of IT and has often led to delays and frustrations. Re-architecting or adding new schema views to an existing closed-world system can be fiendishly difficult.

This difficulty is a major reason why IT resists casual or constant changes to underlying data schema. Unfortunately, this makes these brittle schema difficult to extend and therefore generally unresponsive to changing and growing knowledge. As an environment for knowledge management, the relational data system and the closed-world approach are lousy foundations.

Other Building Blocks

As the self-service information management diagram above shows, RDF and Web services are two further important foundations. RDF (Resource Description Framework) is the canonical data model upon which all input information is represented. This means that the ODapp tools and the adaptive ontologies can work off a single model of knowledge representation. The Web service and architecture component is also helpful in that it allows Web 2.0 technologies to be brought to bear and allows distributed sources and users for the KM system. This provides scalability and distributed applicability, including on smartphones.

The other two pillars of the open semantic enterprise — the layered approach and linked data — are also helpful, but not necessarily integral to the KM and self-service perspectives presented herein.

Benefits from Self-service Information Management

The benefits and flexibilities from self-service information management extend from top to bottom; from creating data and content to publishing and deploying it. Here is a listing of available potentials for self-service, drawing comparison to the current conventional approach dependent on IT:

The fact that any source — internal or external — or format — unstructured, semi-structured and structured — can be brought together with semantic technologies is a qualitative boost over existing KM approaches. Further, all information is exposed in simple text formats, which means it can be readily manipulated and managed with easy to understand tools and applications. Reliance on open standards and languages by semantic technologies also leads to greater use and availability of open source systems.

In short, self-service information management approaches should be cheaper, faster, more responsive and more capable than current approaches.

Great Progress, with Ontology Management the Next Challenge

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Given these perspectives, hearing someone tout data-driven applications or advocate ontologies merely for metadata matching sounds positively Neanderthal. The prospects we have with semantic technologies, ontology-driven apps, and self-service information management systems mean so much more. The prospect at hand is to remake the entire knowledge management function, in the process bringing all aspects from creating and distributing knowledge products into the direct hands of the user. This is truly the democratization of information!

The absolutely fantastic news is none of this is theoretical or in the future. All pieces are presently proven, working and in hand. This is a practical vision, ready today.

Granted, like any new innovation, especially one that is infrastructural and systems-oriented, there are some weak or less-developed parts. These current gaps and needs include:

• Though tools exist, the state of ontology create, edit, manage, update, delete, map and validate tools could be greatly improved [14]. As the central drivers for ODapps, a simplification of tasks geared more to the knowledge worker, and not professional ontologists, is needed (see diagram to right for some of the needed functions). Some of these developments are underway, with more desired
• A relatively complete starting set of about 20 ODapps widgets is presently available. However, more are needed and for different deployment environments. BI analysis remains one weak area, as is an Ajax-based library
• The number of infobox templates is small, and better (WYSIWYG or graphical) create and manage utilities would be most useful, and
• User permission and authorization protocols exist, but are IP-based at present and could be beneficially expanded for different environments and use cases.

Yet, in the grand scheme of things, these gaps are relatively insignificant. The path and general architecture and design for moving forward are now clear.

Self-service information management via appropriately designed semantic technologies is now a reality. It promises to fulfill a vision of information access and control that has been frustrated for decades. We think these are exciting developments for the enterprise — and for the individual knowledge hound. We welcome your inquiries and invite you to join our open OSF group to contribute your ideas.

The Time and Technology is Here to Stand Software Engineering on its Head

As an information society we have become a software society. Software is everywhere, from our phones and our desktops, to our cars, homes and every location in between. The amount of software used worldwide is unknowable; we do not even have agreed measures to quantify its extent or value [1]. We suspect there are at least 1 billion lines of code that have accumulated over time [1,2]. On the order of $875 billion was spent worldwide on software in 2010, of which about half was for packaged software and licenses and the rest for programmer services, consulting and outsourcing [3]. In the U.S. alone, about 2 million people work as programmers or related [4].

It goes without saying that software is a very big deal.

No matter what the metrics, it is expensive to develop and maintain software. This is also true for open source, which has its own costs of ownership [5]. Designing software faster with fewer mistakes and more re-use and robustness have clearly been emphases in computer science and the discipline of programming from its inception.

This attention has caused a myriad of schools and practices to develop over time. Some of the earlier efforts included computer-aided software engineering (CASE) or Grady Booch’s (already cited in [1]) object-oriented design (OOD). Fourth-generation languages (4GLs) and rapid application development (RAD) were popular in the 1980s and 1990s. Most recently, agile software development or extreme programming have grabbed mindshare.

Altogether, there are dozens of software development philosophies, each with its passionate advocates. These express themselves through a variety of software development methodologies that might be characterized or clustered into the prototyping or waterfall or spiral camps.

In all instances, of course, the drivers and motivations are the same: faster development, more re-use, greater robustness, easier maintainability, and lower development costs and total costs of ownership.

The Ontology Perspective in this Mix

For at least the past decade, ontologies and semantic Web-related approaches have also been part of this mix. A good summary of these efforts comes from Michael Uschold in an invited address at FOIS 2008 [6]. In this review, he points to these advantages for ontology-based approaches to software engineering:

• Re-use — abstract/general notions can be used to instantiate more concrete/specific notions, allowing more reuse
• Reduced development times — producing software artifacts that are closer to how we think, combined with reuse and automation that enables applications to be developed more quickly
• Increased reliability — formal constructs with automation reduces human error
• Decreased maintenance costs — increased reliability and the use of automation to convert models to executable code reduces errors. A formal link between the models and the code makes software easier to comprehend and thus maintain.

These first four items are similar to the benefits argued for other software engineering methodologies, though with some unique twists due to the semantic basis. However, Uschold also goes on to suggest benefits for ontology-based approaches not claimed by other methodologies:

• Reduced conceptual gap — application developers can interact with the tools in a way that is closer to their thinking
• Facilitate automation — formal structures are amenable to automated reasoning, reducing the load on the human, and
• Agility/flexibility — ontology-driven information systems are more flexible, because you can much more easily and reliably make changes in the model than in code.

In making these arguments, Uschold picks up on the “ontology-driven information systems” moniker first put forward by Nicola Guarino in 1998 [7]. The ideas around ODIS have had substantial impact on the semantic Web community, especially in the use of formal ontologies and modeling approaches. The FOIS series of conferences, and most recently the ODiSE series, have been spawned from these ideas. There is also, for example, a fairly rich and developed community working on the integration of UML via ontologies as the drivers or specifiers of software [8].

Yet, as Uschold is careful to point out, the idea of ODIS extends beyond software engineering to encompass all of information systems. My own categorization of how ontologies may contribute to information systems is:

1. Domain modeling — this category includes the domain knowledge representations and reasoning and inference bases that are the traditional understanding of ontologies in the semantic space. The structural aspects are akin to a database schema definition; the unique aspects of ontologies reside in their logic foundations and graph structures, which offer more power in inferencing, reasoning and graph analysis than conventional approaches
2. Model-driven architectures (MDA) — like UML, these are platform-independent specifications that provide the functional and dataflow definitions of “models” executed by the system. These are the natural progeny of earlier CASE approaches, for example. Such systems also potentially allow graphical or visual means for building or hooking together components as a substitute to direct coding
3. Program specifications and excecutables — though fairly experimental at present, these approaches use the languages of RDF, OWL or direct use of logic languages to create the equivalent of executable software programs. A couple of experimental systems include Fhat and Neno, for example, point to possible future directions in this area [9]
4. Runtime or utility components — proper construction of ontologies can be a source for labels and prompts within user interfaces and other runtime uses. Because of the ontology basis, these contributions may also be contextual [10]
5. Automated agents — based on context, user choices and the governing ontologies, new instruction sets can be generated via what some term automated agents or “robots” to instruct subsequent steps in the software, including potentially analysis or validation. Mission Critical IT [11] is apparently the most advanced in this area; we discuss their ODASE approach more below
6. Bespoke drivers of generic applications — through using and combining a number of the aspects above, in its totality this approach is a very different paradigm, as we describe below.

When we look at this list from the standpoint of conventional software or software engineering, we see that #1 shares overlaps with conventional database roles and #2, #3 and #4 with conventional programmer or software engineering responsibilities. The other portions, however, are quite unique to ontology-based approaches.

But Is Software Engineering Even the Right Focus?

For decades, issues related to how to develop apps better and faster have been proposed and argued about. We still have the same litany of challenges and issues from expense to re-use and brittleness. And, unfortunately, despite many methodologies du jour, we still see bottlenecks in the enterprise relating to such matters as:

• data access
• queries
• data transformations
• data integration or federation
• reports
• other data presentations
• business analysis, and
• targeted, specialty functionality.

Promises such as self-service reporting touted at the inception of data warehousing two decades ago are still to be realized [12]. Enterprises still require the overhead and layers of IT to write SQL for us and prepare and fix reports. If we stand back a bit, perhaps we can come to see that the real opportunity resides in turning the whole paradigm of software engineering upside down.

Our objective should not be software per se. Software is merely an intermediary artifact to accomplish some given task. Rather than engineering software, the focus should be on how to fulfill those tasks in an optimal manner. How can we keep the idea of producing software from becoming this generation’s new buggy whip example [13]?

For reasons we delve into a bit more below, it perhaps has required a confluence of some new semantic technologies and ontologies to create the opening for a shift in perspective. That shift is one from software as an objective in itself to one of software as merely a generic intermediary in an information task pipeline.

Though this shift may not apply (at least with current technologies) to transactional and process-based software, I submit it may be fundamental to the broad category of knowledge management. KM includes such applications as business intelligence, data warehousing, data integration and federation, enterprise information integration and management, competitive intelligence, knowledge representation, and so forth. These are the real areas where integration and reports and queries and analysis remain frustrating bottlenecks for knowledge workers. And, interestingly, these are also the same areas most amenable to embracing an open world (OWA) mindset [14].

If we stand back and take a systems perspective to the question of fulfilling functional KM tasks, we see that the questions are both broader and narrower than software engineering alone. They are broader because this systems perspective embraces architecture, data, structures and generic designs. The questions are narrower because software — within this broader context — can be now be generalized as artifacts providing the fulfillment of classes of functions.

ODapps: The Ontology-Driven Application Approach

Ontology-driven applications — or ODapps for short — based on adaptive ontologies are a topic we have been nibbling around and discussing for some time. In our oft-cited seven pillars of the semantic enterprise we devote two pillars specifically (#4 and #3, respectively) to these two components [15]. However, in keeping with the systems perspective relevant to a transition from software engineering to generic apps, we should also note that canonical data models (via RDF) and a Web-oriented architecture are two additional pillars in the vision.

ODapps are modular, generic software applications designed to operate in accordance with the specifications contained in one or more ontologies. The relationships and structure of the information driving these applications are based on the standard functions and roles of ontologies (namely as domain ontologies as noted under #1 above), as supplemented by the UI and instruction sets and validations and rules (as noted under #4 and #5 above). The combination of these specifications as provided by both properly constructed domain ontologies and supplementary utility ontologies is what we collectively term adaptive ontologies [16].

ODapps fulfill specific generic tasks, consistent with their bespoke design (#6 above) to respond to adaptive ontologies. Examples of current ontology-driven apps include imports and exports in various formats, dataset creation and management, data record creation and management, reporting, browsing, searching, data visualization and manipulation (through libraries of what we call semantic components), user access rights and permissions, and similar. These applications provide their specific functionality in response to the specifications in the ontologies fed to them.

ODapps are designed more similarly to widgets or API-based frameworks than to the dedicated software of the past, though the dedicated functionality (e.g., graphing, reporting, etc.) is obviously quite similar. The major change in these ontology-driven apps is to accommodate a relatively common abstraction layer that responds to the structure and conventions of the guiding ontologies. The major advantage is that single generic applications can supply shared functionality based on any properly constructed adaptive ontology.

In fact, the widget idea from Web 2.0 is a key precursor to the ODapps design. What we see in Web 2.0 are dedicated single-purpose widgets that perform a display operation (such as Google Maps) based on the properly structured data fed to them (structured geolocational information in the case of GMaps).

In Structured Dynamics‘ early work with RDF-based applications by our predecessor company, Zitgist, we demonstrated how the basic Web 2.0 widget idea could be extended by “triggering” which kind of mashup widget got invoked by virtue of the data type(s) fed to it. The Query Builder presented contextual choices for how to build a SPARQL query via UI based on what prior dropdown list choices were made. The DataViewer displayed results with different widgets (maps, profiles, etc.) depending on which part of a query’s results set was inspected (by responding to differences in data types). These two apps, in our opinion, remain some of the best developed in the semantic Web space, even though development on both ceased nearly four years ago.

This basic extension of data-driven applications — as informed by a bit more structure — naturally evolved into a full ontology-driven design. We discovered that — with some minor best practice additions to conventional ontologies — we could turn ontologies into powerhouses that informed applications through:

• An understanding of the kind of things under consideration, including their inference chains
• The types of data in results sets, and how that informs the nature of the widget(s) (maps, calendars, timelines, charts, tabular reports, images, stories, media, etc.) appropriate to display and manipulate that information, and
• UI and utility functions such as interface labels, mouseovers, auto-suggests, spelling suggestions, synonym matches, etc.

Like the earlier Zitgist discoveries, basing the applications on only one or two canonical data models and serializations (RDF and a simple data exchange XML, which Fred Giasson calls structXML) provides the input uniformity to make a library of generic applications tractable. And, embedding the entire framework in a Web-oriented architecture means it can be distributed and deployed anywhere accessible by HTTP.

Booch has maintained for years that in software design abstraction is good, but not if too abstract [1]. ODapps are a balanced abstraction within the framework of canonical architectures, data models and data structures. This design thus limits software brittleness and maximizes software re-use. Moreover, it shifts the locus of effort from software development and maintenance to the creation and modification of knowledge structures. The KM emphasis can shift from programming and software to logic and terminology [16].

In the sub-sections below, we peel back some portions of this layered design to unveil how some of these major pieces interact.

Built Upon an Ontology- and Web-based Architecture

Again, to cite Booch, the most fundamental software design decision is architecture [1]. In the case of Structured Dynamics and its support for ODapps, its open semantic framework (OSF) is embedded in a Web-oriented architecture (WOA). The OSF itself is a layered design that proceeds from a kernel of existing assets (data and structures) and proceeds through conversion to Web service access, and then ontology organization and management via ODapps [17]. The major layers in the OSF stack are:

• Existing assets — any and all existing information and data assets, ranging from unstructured to structured. Preserving and leveraging those assets is a key premise
• scones / irON – the conversion layer, in part consisting of information extraction of subject concepts or named entities (scones) or the instance record Object Notation for conveying XML, JSON or spreadsheets (CSV) in RDF-ready form (via irON or RDFizers)
• structWSF – a platform-independent suite of more than 20 RESTful Web services, organized for managing structured data datasets; it provides the standard, common interface by which existing information assets get represented and presented to the outside world and to other layers in the OSF stack
• Ontologies — are the layer containing the structured assets “driving” the system; this includes the concepts and relationships of the domain at hand, and administrative ontologies that guide how the user interfaces or widgets in the system should behave
• conStruct – connecting modules to enable structWSF and sComponents to be hosted/embedded in Drupal, and
• sComponents – (mostly) Flex semantic components (widgets) for visualizing and manipulating structured data.

Not all of these layers or even their specifics is necessary for an ontology-driven app design [18]. However, the general foundations of generic apps, properly constructed adaptive ontologies, and canonical data models and structures should be preserved in order to operationalize ODapps in other settings.

OSF is the Basis for Domain-specific Instantiations

The power of this design is that by swapping out adaptive ontologies and relevant data, the entire OSF stack as is can be used to deploy multiple instantiations. Potential uses can be as varied as the domain coverage of the domain ontologies that drive this framework.

The OSF semantic framework is a completely open and generic one. The same set of tools and capabilities can be applied to any domain that needs to manage and understand information in its own domain. With the existing ODApps in hand, this includes from unstructured text or documents to conventional structured databases.

What changes from domain to domain are the data structures (the ontologies, schema and entity references) and their instance data (which can also be converted from existing to canonical forms). Here is an illustration of how this generic framework can be leveraged for different deployments. Note that Citizen Dan is a local government example of the OSF framework with relatively complete online demos:

(click for full size)

Structured Dynamics continues to wrinkle this basic design for different clients and different industries. As we round out the starting set of ODapps (see below), the major effort in adapting this generic design to different uses is to tailor the ontologies and “RDFize” existing data assets.

Lower Layers

Conversion of existing assets to RDF and canonical forms is not discussed further here. See the irON and scones documentation or the TechWiki for more information on these topics.

The structWSF Web Services Layer

The first suite of ODapps occurs at the structWSF Web services layer. structWSF provides a set of generic functions and endpoints to:

• Import or export datasets
• Create, update, delete (CRUD) or otherwise manage data records
• Search records with full-text and faceted search
• Browse or view existing records or record sets, based on simple to possible complex selection or filtering criteria, or
• Process results sets through workflows of various natures, involving specialized analysis, information extraction or other functions.

Here is a listing of current ODapp functions within structWSF (with links to details for each):

At this level the information access and processing is done largely on the basis of structured results sets. Other visualization and display ODapps are listed in the next subsection.

The Semantics Components Layer

The visualization and data display and manipulation ODapps are provided via the semantic components layer. Structured Dynamics’s sComponents are Flex-based widgets that conform to a standard, generic design. Other developers using the OSF framework are developing JavaScript versions [19]. Here is the current library (with links to details for each):

These components can be used in combination with any of the structWSF ODapps, meaning the filtering, searching, browsing, import/export, etc., may be combined as an input or output option with the above.

The next animated figure shows how the basic interaction flow works with these components:

(click for full size)

Using the ODapp structure it is possible to either “drive” queries and results sets selections via direct HTTP request via endpoints (not shown) or via simple dropdown selections on HTML forms or Flex widgets (shown). This design enables the entire system to be driven via simple selections or interactions without the need for any programming or technical expertise.

As the diagram shows, these various sComponents get embedded in a layout canvas for the Web page. By interacting with the various components, new queries are generated (most often as SPARQL queries) to the various structWSF Web services endpoints. The result of these requests is to generate a structured results set, which includes various types and attributes.

An internal ontology that embodies the desired behavior and display options (SCO, the Semantic Component Ontology) is matched with these types and attributes to generate the formal instructions to the sComponents. When combined with the results set data, and attribute information in the irON ontology, plus the domain understanding in the domain ontology, a synthetic schema is constructed that instructs what the interface may do next. Here is an example schema:

(click for full size)

These instructions are then presented to the sControl component, which determines which widgets (individual components, with multiples possible depending on the inputs) need to be invoked and displayed on the layout canvas.

As new user interactions occur with the resulting displays and components, the iteration cycle is generated anew, again starting a new cycle of queries and results sets. Importantly, as these pathways and associated display components get created, they can be named and made persistent for later re-use or within dashboard invocations.

Self-service Reporting

Since self-service reporting has been such a disappointment [12], it is worth noting another aspect from this ODapp design. Every “thing” that can be presented in the interface can have a specific display template associated with it. Absent another definition, for example, any given “thing” will default to its parental type (which, ultimate, is “Thing”, the generic template display for anything without a definition; this generally defaults to a presentation of all attributes for the object).

However, if more specific templates occur in the inference path, they will be preferentially used. Here is a sample of such a path:

At the ultimate level of a particular model of Olympus camera, its display template might be exactly tailored to its specifications and attributes.

This design is meant to provide placeholders for any “thing” in any domain, while also providing the latitude to tailor and customize to every “thing” in the domain.

It is critical that generic apps through an ODapp approach also provide the underpinnings for self-service reporting. The ultimate metric is whether consumers of information can create the reports they need without any support or intervention by IT.

Adaptive Analysis

The Mission Critical IT reference provided earlier [11] helps point to the potentials of this paradigm in a different way. Mission Critical also shows user interfaces contextually chosen based on prior selections. But they extend that advantage with context-specific analysis and validation through the SWRL rules-base semantic language. This is an exciting extension of the base paradigm that confirms the applicability of this approach to business intelligence and general enterprise analytics.

Standing Software Engineering on its Head

All of this points to a very exciting era for enterprise and consumer apps moving into the future. We perhaps should no longer talk about “killer apps”; we can shift our focus to the information we have at hand and how we want to structure and analyze it.

Using ontologies to write or specify code or to compete as an alternative to conventional software engineering approaches seems too much like more of the same. The systems basis in which such methodologies such as MDA reside have not fixed the enterprise software challenges of decades-long standing. Rather, a shift to generic applications driven by adaptive ontologies — ODapps — looks to shift the locus from software and programming to data and knowledge structures.

This democratization of IT means that everything in the knowledge management realm can become “self service.” We can create our own analyses; develop our own reports; and package and disseminate what we and our colleagues need, when they need it. Through ontology-driven apps and adaptive ontologies, we can turn prior decades of software engineering practices on their head.

What Structured Dynamics and a handful of other vendors are showing is by no means yet complete. Our roster of ODapp widgets and templates still needs much filling out. The toolsets available for creating, maintaining, mapping and extending the ontologies underlying these systems are still woefully inadequate [20]. These are important development needs for the near term.

And, of course, none of this means the end of software development either. Process and transactions systems still likely reside outside of this new, emerging paradigm. Creating great and solid generic ODapps still requires software. Further, ODapps and their potential are completely silent on how we create that software and with what languages or methodologies. The era of software engineering is hardly at an end.

What is exceptionally powerful about the prospects in ontology-driven apps is to speed time to understanding and place information manipulation directly in the hands of the knowledge worker. This is a vision of information access and control that has been frustrated for decades. Perhaps, with ontologies and these semantic technologies, that vision is now near at hand.