It is clear that Fred Giasson and I have been spending considerable time on description logics of late. While we could perhaps claim that we like hard-to-read and -understand stuff, our reasons for this interest have been quite pragmatic: How to apply linked data principles to real-world commercial and organizational environments? Indeed, what should those principles even be?
Our first intuition, reaching back nearly two years now, was that linked data needed a context for bringing related datasets together. This belief led us to construct the UMBEL subject concept ontology, a basic reference roadmap for helping to point to information related (or “about”) similar subjects. UMBEL as a set of subject concepts has proved useful as a reference roadmap; and the approach to construct UMBEL and its resulting vocabulary (heavily based in SKOS) has also proved helpful to construct specific domain-level ontologies.
By their nature, these ontologies have been conceptual and structural. They define relationships, but are instance-poor. They focus on ways to describe various lenses — world views — into the domains for which we have been engaged.
But superstructures are meant to be built upon and fleshed out. For that, real instance data is required.
Thus, we have more recently shifted from concepts and structure to focus on how to represent the actual things that populate that structure; that is, a domain’s actual objects or instances. We appreciate that different audiences and proponents will use terminology such as instance, or object, or entity, or individual or even foo to describe such things, but for us (Peirceian logic aside) we simply wanted a way to describe the referents to a specific real-world thing .
We initially chose the term ‘named entities‘ to describe these actual objects. This naming arose from the work of Sekine and his 200 named entity types . Typical named entities are specific (individual) people, organizations, events, artifacts (‘Mona Lisa’), places, products, or whatever. For example, here is our first published definition describing ‘named entities‘:
Actually, ‘named entities‘, in even that sense, do not all have proper names with capitalization. Some accepted ‘named entities‘ are also written in lower case, with examples such as rocks (‘gneiss’) or common animals or plants (‘daisy’) or chemicals (‘ozone’) or minerals (‘mica’) or drugs (‘aspirin’) or foods (‘sushi’) or whatever.
|City of Iowa City|
|Clinton St., Iowa City|
|Location in the state of Iowa|
|Coordinates: 41°39′21″N 91°31′30″W 41.65583°N 91.525°W|
|Metro||Iowa City Metropolitan Area|
|- Type||Council-manager government|
|- Mayor||Regenia Bailey|
|- City Manager||Michael Lombardo|
|- City||24.4 sq mi (63.3 km2)|
|- Land||24.2 sq mi (62.6 km2)|
|- Water||0.3 sq mi (0.7 km2)|
|Elevation||668 ft (203.6 m)|
|Population (2007 est.)|
|- Density||2,748.4/sq mi (1,059.4/km2)|
|Time zone||CST (UTC-6)|
|- Summer (DST)||CDT (UTC-5)|
|GNIS feature ID||0457827|
But, hmmm. While ‘daisy’ might be an instance of the common flowers, is that the same as a specific daisy flower? especially when I can see literally thousands of daisy flowers at present in my back yard?
This epistemological question of thing v instance v individual can really mess you up! Furthermore, from the standpoint of describing these things on the Web, are we talking about the real thing, a symbol of some sort (Peirce again!) for that thing, or a multitude of similar descriptive terms (flower, bloom, daisy, florescence, bellis, chrysanthenum) for that thing?
Whether a thing is an instance or an individual or even a class depends on context. A plant taxonomy could represent its terminal nodes as specific species or subspecies of daisy. But, in a flower show, the specific thing being referred to could actually be a unique individual, the Pretty Miss Daisy blue-ribbon winner.
Description logics with its TBox and ABox splits  actually helps considerably to unravel these potentially confounding distinctions. The ABox covers the description of instances with their asserted attributes or characteristics. Thus, we can have an ABox description of the daisy instance that refers to daisies in general or daisies as a species, or we can have an ABox description of an individual daisy with specific proper name in a flower show.
This instance idea is really a very clean one. As long as we focus on the idea of an instance and its attributes, we can put off for the moment (or defer to another layer, that is the reasoning TBox) what kind of instance this is.
After another segue, we’ll return to this instance concept in a moment.
DBpedia, the structured and linked data extraction of “facts” from Wikipedia, was first released about two years ago. Happy 2nd Birthday! I first wrote about DBpedia shortly thereafter claiming, I think somewhat accurately, the birth of the structured Web. We now know that phenomenon and the many additional datasets that nucleate around DBpedia as linked data.
When first explained, DBpedia used examples of so-called Wikipedia infoboxes for the cities of Leipzig or Innsbruck to describe the source of its structured data. (Subsequently Berlin has also been commonly referenced, all understandable given DBpedia’s two principal founders of SÃ¶ren Auer at the UniversitÃ¤t Leipzig and Chris Bizer at Freie UniversitÃ¤t Berlin; of course, many others have joined and meaningfully supported the project since.) Infoboxes are Wikipedia templates that provide standardized, structured information across related articles of a similar type.
I have copied a similar infobox — in this case for the same city type from Wikipedia for my home town of Iowa City — to show one of these structured data templates (shown to right). In using it I am, of course being a bit parochial, but it is also interesting to see the growth of structured data (attributes) that such templates now contain compared to what was available at the time of DBpedia’s first release.
This infobox is a perfect example of an ABox. The instance it describes is the ‘City of Iowa City’. Each of the items that follow show an attribute or data characteristic of some form with its associated value as a key-value pair. Sometimes those values refer to other instances, some of which are individuals, such as the county or name of the mayor.
In ABox terminology, these values are asserted for each attribute. Because this is Wikipedia, which has a reputation for accuracy and authority, we tend to believe and accept these assertions. But, we also know that sometimes these values are not correct, even for Wikipedia. We also know that instance records can come from many, many different sources, perhaps most not with the accuracy or authority of Wikipedia.
It is these types of instance records (for many other types of things than city, of course!) that are now being published as linked data. Today more than 50 general public datasets and perhaps another 50 from the sciences (especially biology) have been published. The total assertions across all datasets now exceeds millions, and the RDF statements that capture all of the relationships between these instances, attributes and concepts that describe them exceed one billion, as the recent Billion Triples challenge attests.
What is nice about this ABox structure is that they are relatively simple — instances characterized by attributes — with the “facts” so expressed understood to be assertions and not necessarily verified truth or accuracy. No matter what the source, there is no guarantee that all assertions will be complete and accurate. (Though, as has proved to be the case for DBpedia because of its Wikipedia heritage, some of the sources can be comfortably asserted to be authoritative.)
Assertions about many of the attributes are relatively straightforward such as, in the Iowa City instance, zip codes or time zones or population. (Still, the estimates used could also be out of date or the estimation methods could be argued.) However, other assertions, more based on interpretation or personal opinion, such as subject matter or political or religious affiliation or bias, can be quite controversial.
Another potential source of error is the linked data assertion that one instance is the owl:sameAs a different instance in a different dataset. Erroneous ‘same as’ assertions can arise quite simply and not require malice or stupidity. For example, for me, I actually live in Coralville, Iowa, not Iowa City. But, Coralville completely abuts Iowa City, shares a school district, and my wife works in Iowa City. I more often than not claim Iowa City as my location, though my actual mailing address is Coralville. How does one reasonably say that the identity of Michael Bergman of Coralville is the same as the Michael Bergman of Iowa City?
So, what can the perspective of the ABox and description logics tell us about these issues?:
This is the rationale from an earlier posting from me called Back to the Future with Description Logics that clearly separates the TBox and ABox functions:
Now, it is true that the ABox and TBox distinctions are conceptual, and in practice not often actual, with no mandate or requirement based in description logics that they remain separate. However, for reasons of tractability and communication and computational performance at scale, there may be justification for keeping these constructs separate .
In the diagram, note that each ABox instance has the simple appearance of an instance record. Also note that the attributes that describe or characterize those instances should also be included and described with relationships modeled at the TBox level. The TBox is the proper place to describe all of the attribute relationships.
So, for Structured Dynamics, we have made a clean split in these roles and data structures in those client architectures over which we have design control. Ontologies populate the TBox level. Instance records assembled into instance dictionaries populate the ABox level, with various instance types governed by their own lightweight schema and vocabularies. This simple functional split leads to cleaner architectures and easier decisions about what belongs in which box or another for a given circumstance. It is also more performant, but more on that in a later part.
Of course, this is not how the linked data and semantic Web is currently architected or conceptualized. This smearing of roles and work responsibilities leads, we think, to many communication issues and slower uptake. As our own thinking gets clearer on these issues, we see there are some key benefits arising from keeping distinct the TBox (ontologies) and ABox (instances) pieces of the semWeb pie.
Wikipedia is a good case in point where conjoining facts with a world view does not work well. One part that does work well are the “facts” in the specific Wikipedia pages that describe things. They are the ABox structure of the Wikipedia knowledge base. Another useful aspect of Wikipedia, kind of at the interstices of the ABox and TBox, are its see also and disambiguation pages. These, too, have proved to be very useful for gathering synonyms for a specific instance or for disambiguating two similarly named instances.
But at the conceptual level of how the world is organized — what are the relations between instances and how those instances are categorized — Wikipedia has arguably been unsatisfactory. Why that might be is a discussion for another time.
One perhaps could make an inverse observation about the Cyc knowledge base where a quite coherent world view of concepts exists (and, actually, many world views through Cyc’s very useful microtheories construct), but is often hard to discern and discover because of the admixing of instances, the coverage of which is also quite lumpy. Some domains have many instances, others are quite sparse.
Trying assiduously to keep bodies of facts and assertions (ABox) separate from how to interpret that world (TBox) brings distinct benefits. The facts base (ABox) is more easily tested for consistency. Different world views (TBox) can be more easily applied and compared against these fact bases. Testing and accepting different aspects of different sources is made easier if the ABox and TBox are not conjoined.
When the different purposes and roles and resulting work that might be applied to ABox and TBox are conjoined, our ability to describe things gets murky. We sometimes call mere controlled vocabularies “ontologies”, for example, which only acts to dilute the concept. We have facts and assertions and relations and hierarchies and stuff ranging from the minutiae to the abstract and sublime being lumped and described with the same terminology. Because we can not clarify and describe to ourselves roles and responsibilities for this stuff, no wonder we can’t communicate well with the broader public.
I believe if the semantic Web community could stand back and try again to apply the rigor of description logics to its enterprise, now that we are gaining some real exposure and success with linked data, we could begin to clean up this emerging mess we are creating for ourselves.
Here are some starting suggestions. Let’s call the combination of ABox and TBox a knowledge base, not an ontology. Let’s reserve the term ontology for the terminological relationships and concepts at the TBox level. And let’s focus on ABox instances as requiring only simple vocabularies to describe the assertions of attributes (what we might call schema consistent with RDFS and relational database schema). We thus could see a set of pieces similar to:
Note I suggest a couple of interesting work items at the interface between the TBox and ABox: disambiguating instances and determining the identity relatedness (for example, ‘same as’) between instances. This is work that should be kept apart from the ABox, but may or may not be best handled in the TBox (and, in any case, is generally separate work from the conceptual structure of the TBox).
This separation of concerns, or something akin to it, would result in a much cleaner — and, therefore, simpler — terminology for communicating with the interested public.
Prima facie, an instance schema that merely needs to capture attribute assertions for an instance will be much simpler than current practice. In turn, that should lead to more patterned schema with easier and quicker extension to new domains and vocabularies. And, that, in turn, will aid ABox consistency checking.
Without the need for ontology mapping at time of conversion, existing RDFizers could be more readily applied to convert other structured data forms to simple RDF schema.
Splitting the pie as suggested is merely the application of separation of concerns, which I believe all would largely acknowledge as leading to better substitutability and modularity. Besides swapping alternative world views to test their implications against common ABox datasets (the Benefit #1 case), we would also likely see quicker improvements in methods and algorithms for ABox consistency checking.
There has been growing interest and effort behind finding methods and vocabularies for describing datasets. The Sindice effort has led to the creation of suggested sitemap standard for crawling purposes; UMBEL has suggested standard vocabularies for describing what datasets are “about”, and voiD has been working to standardize how to characterize the nature of a dataset.
Insofar as the ABox and TBox are more cleanly separated, the decisions and tradeoffs for accomplishing these tasks should enable better dataset descriptions.
The discourse between the OWL and RDF communities can often be strained and at cross purposes. Many data publishers in the OWL community are from the sciences, where reasoning and decidability is imperative . Many in the linked data community are trying to get as much data exposed and published as possible. Kendall Clark recently blogged about these ‘tribes’ to which I also commented.
Like any world view, there is nothing inherently wrong with being more comfortable or wanting to live in one world as opposed to another. But ultimately, the assertions made by most linked data at the ABox level needs to be tested for reasonableness, and structure and an organizational view of the world (TBox) is not terribly helpful without instance data.
I wonder, in fact, whether it might be best for linked data publishers to eschew OWL altogether. Different RDF predicates could be adopted to claim sameAs-type assertions, for example, and ABox vocabularies and schema could be greatly simplified and patterned for easier development and templating. No matter how we cut it, all of this published data and its properties are only assertions until they can be tested for reasonableness, so why not accept that and make linked data generation faster and easier?
Everyone knows that data for data’s sake — linked or not — has to be tested for reasonableness before it can be relied upon for real work. Simple RDF schema for structured search purposes can work alone just fine: simply look at the error rates with current search engines. But, beyond search and non-critical linked browsing, reasoning is necessary.
The reasoning community has known for some time that all of these linked data assertions will have to be tested anyway. So, why not accept roles? Make linked data easier for search and browsing and publishing, and keep silly entailment assertions out of the mix. Then, allocate the reasoning work to coherent ontologies that know their world view and how to test for it. Instance records and ABoxes are not decidable on their own, so why pretend otherwise?
Ever since I first started to learn in earnest about ontology, something has been gnawing at me. The term seemed to be (shall I say?) an obtuse one whose obscurity was not the result of subtle precision or technicality, but rather one of fuzziness. As I introduced my Intrepid Guide to Ontology two years ago, I noted:
Since then, I have continued to find ontology one of the hardest concepts to communicate to clients and quite a muddled mess even as used by practitioners. I have come to the conclusion that this problem is not because I have failed to grasp some ephemeral nuance, but because the term as used in practice is indeed fuzzy and imprecise.
Even two years ago, I noted more than 40 different types of information structure that have at one time or another been labelled as an example of an “ontology”:
Since then, I could add even more terms to this list.
Lack of precision as to what ontology means has meant that it has been sloppily defined. As I have harped upon many times regarding semantic Web terminology, this is a sad state of affairs for the semWeb endeavor that has meaning at the core of its purpose.
I’m pretty sure that the original intent in embracing the concept of ontology within the realm of knowledge representation was not to see this term so broadly misused or mis-applied. I suspect, as well, that if we could sharpen up our understanding and remove some of the fuzziness that we could improve communications with the lay public across many levels of the semWeb enterprise.
Recently, I have been looking to the semantic Web’s roots in description logics. One of my writings, Thinking ‘Inside the Box’ with Description Logics, looked at the conceptual distinctions between the so-called ‘TBox‘ and ‘ABox‘. That is, a knowledge base is a logical schema of roles and concepts and the relationships between them (the TBox), which is populated by the actual data (instances) asserting memberships and attributes (“facts”) (the ABox).
By analogy, in a conventional relational database system, the database or logical schema would correspond to the TBox; the actual data records or tables would correspond to the ABox. Often, the term ontology is used to cover both ABox and TBox statements (which, I argue, only makes the understanding of the ‘ontology’ concept more difficult).
My recent writing, Back to the Future with Description Logics, discussed at some length the advantages of keeping the TBox and ABox separate. This current article now expands on those thoughts, particularly with respect to the definition and understanding of ontology.
The starting point for this new mindset is to return to the ideas of data records or data tables v. the logical schema that is prevalent in relational databases.
The last time I took a census, about a year ago, there were more than 100 converters of various record and data structure types to RDF . These converters — also sometimes known as translators or ‘RDFizers’ — generally take some input data records with varying formats or serializations and convert them to a form of RDF serialization (such as RDF/XML or N3), often with some ontology matching or characterizations. That last census listed these converters:
||Note that MIT’s SIMILE RDFizers also recognizes these formats:||There is a growing list of third-party RDFizers as well:|
This wealth of formats shows the robustness of the RDF data model to capture structure and data relationships from virtually any input form. This is what makes RDF so exciting as a canonical target for getting data to interoperate.
However — and this is crucial — standard users for decades have preferred simple, text-based and human readable formats for writing and transferring their structured data.
These various forms, sometimes well specified with APIs and sometimes almost ad hoc as in spreadsheet listings, are what we call ‘structs‘. Structs can all be displayed as text and have, at minimum, explicit or inferrable key-value pairs to convey data relationships and attributes, with data types and values often noted by various white space, delimiter or other text conventions.
There is no doubt that the vast majority of extant data is found in such formats, including the results of data or information extraction from unstructured text. Indeed, even HTML and many markup languages with their angle bracket-delimited fields fall into this category.
There have literally been hundreds of various formats proposed over decades for conveying lightweight data structures. Most have been proprietary or limited to specific domains or users. Some, such as fielded text, structured text, simple declarative language (SDL), or more recently YAML or its simpler cousin JSON, have become more widely adopted and supported by formal specifications, tools or APIs. JSON, especially, is a preferred form for Web 2.0 applications.
Some, like microformats or this example BibTeX record below (with some non-standard extensions), rely less on syntax conventions and may use reserved keywords (such as AUTHOR or TITLE as shown) to signal the key type for the key-value pair:
ID_LOCAL arXiv:0711.3808 AUTHOR <a href="#Schramm_O">Oded Schramm</a> BIBTYPE ARTICLE ID arXiv:0711.3808 JOURNAL Electron. Res. Announc. Math. Sci. PAGES 17--23 SUBJECTS geom TITLE Hyperfinite graph limits URL http://www.aimsciences.org/journals/doIpChk.jsp?paperID=3117&mode=full URL http://www.aimsciences.org/journals/displayPapers0.jsp?comments=&pubID=221&journID=14&pubString_num=Volume: 15, 2008 Journal Issue VOLUME 15 YEAR 2008
Some of these simple formats have been more successful than others, though none have achieved market dominance. There also appear to be few universal principles that have emerged as to syntax or format. Nonetheless, any of these various struct forms are easy for casual readers to understand and easy for domain experts to write.
For modeling and interoperability purposes, many of these forms are patently inadequate. That is why many of these simpler forms might be called “naïve”: they achieve their immediate purpose of simple relationships and communication, but require understood or explicit context in order to be meaningfully (semantically) related to other forms or data.
Yet, if we have learned nothing else with the phenomenal success of the Web it is this: simplicity trumps elegance or expressivity.
The RDF (Resource Description Framework) data model is expressed as simple subject-predicate-object “triple” statements. That sounds fancy, but just substitute verb for predicate and noun for subject and object. In other words: Dick sees Jane; or, the ball is round. It may sound like a kindergartner reader, but it is how data can be easily represented and built up into more complex structures and stories.
RDF triples can be applied equally to all structured, semi-structured and unstructured content. RDF is clearly a most capable data model that — through its ability to be extended with further concepts and relationships (vocabulary) — can create elegant and logical structures to represent comprehensive domains and knowledge bases. Finding such a model has been a quest in my professional life; I believe we finally have a winner to facilitate data interoperability using RDF.
But RDF has not achieved the market acceptance that its suitability as a data representation model might suggest. I think there are three reasons for this:
Canonical forms embody all of the specification that the canon guiding them requires. What we may have failed to see in embracing RDF, however, is that getting useful data into the system need not carry all of this burden.
So, what does all of this have to do with my starting diatribe about the term ontology?
Whether a single database or the federation across all information known to human kind, we have data records (structs of instances) and a logical schema (ontology of concepts and relationships) by which we try to relate this information. This is a natural and meaningful split: structure and relationships v. the instances that populate that structure.
Stated this way, particularly for anyone with a relational database background, the split between schema and data is clear and obvious. Yet, the RDF, semantic Web and linked data communities have done an abysmal job of recognizing this fundamental separation of concerns.
We create “ontologies” that mix instances and schema. We insist on simple data record conversions that are burdened with relationship specifications as well. We tout a “linked data” infrastructure that is based solely on the same identity of instances without respect or attention to structure or conceptual relationships. We dismiss communities that work to express their data with useful local structures. We insist on standards and practices up and down the data staging and preparation chain that turns off the general market and makes us seem arrogant and dismissive. Frankly, in so many ways, we just don’t get it .
What has struck me personally over the past few months as these realizations have unfolded has been how much our own mindsets and language may be trapping us.
At least for this diatribe, my essential conclusion is that we need to shift the burden of the schema and conceptual relations and (yes) world views to the TBox. We need to skinny down the ABox and make it a warm and welcoming environment by which any structured data (including the most naïve) can join.
So, ultimately, the bottom line is this: the burden of the semantic Web rests on us, not the providers of structured data.
It is time to streamline the ABox to smooth data contributions, assume as publishers the responsibility for the TBox, and keep those concerns separate. As for instance-related stuff, I now intend to refer to them as structs governed by a controlled vocabulary (at most). I intend to reserve ontology as a means to describe a given world view, a TBox, the schema and its relations of the domain at hand. And, frankly, this definition of ontology brings it back in balance with its roots in ontos and the nature of the world.
It’s a good time to lighten up!
I was glad to see Kendall Clark pick up on parts of my earlier piece on Thinking ‘Inside the Box’ with Description Logics. He took one point of view in his posting — that I mostly agree with — but I’d also like to reinforce some other thoughts. And, those thoughts are: description logics (DL) provides earlier lessons and insights that our current zeal for linked data should not overlook, and the lessons we can gain from DL are really fundamental and architectural.
For those of you who have not read Kendall’s piece — which I heartily recommend — let me give you my Cliffs Note’s summary: there are those within the semantic Web community that want to capture the conceptual relationships within knowledge and domains, the Maximum Fidelity tribe, and then those that want to link and describe as many things as possible, the Maximum Scalability tribe, with those (like Kendall’s firm, Clark & Parsia) residing in the middle and following the precepts of DL. The theme is that extremes exist and need to be bridged. 
Posing these contrasts is an effective way to describe different ideas and approaches, but, like all straw men, perhaps it hides nuances and complexity. And, as I note below, it may also pose the wrong straw man dichotomy.
Jim Hendler, for one, took exception to Kendall’s characterization to make the obvious point that different use cases demand different approaches. What was interesting, however, in these interchanges was that a nerve was seemingly struck about differences in viewpoints and approaches. Indeed, the very reference to “tribes” seemed to bring out the (ahem) tribal response.
So, just so we are clear, in what I say below I take on the position of a tribe of one; that is, my own opinion. Of course, this is what all of us do. By positing tribes and viewpoints we simplify what is nuanced and subtly convey that opinions are cultural (“tribal”) and not subject to learning and change. Perhaps within the temporal viewpoint of whatever may be today’s trends and “memes” such thinking may hold, but I fundamentally disagree with such a static view of collective understanding and communities over more meaningful periods of years or decades. But, I digress. . . .
At the risk of being simplistic, I think we can say that there was a rich academic and intellectual history behind description logics going back to the early 1990s . Then, with the seminal semantic Web paper built from thinking in the late 90s by Berners-Lee and published by him and Hendler and Lassila in 2001 in Scientific American , a real marker was put down for machine-readable and -actionable data (via “agents”) accessible on the Web. Many have been disappointed at the slow pace of the semWeb’s unfolding and some have blamed and rejected AI and “big” ontologies for this slowness. As usable standards finally emerged, a newer set of acolytes pushed “just getting data out there” and RDF linked data began to assume prominence from about 2006 onward, spearheaded by DBpedia and the linked open data community.
In so many ways we are coming full circle — coming back to the future — in seeing how our new linked data techniques can again benefit from this earlier DL thinking. Rather then poles and spectrums, I think we are experiencing the need to revisit our intellectual past now that workable publishing mechanisms and scalability and organization assume real prominence. Though clearly not intentional, the linked data community (and, in a related way, microformats), may just have stumbled upon a very cool architectural design that can leverage DL precepts.
Some of this DL and semWeb terminology can be off-putting. But it is helpful to know the lingo if one wants to look into the technical literature. Though most of this stuff can be described without resorting to such terms and can be readily grasped on an intuitive basis, here are some important grounding terms:
Within description logics and for our purposes herein, the two concepts we will most focus upon are the ABox and the TBox. As the definitions above suggest, the TBox is more structural and reflects the logical and conceptual relationships within a domain; that is, the role and concept and class relationships. The ABox provides the data (instance) records and characterizations within that schema; that is the instances and facts assertions. By analogy, in a conventional relational database system, the database or logical schema would correspond to the TBox; the actual data records or tables would correspond to the ABox.
These distinctions suggest very different purposes and roles, then, for the TBox and the ABox:
While certainly many of the ABox tests and checks require TBox structure, there is a pretty clear separation of purpose and role. Moreover: 1) the scale of the information in each “box” is vastly different (perhaps a few to hundreds to at most thousands of concepts in the TBox in contrast to potentially millions or more instances in ABoxes); and 2) ABox dataset repositories may also be (indeed, often are!) numerous, spatially distributed and semantically heterogeneous.
DL and semantic Web stuff in general are data and logic models, not architectural guidance. So, rarely does one see discussion of the architectural imperatives that some of these logical underpinnings provide. We see knowledge bases and ontologies both used as umbrella terms encompassing both the ABox and the TBox.
However, our own deployment experiences and the literature suggest there are manifest advantages to keeping the TBox and ABox separate:
|Advantages of Keeping the TBox and ABox Separate|
It would be useful to refrain from lumping the very different purposes of ABoxes and TBoxes under the umbrella rubric of ‘ontology’. It would also be useful for designers and vocabulary authors to be more explicit in their own minds as to purpose and content when formulating new ontologies. Smushing all of these concepts into one bubbling mess may not lead to clarity nor good performance.
Taking these basic ideas we can visualize a general schematic for best practice splits within the ontology or knowledge base:
The TBox is clearly focused on the domain at hand, but also includes links and equivalents to external ontologies. The TBox level should be entirely free of instance data, though all attributes, properties and concepts that might be found at the ABox level are also defined with their relationships at the TBox level. Like any semWeb ontology, this TBox level should also re-use common Web ontologies such as FOAF, SIOC, UMBEL, etc.
It is also the case that because of the reasoning needs at the TBox layer, the semantic Web language used should likely be a dialect of OWL (see below).
(BTW, for my own practice, I will try to limit my use of the ‘ontology’ term to the concepts and classes at this TBox level.)
The ABox level, in contrast, may consist of multiple datasets and name spaces. These structures are most appropriately seen as lightweight controlled vocabularies with limited structure; if written by scratch perhaps limited to RDF or RDFS (the schema variant). This layer, however, can also remain in non-semWeb native form — such as RDBMS data tables, microformats or other formats — that are wrapperized for interoperability through one or more ‘RDFizers‘ or GRDDL.
These structures should likely not make many external assertions, if any, and if done, perhaps in separate mapping or linkage file that can be processed and analyzed independently. It is important, however, to make sure that all attributes at this ABox layer have a counterpart with relationships and structure defined at the TBox layer.
This architectural design enables complete independence of the instance datasets from the inferencing logic or federation that might be applied to them.
Since it first took off in 2006, linked data and the various datasets now shown in the ‘LOD cloud‘ have been dominated by instance data. There are perhaps 10 million to 20 million instance objects available as linked data, many of which are derived from Wikipedia (via DBpedia) with attributes or structure coming from the Wikipedia infoboxes.
“There need not be a trade-off between expressiveness and scalability. Proper design, language choice and architecture can readily achieve both — while maintaining independence of scope or purpose.”
A similarly fast explosion has taken place with structured records via microformats and other simple data structures. For example, some earlier estimates suggest there are perhaps more than 2 billion pages that include microformats .
I have at times recently made comments about the dominance of instance data within the linked data community and the need for organizing structure. While this observation, I believe, remains true and provides a rationale for UMBEL as an organizing subject structure (or any other organizing structure, for that matter), perhaps I have been missing a more fundamental point: linked data (at least as practiced to date) is really about exposing ABoxes with simple structure. Perhaps, by serendipity, linked data (and other light structures like microformats) are showing the way to a distributed, mixed ABox-TBox structure for the Web.
With this altered viewpoint, a number of new observations emerge:
Linked data and microformats and other lightweight structures are now giving us the exposed instance data to begin reasoning and showing differences due to inferencing and other logic advantages for the semantic Web. Now that the ABox is being proven, let’s move on and stress-test the TBox!
Since the first version of OWL there has been confusion and some limitations with the dialects of the language. Only OWL Full allowed classes to be treated both as instances and classes (so-called metamodeling), and was therefore used as the basis for mapping UMBEL, for example, to RDF and RDFS vocabularies and to Cyc. This design was necessary, but left UMBEL undecidable using standard DL reasoners; only the two dialects of OWL DL and OWL Lite met description logics requirements.
Indeed, it was even hard to determine what dialect an OWL file represented, among many other problems and issues. The technical committee behind OWL 2, in fact, has written an excellent critique of issues with this first version of OWL .
For nearly two years the next version of OWL, OWL 2, has been undergoing development, with the last draft now published and available for last comment before January 23 . Lessons and refinements to the use of DL have also occurred. Some have criticized this effort and have criticized the need for OWL 2′s growing expressiveness and vocabulary [see 1, for example]. I believe these criticisms to be unfair and to miss many of the thoughtful improvements in this new version.
Version control and expressiveness are two of these benefits. A broader benefit, though, has been the keen attention the developers have given to compliance with description logics and the ability to formulate fragments (called “profiles”) that only present subsets of DL useful for computational considerations . For example, one profile, OWL 2 QL, appears well suited to the ABox; another, EL, appears well suited to the TBox. Users and tools builders may define other subsets of OWL 2 to deal with different use cases.
What is emerging are possible design patterns that would have comprehensive TBox guidance and inference structures that first receive a query, then do query rewriting for less capable OWL dialects and mapping to distributed ABox datasets, some of which might be kept in native relational DB or other structural forms [9, 14, 15]. Other approaches and designs, such as overviewed for DLDB2, KAON2, OWLIM, BigOWLIM and Minerva, are testing other architectural and DL combinations [see 15]. And, at the level of the specific triplestore, other optimizations are being made such as owl:sameAs or query rewrite with Virtuoso . This new version of OWL and its profiles have adapted to past lessons and can be matched well to the emerging hardware and architectural designs.
These changes appear to now provide the option for various dialects of OWL to be matched with reasoners and architectural designs in order to optimize for different purposes. Rather than a spectrum, we appear to be learning and maturing. Hopefully, getting back to the architectural implications of the TBox – ABox split can show us there need not be a trade-off between expressiveness and scalability. Proper design, language and dialect choice, and architecture can readily achieve both — while maintaining independence of scope or purpose.
Thanks, OWL 2! You have fulfilled your commitment to description logics. It is now our turn to figure out the best practices for working with these tools.
Fred Giasson and I are pleased to announce our new venture, Structured Dynamics LLC. Structured Dynamics springboards from our previous company, Zitgist, and simplifies and focuses our structured Web and linked data services. We are pleased to begin operations with major clients in hand and exciting prospects for helping to make major aspects of the semantic Web a reality in 2009.
The astute observer may have noticed the transition to Structured Dynamics occurring in the background over the past month or so. But, securing start-up contracts and getting all of the paperwork niceties in place has caused us to delay a formal announcement until the symbolic beginning of a New Year and new administrations (on many fronts!).
Structured Dynamics (SD) kicks off with myself as CEO and Fred as CTO and chief programmer. BTW, we are actively looking to hire the right developers with experience in the semantic Web, linked data, PHP, Python, Virtuoso or Drupal; please contact Fred if you think your fit is great.
Structured Dynamics is dedicated to assist enterprises and non-profit organizations and projects to adopt Web-accessible and interoperable data. The basic premise is that the data itself becomes the application: by virtue of its structure, information can be combined, inferred, analyzed, filtered by tag or facet, queried, searched, reported, templated or visualized. A suite of Web services provides these capabilities, generalized to be driven by the structure of the input data itself.
Structured Dynamics supports both open and proprietary data, including the extraction of structure from fully structured data (RDF), from conventional structured data (such as relational databases), from unstructured (text) data, and from semi-structured (metadata, tags and mark-up) sources. SD’s professional services include:
Structured Dynamics is platform- and language-neutral, though all of our services are based on open source software. Fred and I have been advocates of linked data done right as our frequent and oft-cited blog posts attest.
Our ongoing open source ontology efforts for UMBEL, BIBO (the Bibliographic Ontology) and MO (Music Ontology) will continue without interruption. Community members will not notice any change in activity level or support. Structured Dynamics will continue to support Fred’s and my time as editors of these ontologies, and SD will continue to host the ontologies’ Web sites and documentation and support activities.
As central aspects of Structured Dynamics’ services, expect to see us develop and support still further useful ontologies for the benefit of the open marketplace.
Though Zitgist has ceased as an operating company, zitgist.com remains as a sandbox to host its prior services including UMBEL Web services and the DataViewer. Zitgist’s other Web properties of Ping the Semantic Web and Talk Digger are also now operated and supported jointly by OpenLink Software and Structured Dynamics.
Fred and I are tremendously grateful to OpenLink Software and Kingsley Idehen for early incubation support. Structured Dynamics retains a close partnership with OpenLink and plans to support and utilize the Virtuoso framework frequently in our services. We are further excited to be able to support and recommend best-of-breed choices to our customers whatever their circumstance.
Pundits first cited 2008 as the year of breakthrough for the semantic Web; many more are now doing so for 2009. Clearly, the transition to commercial readiness and deployment is underway.
The dialog is naturally shifting from one taking place solely within the community to outreach to the marketplace. Our language needs to become less jargon-filled and technical to one of solutions and benefits. Pragmatism and piecing together cost-effective, end-to-end solutions will take precedence over tools and arcane arguments. It is now time to get the job done.
The past decade has produced ready to deploy standards and best practices. The building blocks for comprehensive, enterprise-capable and -scalable solutions now exist. Fred and I look forward with much excitement to hopefully be some of the key agents in helping to make this maturation and change come about.
We enter the New Year with much optimism and hope. We wish all of you individually — and the global commons collectively — a much improved and more prosperous 2009! Now, let’s kick some A and have some fun!
I’m pleased to wrap up a multi-part interview with the Federated Search Blog as part of their ongoing ‘Search Luminaries’ series. Sol Lederman, editor of the blog, does a thorough and comprehensive job! Over the past month on every Friday, I have answered some 25 or so of his detailed questions.
Federated Search Blog was particularly interested in the deep Web, its discovery and size. Many of the early questions deal with those themes. However, by Part 4 things get a bit more current, with the topics shifting to the semantic Web, linked data and Zitgist.
Here are the links to the series:
To give you a flavor of the interview, here is an example of one of the questions (and probably my favorite):
20. Tim Berners-Lee, credited with inventing the World Wide Web, has been talking about the importance and value of the Semantic Web for years yet common folks don't see much evidence of the Semantic Web gaining traction. Is there substance to the Semantic Web? What's happening with it now and what does its future look like?
No, actually, this is a very good question. As things go, I am a relative newbie to the semantic Web, only having studied and followed it closely since about 2005. I'm sure my perspective in coming later to the party may not be shared by those at the beginning, which dates to the mid-1990s as Berners-Lee's vision naturally progressed from a Web of documents, as most of us currently know the Web, to a Web of data.
I think there is indeed incredibly important substance to the semantic Web. But, as I have written elsewhere, the semantic Web is more of a vision than a discernable point in time or a milestone.
The basic idea of the semantic Web is to shift the focus from documents to data. Give data a unique Web address. Characterize that data with rich metadata. Describe how things are related to one another so that relationships and connections can be traced. Provide defined structures for what these things and relationships "mean"; this is what provides the semantics, with the structures and their defined vocabularies known as "ontologies" (which in one analog can be seen as akin to a relational database schema).
As these structures and definitions get put in place, the Web itself then becomes the infrastructure for relating information from everywhere and anywhere on any given topic or subject. While this vision may sound grandiose, just think back to what the Web itself has done for us and documents over the past decade or so. This same architecture and infrastructure can and should be extended to the actual information in those documents, the data. And, oh, by the way, conventional databases can now join this party as well. The vision is very powerful and very cool.
Progress has indeed been slow. Many advocates fairly point to how long it takes to get standards in place and for a while people spoke of the "chicken-and-egg" problem of getting over the threshold of having enough structured data to consume to make it worthwhile to create the tools and applications and showcases that consume that data.
From my perspective, the early visions of the semantic Web were too abstract, a bit off perhaps. First, there was the whole idea of artificial intelligence and machines using the data as opposed to better ways for humans to draw use from the data at hand. The fundamental and exciting engine underneath the semantic Web — the RDF (Resource Description Framework) data model — was not initially treated on its own. It got admixed with XML that made understanding difficult and distinctions vague. There is and remains too much academia and not enough pragmatics driving the bus.
But that is changing and fast.
There is now an immediate and practical "flavor" of the semantic Web called linked data. It has three simple bases:
(1) RDF as the simple but adaptable data model that can represent any information — structured or unstructured — as the basic "triple" statement of subject-predicate-object. That sounds fancy, but just substitute verb for predicate and noun for subject and object. In other words: Dick sees Jane; or the ball is round. It sounds like a kindergartner reader, but that is how data can be easily represented and built up into more complex structures and stories
(2) Give all objects a unique Web identifier. Unique identifiers are common to any database; in linked data, we just make sure those identifiers conform to the same URIs we see constantly in the address bar of our Web browsers, and:
(3) Post and expose this stuff as accessible on the Web (namely, HTTP).
My company adds some essential "spice" to these flavors with respect to reference structures and concepts to give the information context, but these simple bases remain the foundation.
These are really not complex steps. They are really no different than the early phases of posting documents on the Web. Only now, we are exposing data.
More importantly, we can forget the chicken-and-egg problem. Each new data link we make brings value, in the similar way that adding a node to a network brings value according to Metcalfe's Law. Only with linked data, we already have the nodes — the data — we are just establishing the link connections (the verbs, predicates or relations) to flesh out the network graph. Same principle, only our focus is now to connect what is there rather than to add more nodes. (Of course, adding more linked nodes helps as well!)
The absolutely amazing thing about our current circumstance as Web users is that we truly now have simple and readily deployable mechanisms available to finally overcome the decades of enterprise stovepipes. The whole answer is so simple it can be mistaken as snake oil when first presented and not inspected a bit.
As an industry accustomed to hype and cynical about so much of this, I only ask that your readers check out these assertions for themselves and suspend their normal and expected disbelief. For me, in a career of more than 30 years focusing on information and access, I feel like we finally now have the tools, data model and architecture at hand to actually achieve data interoperability.
Thanks again to Sol and Federated Search Blog for this opportunity.