In Part 1 of this series, I advocated the placement of linked data in an ABox construct from description logics  based on a separation of concerns argument. In Part 2 of this series, I reinforced that argument from the perspective of the work to be done within a knowledge base.
I came to these viewpoints independently. I do not have any special background in these disciplines; I am a recent researcher and practitioner in the field, perhaps akin to a gentleman natural scientist of the 1800s. As these ideas have formed, therefore, I have also attempted to see what some of the noted experts in the field have said and wrote.
Like any other field, there is no common viewpoint or doctrine about these matters. But, there is considerable — and historic — support for this viewpoint of splitting the TBox and the ABox for many different reasons. To my knowledge, this viewpoint has yet to be consolidated and applied to linked data. Perhaps this series will help stimulate that discussion.
The first specific discussion of this matter I was able to discover (though I suspect it had been discussed in earlier internal forums or papers) was on the W3C’s RDF logic mail lists in 2001. While only eight years ago, it does feel a bit like ancient history with regard to the development and understanding of semantic Web languages.
The mail list topic was what role RDF should assume, at a formative point in the language’s development. A possibly restricted scope for RDF akin to a relational database or even as an “ABox” was being discussed. (This restriction, of course, was dropped for the more open, free scope of the present RDF.) To help clarify these matters, Jérôme Euzenat first noted in a thread with Pat Hayes, who would later author the RDF semantics W3C standards document  two years hence, that:
I find two important ideas emerging at this point. First, the roles and purposes of the TBox and ABox are being made clearer, consistent with the definition we have been using  and with better clarity and applicability to the semantic Web than what was earlier presented in the Description Logics Handbook . And, second, the idea of a language split between OWL and RDF viz the TBox and ABox is made public for the first time.
The conceptual foundations of the relational model and RDF are indeed quite similar, based as they are on set theory and relationships. The relational model and description logics are both based on FOL (first-order predicate logic). The linkage with the data table of relational database systems is especially close and direct and was first a topic of a design architecture document by Tim Berners-Lee in 1998 .
There is a rich literature to investigate these aspects in detail. And, of course, these matters are of critical import because 99% of the current structured data in the world is being managed by RDBMs .
However, of more direct interest to this specific series of articles is how this close relationship between RDB and RDF is viewed with respect to the ABox and TBox separation of concerns.
Ian Horrocks (not surprisingly given the nature of his comments above), among others, has played a prominent role in looking at questions such as “hybrid DL-DB” (description logics + [relational] database) systems  and building conceptual links between relational databases and ontological-level reasoning .
We need not deconstruct his observations and arguments here in detail. What I glean from his strong background in description logics, however, is that relational data tables can be left in situ as ABox constructs, in the process gaining the efficiency of limited ABox reasoning and the efficiency of RDBMs. With proper design — which I also understand to be pretty straightforward — it should be possible to design hybrid ABox and TBox systems that work in a distributed context.
(Hmmm; sounds to me like ideas applicable to linked data !).
Horrocks’ et al. more recent paper  and its expansion  propose an extension of OWL for such hybrid or split knowledge bases. These extensions are designed to allow modelers to designate a subset of TBox axioms as integrity constraints. For TBox-level reasoning, these axioms are treated as usual. However, these axioms may also be applied separately to ABox instance data to perform integrity checks. Integrity can then be checked in advance with those axioms ignored during standard TBox reasoning, thus also improving performance. I think these points also have direct relevance to linked data.
As a proponent of the OWL side of the spectrum, Horrocks’ viewpoints have perhaps been too readily dismissed by some in the linked data community. Yet the major reason for looking at all of these questions from the perspective of description logics is to gain a coherent view across the entire semWeb enterprise. In the end, we are linking data for a purpose, to be able to do meaningful work with it. Just as RDB data tables can be looked at and integrated productively as ABoxes in a DL construct, so may linked data.
If there truly is a separation of concerns between instance records (ABox) and reasoning constructs (TBox ontologies), what does that begin to tell us about the languages we need for these purposes? If we can postulate no OWL in a linked data instance construct (the ABox), why not narrow RDFS  as well in order to have a vocabulary only as expressive as what an instance record and its assertions require?
de Bruijn et al in 2005 demonstrated logically how RDF models can be related to description logics-based ontology languages, especially OWL DL, without the need to change syntax or sematics in either language . They noted specifically the use of RDF graphs as ABoxes that could be readily queried using SPARQL.
Herman ter Horst wrote another influential paper in 2005 where he looked closely at the proofs of completeness and decidability for RDF and RDFS . He defined a general RDF graph extension that was fully decidable, and importantly looked at each statement in the language from the standpoints of complexity and computational tractability. He was particularly seeking logics that would be more computationally efficient due to fewer entailments, while still being “decidable” (that is, provable to reach computational closure). Here is the basic chart of plotting the various language dialects he investigated:
He noted that inclusion of XML datatypes required the use of RDFS for closure and the addition of the so-called ‘D*entailment‘ could extend RDFS to include reasoning with datatypes. He then extended that construct into what he called the ‘pD*semantics,’ which was intended to allow useful conclusions to be drawn about instances in the presence of an ontology with relatively low computational complexity.
What this construct means — as I understand it in the context of this series — is that specialized dialects (pD*) could govern the work of instance checking and other specialized work at the ABox level (RDFS) while being fully compatible with the TBox level (OWL) ontology. This means that languages and dialects could be tailored for the work at hand for efficiency and representational reasons, while maintaining logical integrity. Indeed, this very pD* dialect of OWL is now included as one of the proposed profiles, OWL 2 RL, for the new release of OWL 2 .
In a different vein, the paper that won the best award at ESWC in 2007 looked at the question of simplifying RDFS . The authors were able to identify a fragment of RDFS that captured the complete semantics of RDF by carefully removing pieces that only described or allowed reasoning of the language itself.
A relatively streamlined and simplified structure for the ABox is not a new idea. Through version 3x, the Protégé ontology editor included a built-in for SWRL (the Semantic Web Rule Language) that included an ABox ontology :
<rdf:RDF xml:base='http://swrl.stanford.edu/ontologies/built-ins/3.3/abox.owl'> <owl:Ontology rdf:about=' '/> <swrl:Builtin rdf:ID='hasValue'/> <swrl:Builtin rdf:ID='hasURI'/> <swrl:Builtin rdf:ID='isNumeric'/> <swrl:Builtin rdf:ID='notNumeric'/> <swrl:Builtin rdf:ID='isIndividual'/> <swrl:Builtin rdf:ID='isConstant'/> <swrl:Builtin rdf:ID='hasClass'/> <swrl:Builtin rdf:ID='hasProperty'/> <swrl:Builtin rdf:ID='hasIndividual'> <swrlb:maxArgs rdf:datatype='http://www.w3.org/2001/XMLSchema#int'>1</swrlb:maxArgs> <swrlb:args rdf:datatype='http://www.w3.org/2001/XMLSchema#int'>1</swrlb:args> <swrlb:minArgs rdf:datatype='http://www.w3.org/2001/XMLSchema#int'>1</swrlb:minArgs> </swrl:Builtin> <swrl:Builtin rdf:ID='setValue'/> </rdf:RDF>
Don’t be fooled by the OWL designation in this file; for these uses, Protégé by convention requires all of its files to be of the OWL type. Note the simple vocabulary above has solely RDF predicates. We do not think this is yet the correct design (see Part 4), but it captures the right idea.
Logical and mathematical advances since the first releases of RDF and OWL now suggest that, with proper care and design, various dialects or fragments can be designed for specific purposes and for computational efficiency while maintaining — in their combination — logical integrity. An RDFS fragment, if you will, dedicated for linked instance data and ABox instance record purposes, appears conceptually doable. And, it may be computationally advisable.
The SWSE (“swizzy”) project from DERI and the National University of Ireland in Galway has an interesting legacy and has been combining many of these threads into one approach to a semantic web search engine (hence, SWSE). You can use and test for yourself the new VisiNav interface and service, the newest instantiation of SWSE.
Relative to ABox (instance) data, the volume of TBox (structural) data on the Web is small: only around 0.7% of statements were classifiable as TBox statements .
In building what they call SAOR (for Scalable, Authoritative OWL Reasoner) for the SWSE effort, Aidan Hogan, Andreas Harth and Axel Polleres have intersected a number of interesting approaches and have taken some innovative paths to the questions of separating the TBox and ABox . They have further applied this to the large-scale Billion Triples Challenge with interesting findings and results .
In their approach to building SAOR, the designers:
They further picked up on a variant of the ter Horst pD*semantics noted above to speed the reasoner for calculating the forward-chaining inferences.
According to these decisions and rules, they found the overwhelming majority of statements within the 315,000 sources they crawled as being “non-authoritative” and indeed made many decisions that, in essence, threw out statements in the source instance sets. One interpretation, related to the thesis of this series but not directly noted by the authors, is that much of the linked data presently available on the Web is either over-specified or mis-specified. (I would argue that is due in part to linked data instance records trying to do more than their natural assertional role.)
Now, perhaps one could quibble with the rules and the decisions the authors employed (indeed, we do), but that is a topic for another day. What is interesting about the entire SAOR approach, I think, is its close attention to value and authoritativeness, all being split and recast into more tractable ABox and TBox portions, for handling reasoning at scale over large numbers of instances.
In my opinion, this is a seminal approach to the next generation of linked data that warrants much inspection and discussion.
A cursory discussion of the literature also shows some efforts that address the interstitial work areas noted in my conceptual architecture from Part 2.
Part 2 discussed full-text search engines in the broad semantic sense, and not specifically related to the ABox-TBox split. A couple of those and some others deserve a look because of their tighter integration of full-text search and attention to work splits.
A sister project to SWSE is Sindice, which also uses Solr and employs the ter Horst pD* semantic framework . An inspection of Sweet Tools, the semantic Web and -related tools listing, also suggests Aperture (a broadscale, full-text harvester with semantic capabilities); LARQ (which adds free text search to ARQ); Virtuoso (full-text and faceted search on top of a universal datastore); Watson (full-text search of metadata fields); and Zebra (specializing in structured library data and related).
A couple of different approaches are being taken to identity testing, similarity or relations. The more direct approach is to do identity matching with a canonical ID or similar.
The SWSE group has one approach to object consolidation , which uses a clever method based on the owl:InverseFunctionalProperty (IFP) for performing large-scale consolidation of object identifiers for equivalent instances across data sources. Yet, as the authors note,
This is both bad logic and wrong in many cases [see 19 for a critique]. The authors therefore needed to drop this assignment from their method. But, frankly, I think the broader problem again is too much predicate firepower for what should be a simple assertion that Joe Farmer has a blog (in fact, may have three!) and here are their URIs.
A very large, multi-year project to assign unique identifiers to entities is the OKKAM project . The intent is to provide a single and globally unique identifier for any entity through an ‘Entity Name System‘, plus tools. Many methods will be employed to assign the identity relationship; specifics are still forthcoming with dozens of researchers working on the problem. I should note that the reference paper also touches upon some of the massive challenges associated with the current use of owl:sameAs.
Others have questioned a centralized ID service, instead preferring a mechanism that is more local and builds on co-reference research . The ReSIST project has noted some of the issues of owl:sameAs use and management. It has proposed, instead, a ‘Consistent Reference Service‘ (or CRS). Asserting a co-reference in this approach is like its use in linguistics: it means a URI that describes the same entity, as does ‘he’, ‘she’ or ‘it’ as a co-reference in a sentence. This predicate indicates that the two resources are describing the same thing without carrying all of the heavy entailment of the owl:sameAs predicate that semantically means the two resources are exactly the same. The CRS are proposed to be set up and managed locally and in a distributed fashion.
A very different approach to identity assignments is Rough DL, which is a qualitative, “fuzzy” ontology for relating entities or concepts to their similar resources . The method has also been applied to the very difficult problem of bibliographic records , where similarity is harder to judge because of use of initials and abbreviations. Rough DL may be especially appealing because even with the best state-of-the-art, there are error rates in any of the identity relating or disambiguation methods available. And, rather than try to assess these similarities with a probabilistic score, the “fuzzy” approach may even be one that can be reasoned over.
To my knowledge, there is no disambiguation of entities presently taking place for distributed linked data sets. But, if not already, it soon will.
An example of how such a service might occur is the uBio Taxonomic Name Server from The Marine Biological Laboratory at Woods Hole. Via Web service or direct HTML form, an entity name (in this case a biological species) or its variants can be submitted for disambiguation and assignment to the proper identifier (name).
There is much research behind the algorithms and approaches to named entity disambiguation beyond the scope of this present series.
Our arguments to this point in this series do not suggest nor require that current practice need change. Clearly, we are seeing growth, uptake and use with current practices regarding linked data.
One of the beautiful aspects of RDF as a data model and the semantic Web is that the underlying languages and standards are so flexible. Find a way to do stuff better in the future? Fine; go ahead and do it, because what has come before can be easily transitioned or accommodated.
The real thrust of this series has been “best practice.” There are certainly many viewpoints on that topic, and the understanding of it for a linked data environment at scale is also evolving. This is healthy, vibrant and exciting. Who knows what is truly best practice? I personally believe the market will determine that by what gets adopted and becomes self-sustaining by providing value.
However, as Structured Dynamics attempts to think through these issues — to look seriously at moving from simply proving the exposure of Web data to one of meaningfully doing work and relying on it at large scale — we see warts and challenges. Such is growth. It is natural. And change does not mean that what came before was wrong.
So, what do we see as some of these ‘big picture’ implications?:
If we can do these things, we can simplify what it means to publish “linked data-ready” structured data. Being coherent about these matters is a key.