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November 2, 2011

Some notes on ISWC2011…

The 10th International Semantic Web Conference (ISWC2011) took place in Bonn last week. Others have already blogged on the conference in a more systematic way (see, for example, Juan Sequeda’s series on semanticweb.com); there is no reason to repeat that. Just a few more personal impression, with the obvious caveat that I may have missed interesting papers or presentations, and the ones I picked here are also the results of my personal bias… So, in no particular order:

Zhishi.me is the outcome of the work of a group from the APEX lab in Shanghai and Southeast University: it is, in some ways, the Chinese DBPedia. “In some ways” because it is actually a mixture of three different Chinese, community driven encyclopedia, namely the Chinese Wikipedia, Baidu Baike and Hudong Baike. I am not sure of the exact numbers, but the combined dataset is probably a bit bigger than DBpedia. The goal of Zhishi.me is to act as a “seed” and a hub for Chinese linked open data contributions, just like DBpedia did and does for the LOD in general.

It is great stuff indeed. I do have one concern (which, hopefully, is only a matter of presentation, i.e., may be a misunderstanding on my side). Although zhishi.me is linked to non-Chinese datasets (DBPedia and others), the paper talks about a “Chinese Linked Open Data (COLD)”, as if this was something different, something separate. As a non-English speaker myself I can fully appreciate the issues of language and culture differences but I would nevertheless hate to see the Chinese community develop a parallel LOD, instead of being an integral part of the the LOD as a whole. Again, I hope this is just a misunderstanding!

There were a number of ontology or RDF graph visualization presentations, for example from the University of Southampton team (“Connecting the Dots”), on the first results of an exploration done by a Magnus Stuhr and his friends in Norway, called LODWheel (the latter was actually at the COLD2011 Workshop), or another one from a mixed team, led by Enrico Motta, on a visualization plugin to the NeOn toolkit called KC-Viz. I have downloaded the latter, and have played a bit with it already, but I haven’t had the time to have a really informed conclusion on it yet. Nevertheless, KC-Viz was interesting for me for a different reason. The basic idea of the tool is to use some sort of an importance metric attached to each node in the class hierarchy and direct the visualization based on that metric. It was reminiscent to some work I did in my previous life on graph visualization, though the metric was different, the graph was only a tree, the visualization approach was different, but nevertheless, there was a similar feel to it… Gosh, that was a long time ago!

The paper of John Howse et al. on visualizing ontologies was also interesting. Interesting because different: the idea is a systematic usage of Euler diagrams to visualize class hierarchies combined with some sort of a visual language for the presentation of property restrictions. In my experience property restrictions is a very difficult (maybe the most difficult?) OWL concept to understand without a logic background; any tool, visual or otherwise, that helps teaching and explaining this can be very important. Whether John’s visual language is the one I am not sure yet, but it may well be. I will consider using it the next time I give a tutorial…

I was impressed by the paper of Gong Cheng and his friends from Nanjing, “Empirical Study of Vocabulary Relatedness…”. Analyzing the results of a search engine (in this case Falcons) to draw conclusion on the nature, the usage, the mutual relationship, etc., of vocabularies is very important indeed. We need empirical results, bound to real life usage. This is not the first work in this direction (see, for example, the work of Ghazvinia et al, from ISWC2009), but there is still much to do. Which reminds me of some much smaller scale work Giovanni, Péter and I didon determining the top vocabulary prefixes for the purpose of the RDFa 1.1 initial context (we used to call it default profile back then). I should probably try to talk to the Nanjing team to merge with their results!

I think the vision paper of Marcus Cobden and his friends (again at the COLD2011 Workshop) on a “Research Agenda for Linked Closed Data” is worth noting. Although not necessarily earthshaking, the fact that we can and we should speak about Linked Closed Data alongside Linked Open Data is important if we want the Semantic Web to be adopted and used by the enterprise world as well. One of the main issue, which is not really addressed frequently enough (although there have been some papers published here and there) is access control. Who has the right to access data? Who has the right to access a particular ontology or rule set that may lead to the deduction of new relationships? What are the licensing requirements, how do we express them? I do not think our community has a full answer to these. B.t.w., W3C organizes a Workshop concentrating on the enterprise usage of Linked Data in December…

Speaking about research agenda… I really liked Frank van Harmelen’s keynote on the second day of the conference. His approach was fresh, and the question he asked was different: essentially, after 10 or more years of research in the Semantic Web area, can we derive some “higher level” laws that describe and govern this area of research? I will not repeat all the laws that he proposed, it is better to look his Web with the HTML version of his slides. The ones that is worth repeating again and again are that “Factual knowledge is a graph”, “Terminological knowledge is a hierarchy”, and “Terminological knowledge is much smaller than the factual knowledge”. Why are these important? To quote from his keynote slides:

  1. traditionally, KR has focussed on small and very intricate sets of axioms: a bunch of universally quantified complex sentences
  2. but now it turns out that much of our knowledge comes in the form of very large but shallow sets of axioms.
  3. lots of the knowledge is in the ground facts, (not in the quantified formula’s)

Which is important to remember when planning future work and activities. “Reasoning”, usually, happens on a huge set of ground facts in a graph, with a shallow hierarchy of terminology…

I was a little bit disappointed by the Linked Science Workshop; probably because I had wrong expectations. I was expecting a workshop looking at how Linked Data in general can help in the renewal of the scientific publication process as a whole (a bit along the lines of the Force11 work on improving the future of scholarly communication). Instead, the workshop was more on how different scientific fields use linked data for their work. Somehow the event was unfocussed for me…

As in some previous years, I was again part of the jury for the Semantic Web Challenge. It was interesting how our own expectations have changed over the years. What was really a wow! a few years ago, has become so natural that we are not excited any more. Which is of course a good thing, it shows that the field is maturing further, but we may need some sort of a Semantic Web Super-Challenge to be really excited again. That being said, the winners of the challenge really did impressive works, I do not want to give the impression of being negative about them… It is just that I was missing that “Wow”.

Finally, I was at one session of the industrial track, which was a bit disappointing. If we wanted to to show the research community that the Semantic Web technologies are really used by industry, then the session did not really make a good job on that. With one exception, and a huge one at it: the presentation of Yahoo! (beware, the link is to a PowerPoint slidedeck). It seems that Yahoo! is building an internal infrastructure based on what they call “Web of Objects”, by regrouping pieces of knowledge in a graph-like fashion. By using internal vocabularies (superset of schema.org) and using the underlying graph infrastructure they aim at regrouping similar or identical knowledge pieces harvested on the Web. I am sure we will hear more about this.

Yes, it was a full week…

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June 1, 2009

PWC report on Semantic Web

There has already been a number of blogs and tweetes on PriceWaterhouseCoopers’ Spring ’09 Technology Forecast on Semantic Web, but it may still be worth writing about it. The document can be downloaded from the Web free of charge in return for a registration. It includes some of PWC’s own overview on the technology, plus interviews with Tom Scott (BBC), Uche Ogbuji (Zepheira), Lynn Vogel (University of Texas M.D. Anderson Cancer Center), and Frank Chum (Chevron).

The document is clearly not aimed at technologists of the Semantic Web. But there are number of well chosen wordings and quotes that might help us to talk to people around us who have to be convinced about the value of Linked Data/Semantic Web. Just a few of those:

PricewaterhouseCoopers believes a Web of data will develop that fully augments the document Web of today. You’ll be able to find and take pieces of data sets from different places, aggregate them without warehousing, and analyze them in a more straightforward, powerful way than you can now.


Let’s say your agency represents musicians, and you want to develop your own ontology […]. You might create your own ontology to keep better tabs on what’s current in the music world […]. You also can link your ontology to someone else’s and take advantage of their data in conjunction with yours. Contrast this scenario with how data rationalization occurs in the relational data world. Each time, for each point of data integration, humans must figure out the semantics for the data element and verify through time consuming activities that a field with a specific label […] is actually useful, maintained, and defined to mean what the label implies. Although an ontology-based approach requires more front-end effort than a traditional data integration program, ultimately the ontological approach to data classification is more scalable […]. It’s more scalable precisely because the semantics of any data being integrated is being managed in a collaborative, standard, reusable way.


With the Semantic Web, you don’t have to reinvent the wheel with your own ontology, because others […] have already created ontologies and made them available on the Web. As long as they’re public and useful, you can use those. Where your context differs from theirs, you make yours specific, but where there’s commonality, you use what they have created and leave it in place. Ideally, you make public the non-sensitive elements of your business-specific ontology that are consistent with your business model, so others can make use of them. All of these are linked over the Web, so you have both the benefits and the risks of these interdependencies. Once you link, you can browse and query across all the domains you’re linked to.


Traditional data integration methods have fallen short because enterprises have been left to their own devices to develop and maintain all the metadata needed to integrate silos of unconnected data. As a result, most data remain beyond the reach of enterprises, because they run out of integration time and money after accomplishing a fraction of the integration they need.[…] The most basic lesson is that data integration must be rethought as data linking—a decentralized, federated approach that uses ontology-mediated links to leave the data at their sources. The philosophy behind this approach embraces different information contexts, rather than insisting on one version of the truth, to get around the old-style data integration obstacles.

Yeah, we all know that, right? But can we really put it in succint terms for outsiders? That is not that easy… Ie, worth reading the report (and thanks to PWC!).

December 3, 2008

Bridge between SW communities: OWL RL

The W3C OWL Working Group has just published a series of documents for the new version of OWL, most of them being so-called Last Call Working Drafts (which, in the W3C jargon, means that the design is done; after this, it will only change in response to new problems showing up).

There are many aspects of the new OWL 2 that are of a great interest; I would concentrate here on only one of those, namely the so-called OWL RL Profile. OWL 2 defines several “profiles”, which are subsets of the full OWL 2; subsets that have some good properties, e.g., in terms of implementability. OWL RL is one of those. “RL” stands for “Rule Language” and what this means is that OWL RL is simple enough to be implemented by a traditional (say, Prolog-like) rule engine or can be easily programmed directly in just about any programming language. There is of course a price: the possibilities offered by OWL RL are restricted in terms of building a vocabulary, so there is a delicate balance here. Such rule oriented versions of OWL have also precedences: Herman ter Horst published, some years ago, a profile calld pD*; a number of triple store vendors have a similar, restricted versions of OWL implemented in their systems already, referred to as RDFS++, OWLPrime, or OWLIM; and there has been some more theoretical work done by the research community in this direction, too, usually referred to by “DLP”. The goal was common to all of these: find a subset of OWL that is helpful to build simple vocabularies, and that can be implemented (relatively) easily. Such subsets are also widely seen as more easily understandable and usable by communities that work with RDF(S) and need only a “little bit of OWL” for their applications (instead of building more rigorous and complex ontologies which requires extra skills they may not have). Well, this is  the niche of OWL RL.

OWL RL is defined in terms of a functional, abstract syntax (defining a subset of DL) as well as a set of rules of the sort “if that and that triple pattern exists in the RDF Graph then add these and these triples”. The rule set itself is oblivious to the DL restrictions in the sense that it can be used on any RDF graphs, albeit with a possible loss of completeness. (There is a theorem in the document that describes the exact situation if you are interested.)

The number of rules is fairly high (74 in total), which seems to deceive the goal of simplicity. But this is misleading. Indeed, one has to realize that, for example, these rules subsume most of RDFS (e.g., what the meaning of domain, range, or subproperty is). Around 50 out of the 74 rules simply codify such RDFS definitions or their close equivalents in OWL (what it means to be “same as”, to have equivalent/disjoint properties or classes, that sort of things). All of these are simple, obvious, albeit necessary rules. There are only around 20 rules that bring real extra functionality compared to RDFS for building simple vocabularies. Some of these functionalities are:

  • Characterization of properties as being (a)symmetric, functional, inverse functional, inverse, transitive,…
  • Property chains, ie, defining the composition of two or more properties as being the sub property of another one. (Remember the classic “uncle” relationship that cannot be expressed in terms of OWL 1? Well, by chaining “brother” and “parent” one can say that the chain is a subproperty of “uncle” and that is it…)
  • Intersection and union of classes
  • Limited form of cardinality (only maximum cardinality and only with values 0 and 1) and  property restrictions
  • An “easy key” functionality, i.e., deducing the equivalence of two resources if a list of predefined properties have identical values for them (e.g., if two persons have the same name, same email address, and the same home page URI, then the two persons should be regarded as identical)

Some of these features are new in OWL 2 (property chaining, easy keys), others are already been present in OWL 1.

Quick and dirty implementations of OWL RL can be done fairly easily. Either one uses an existing rule engine (say, Jena rules) and lets the rule engine take its course or one encodes the rules directly on top of an RDF environment like Sesame, RDFLib, or Redland, and uses a simple forward chaining cycle. Of course, this is quick and dirty, i.e., not necessary efficient, because it will generate many extra triples. But if the rule engine can be combined with the query system (SPARQL or other), which is the case for most triple store implementations, the actual generation of some of those extra triples (e.g., <r owl:sameAs r> for all resources) may be avoided. Actually, some of the current triple stores already do such tricks with the OWL profiles they implement. (And, well, when I see the incredible evolution on the size and efficiency of triple stores these days, I wonder whether this is really an issue on long term for a large family of applications.) I actually did such quick and dirty implementation in Python; if you are curious what triples are generated via OWL RL for a specific graph, you can try out a small service I’ve set up. (Caveat: it has not been really thoroughly tested yet, i.e., there are bugs. Neither it is particularly efficient. Do not use it for anything remotely serious!).

So what is the possible role of OWL RL in developing SW applications? I think it will become very important. I usually look at OWL RL as some sort of a “bridge” that allows some RDF/SW applications to evolve in different directions. Such as:

  • Some applications may be perfectly happy with OWL RL as is (usually combined with a SPARQL engine to query the resulting, expanded graph), and they do not really need more in term of vocabulary expressiveness. I actually foresee a very large family of applications in this category.
  • Some applications may want to combine OWL RL with some extra, application specific rules. They can rely on a rule engine fed with the OWL RL rules plus the extra application rules. B.t.w., although the details are still to be fleshed out, the goal is that a RIF implementation would accept OWL RL rules and produce what has to be produced. I.e., RIF compatible implementation would provide a nice environment for these types of applications.
  • Some applications may hit, during their evolution, the limitations of OWL RL in terms of vocabulary building (e.g., they might need more precise cardinality restrictions or the full power of property restrictions). In which case they can try expand their vocabulary towards more complex and formal ontologies using, e.g., OWL DL. They may have to accept some more restrictions because they enter the world of DL, and they would require more complex reasoning engines, but that is the price they might be willing to pay. While developers of applications in the other categories would not necessarily care about that, the fact that the language is also defined in terms of a functional syntax makes (i.e., that that version of OWL RL is integral part of OWL 2) this evolution path easier.

Of course, at the moment, OWL RL is still a Draft, albeit in Last Call. Feedbacks and comments of the community as well as the experience of implementers is vital to finalize it. Comments to the Working Group can be sent to public-owl-comments@w3.org (with public archives).

June 13, 2008

Web data visualization with ontologies

Filed under: General,Semantic Web,Work Related — Ivan Herman @ 10:14
Tags: , ,

It is nice to see when very different communities reuse one another’s work, ie, when the fragmentation of research and development into different fields is, at least a little bit, reduced… I ran into a paper Gilson & al[1] on “From Web data to visualization via ontology mapping” in a journal (the Computer Graphics Forum) that is usually not read by Semantic Web experts. So it may be worth drawing their attention on it… Instead of trying to paraphrase the content of the paper, why not simply reproduce the abstract:

In this paper, we propose a novel approach for automatic generation of visualizations from domain-specific data available on the web. We describe a general system pipeline that combines ontology mapping and probabilistic reasoning techniques. With this approach, a web page is first mapped to a Domain Ontology, which stores the semantics of a specific subject domain (e.g., music charts). The Domain Ontology is then mapped to one or more Visual Representation Ontologies, each of which captures the semantics of a visualization style (e.g., tree maps). To enable the mapping between these two ontologies, we establish a Semantic Bridging Ontology, which specifies the appropriateness of each semantic bridge. Finally each Visual Representation Ontology is mapped to a visualization using an external visualization toolkit. Using this approach, we have developed a prototype software tool, SemViz, as a realisation of this approach. By interfacing its Visual Representation Ontologies with public domain software such as ILOG Discovery and Prefuse, SemViz is able to generate appropriate visualizations automatically from a large collection of popular web pages for music charts without prior knowledge of these web pages.

Worth reading. And thanks to my friend David Duce to talk to me about it…

[1] O. Gilson et al., “FromWeb Data to Visualization via Ontology Mapping,” Computer Graphics Forum, vol. 27, Number 3, 2008 (the paper is also available on-line). The paper was originally presented at the joint Eurographics/IEEE Symposium on Visualization, where it won the best paper award.

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