Semantic Web Company

Some new releases around Apple´s iPhone family, like the new OS3.0 or the new 3G S have stimulated another big hype around this “little darling”. I took a look at another facet, namely: Has the Semantic Web entered the iPhone realm yet (or vice versa)? Experts have been talking about the need for semantically enhanced mobile applications for years, so let´s see, if they are in place already.

Searching for “semantic web” in the AppStore delivers six results, one of them called “SemanticWb” is obviously an interesting match. The application “extracts current life sciences and health care knowledge and place them conveniently at your fingertips on your iPhone”. The application offers search suggestions and moderated search and retrieves articles from PubMed or genetic disorders which are related to the search term. Good start, this is a neat iPhone application which should be interesting for medical doctors and related professions.

Another application on the iPhone which is related to the semantic web is the “English wordnet dictionary” based on WordNet from Princeton University.

So, not much semantic web on the iPhone so far – I thought until Evriverse was released some weeks ago. The iPhone version of evri.com offers a new way to find connections between all kind of things. Similar to OpenCalais Evri can extract people, places, organisations, products etc. from unstructured information like news or blogs. The innovation around Evriverse is the way how complex search queries around “anything” can be formulated by just touching the screen. For example, if you are looking for information about “Tim Berners-Lee” the application not only offers auto-complete but also suggests related people, organisations etc. to refine any search query. Such relations are updated constantly and are based on the semantic analysis of news and blogs.

Evriverse offers the most comfortable way to do news research on the iPhone today. It shows how semantic technologies can enhance user experience on a mobile device and it will path the way to more semantic (web) apps on the iPhone.

My colleague Tassilo Pellegrini recently did an interview with Bo Hu, a researcher and former fellow at the Intelligence, Agent, Multimedia Group (IAM), University of Southampton: At present, Bo is working at the SAP Research Center CEC Belfast where his research focuses on the application of Semantic Web and Web 2.0 technologies in e-learning and e-healthcare. In the interview, he talks about the potential of Semantic Web and intelligent agent technologies in medicine and life sciences, which he sees as a discipline particularly suited for semantic technologies as “many sub-domains of medicine are subject to controlled nomenclatures providing solid ground upon which semantically enriched applications can be built.” About the potential of intelligent agents:

[...] in distributed environments, it is difficult to exploit the available data from different sources, especially data that is normally projected onto the body of a patient to reach diagnostic and prognostic decisions. Many of the available data are interrelated, calling for paradigms that facilitate knowledge discovery by intelligently integrating data sources.

An agent-based framework is particularly useful in this case where individual agents are equipped with “memory” and “reasoning/thinking” capabilities to constantly acquire new knowledge and solve allocated tasks. Communication among agents, prescribed by a common vocabulary/ontology, ensures the entire community works towards a common goal.

On the other hand, frameworks with agents encapsulating special functions deliver better customised and personalised healthcare. As a result, we will be witnessing more patient power and better adherence to treatment regimens.

Read the whole interview on the SWC website.

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• Tonyvirtual: Intelligent Service Coordination in the Semantic Web

(by Matthias Samwald)

The first VoCamp ever was successfully completed this week at Oxford University. Tom Heath (Talis) and Jun Zhao (Oxford University) led us through two days devoted to creating new vocabularies, schemas and ontologies. The first day was mainly spent on finding common interests, getting to know each other, and identifying the vocabularies that needed to be created. The second day was spent on creating the vocabularies, first on paper, then on the computer.

Fabien Gandon introduced me to his interesting work around corporate ontologies, which I will explore in further detail for the KiWi project. We also made significant progress on developing a basic, common ontology for the representation of agreement, disagreement and discourse, based on SIOC, SCOT, FOAF and the bibliographic ontology. Such an ontology can be of great utility in many knowledge domains, such as biomedical research or the representation of bug/issue reports in software development knowledge management (something that needs to be adressed for adapting the KiWi system for a use-case at Sun Microsystems). I will elaborate on these developments in separate blog posts next week.

In a very short timespan, the participants of the VoCamp created several new vocabularies, such as:

IRC Vocabulary

Participation Ontologies

UDO (Unified Discourse Ontology)

VotePost

Evidence ontology

Whisky Ontology (yes, it’s an ontology about whiskey)

Journey Ontology

Data publishing, sharing, visualisation ontology

On the second day of the VoCamp I also held a short session called „Do OpenCyc and UMBEL know it?“, where I asked for classes and properties that others wanted to create in their developing vocabularies. It turned out that OpenCyc and UMBEL had a relatively good coverage of the terms that others were creating, including whiskey, evidence, or the relation that one person is the boss of another person (relevant for the corporate Semantic Web). I tried to emphasize that linking and re-using such existing entities was vital for the success of new vocabularies, and of the Semantic Web as a whole. Others objected that re-using such existing resources might not be possible given the often very specific requirements and short time-frames of some projects. Still, I think that linking to existing, large resources on the Semantic Web should have a very high priority when developing new vocabularies.

If you are interested in getting new vocabularies out, but did not get the chance to attend VoCamp Oxford: don’t worry, there are many VoCamps planned for the near future. The next VoCamp will already happen in November, and will be located at DERI Galway. The Semantic Web Company will host a VoCamp in Vienna next year.

(by Matthias Samwald)

I am packing my bags once again: The first VoCamp (hosted at Oxford University, UK) is about to start this week. So, what is a VoCamp supposed to be? The official definition reads like this: “A VoCamp is a series (hopefully) of informal events where people can spend some dedicated time creating lightweight vocabularies/ontologies for the Semantic Web/Web of Data. The emphasis of the event(s) is not on creating the perfect ontology in a particular domain, but on creating vocabs that are good enough for people to start using for publishing data on the Web.”

I always thought that the lack widely established vocabularies/ontologies has been very damaging to the developent of the Semantic Web. The VoCamp initiative could help changing this situation for the better, so I really hope that this is the start of a long series of events.

My topics of main interest are: 1) Associative Tags; 2) Agreement, Disagreement, discourse; 3) Corporate Semantic Web, 4) “Are upper level ontologies/vocabularies not so bad after all?”, 5) “ Cleaner schemas and ontologies”. These interests are motivated partly by use-cases from the “KiWi – Knowledge in a Wiki” EU project, and partly by developments in the area of biomedical research at DERI Galway and the W3C Interest Group for Health Care and Life Science. Details below.

__Associative Tags__

Tagging is one of the key components of the ‘Web 2.0′, and Semantic Web technologies will help to make tagging even more powerful. Schemas such as SCOT or MOAT have already been established, and make it possible to ‘tag’ not only with simple strings, but with entities. These entities (such as concepts described in SKOS) can be associated with clear semantics and can be further described with RDF statements, to describe hierarchies of entities, or to link entities to rich data sources such as DBpedia. This enables sophisticated data-integration and cross-data source queries that would not have been able with simple, string-based tags.

On the other hand, Semantic Web developers can learn from the simplicity that has made tagging so successful. Creating useful tags is very simple, and good user interfaces can further improve the simplicity of creating useful tag with feature such as autocompletion and tag recommendation. This simplicity should server as a role model for many Semantic Web applications.

Specifically, I am interested in what I call ‘associative tags’, bundles of tags/entities/concepts that can be used for the simple representation of facts. The primary intention of creating aTags is not the categorization of the document, but the representation of the key facts inside the document. Key facts in the biomedical domain might be, for example,

“Protein A interacts with protein B” (which can be represented with an aTag comprising of the three entities “Protein A”, “Molecular interaction” and “Protein B”) or

“Overexpression of protein A in tissue B is the cause of disease C” (an aTag comprising of the four entities “Overexpression”, “Protein A”, “Tissue B” and “Disease C”).

Once the aTags from these different sources are aggregated, it is possible to pose a query such as “show me molecules that are associated with molecules that are associated with disease C”, yielding “protein A” as an answer. Hierachies (in the form of rdfs:subClassOf and skos:narrower) can be used to expand queries based on background knowledge (e.g., that “disease D” is a subclass of “disease C”).

In many cases (especially with some ontologies in the biomedical domain), creating such associative tags can be much simpler than the creation of ‘real’ statements, i.e., relations between individuals and property restrictions of classes.

__Agreement, Disagreement, discourse__

Many people in the Semantic Web community are interested in the representation of argumentation structures on the web. For example: stating that one snippet of text contains statements that are in disagreement with another snippet of text, which is in agreement with yet another snippet of text. This can be of use for many knowledge domains, such as news articles, biomedical publications or reports submitted to a software bug tracker. Of special interest in this context are extensions of established schemas, especially SIOC. There is also another ontology called SWAN that is specifically tailored to the biomedical domain, and efforts to align SWAN with SIOC have started recently.

__Corporate Semantic Web__

As Semantic Web technologies are finally getting mature enough to allow industrial uptake, it is becoming clear that ontologies for describing organization structures and business processes are still lacking maturity. FOAF allows us to represent basic information about persons, organizations and their relationships, but lacks vocabulary for stating that one person is the boss of another person, that a project consists of several subtasks, et cetera. While there are some small projects that try to create such schemas/ontologies, a solution of widespread acceptance does not seem to be in sight at the moment.

__Are upper level ontologies/vocabularies not so bad after all?__

FOAF seemingly tried it a long time ago – foaf:Person is a subclass of, “http://xmlns.com/wordnet/1.6/Person”, foaf:Document “http://xmlns.com/wordnet/1.6/Document” and so on. Linking to external schemas/ontologies (or making use of their classes and properties directly) can definitly help in facilitating semantic interoperability. For a long time, many web developers were very skeptical about such ‘top-down’ approaches of data integration, but recently the recognition of the potential values of such resources seems to be increasing. In parallel, the recent 1-2 years brought us some very large upper ontologies that are available as linked data, such as:

• Wordnet 2.0, hosted by the W3C
• Yago/DBpedia
• OpenCyc (now with new URIs)
• UMBEL (derived from OpenCyc and others).

I think the practice of re-using and linking to such upper ontologies as should become popular (again). It helps in creating a highly interlinked Semantic Web, and helps to avoid re-inventing the wheel for each new schema/ontology. This linking should not be done post-hoc, but should be a central part of the early stages of vocabulary/ontology/data creation.

__Cleaner schemas and ontologies__

Working with established ontologies and schemas in ontology editors can be a chore. Most have dependencies on other ontologies, but don’t use owl:imports. Most use an awkward mix of OWL statements and RDF(S), resulting in ontologies that are OWL Full. Many require some OWL reasoning to make use of sameAs statements and inverse properties, but at the same time reasoning is complicated because the ontologies are OWL Full or even contain logical inconsistencies. Often enough, there seems to be no practical reason for the design choices that caused the trouble: some minor changes can turn a messy OWL Full ontology into an OWL lite or OWL DL ontology. At the moment, many different working groups have created local versions of schemas such as FOAF or Dublin Core that are valid OWL-DL to fix that problem.

It doesn’t have to be this way.

Trying to adhere to OWL lite/DL and adding owl:imports statements can help building cleaner, modular and more sustainable ontologies, and does not require significant additional effort during the creation of ontologies. Maybe we can find a consensus that this would be a worthwhile goal, and develop plans towards reaching that goal.

This colorful map shows RDF/OWL resources for life sciences and health care. It is an attempt at finding a geographical metaphor for the complex interrelationships of entities on the Semantic Web. The map captures resources that are available on the web for importing or querying and that have received a minimum of attention by the Semantic Web Health Care and Life Science Interest Group. Connections are drawn only where different RDF/OWL resources actually share URIs — as you can see, there exist several RDF/OWL datasets in the same knowledge domain that are still unconnected. I drew this map in July 2007 for a poster I presented at the ISMB2007 conference. It does not claim to be complete.

At the moment I am (slowly) working on linking some of the datasets represented on this map to some resources of the Linked Open Data cloud. The first large segments of the Giant Global Graph (GGG) are starting to form!