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This workshop is partially supported by EASTWEB

Outline:

We recently heard about a telecom project that required reasoning about 10 billion RDF triples in less than 100 ms. This use case was defined around generating revenue streams through new context-sensitive and personalized mobile services. This is just one example of a general demand for reasoning over extremely large scale semantic repositories.

The Web has made tremendous amounts of information available that should be processed based on formal semantics attached to it. The Semantic Web community has developed a number of languages (RDF, RDF Schema, OWL) that deploy logic for this purpose. Impressive progress has been made on scaleable storage, querying and inference for these languages, and they are succesfully being deployed on large intranets and medium-scale web-applications. However, it is unlikely that these approaches will scale to the amount of information and setting the Web is providing.

The main open question that will be discussed at the workshop is:

Some of the limiting assumptions underlying current reasoning languages for the Web are:

• sets of axioms and facts are restricted in size
• the axioms and facts are static and known in advance
• the inference process must be sound and complete

Each of these assumptions (and most likely others) needs to be revisited and adopted to the reality as it is provided by the Web.

Contributions form many very different scientific fields will be needed to establish the paradigmatic shift that is necessary to obtain this goal. The workshop aims to attract participants from scientific fields not typically seen at Semantic Web events:

• Economics can tell us about cost-benefit trade-off models, sunk-cost theory and the role of negotiation in obtaining near-optimal results under bounded resources
• Cognitive Science can tell us about strategies of human memory and human reasoning which is so succesful in reaching good enough conclusion in limited time-spans, using methods such as priming, attention scoping, recency-based self-optimising memory, etc.
• Computational Learning Theory has contributions to make with their notions of Probably Approximately Correct (PAC) computing, and strategies for abstraction and compression of information.
• Combinatorial Search has made recent breakthroughs in handling massive search-spaces with heuristics based on Monte Carlo simulations.

Background information:

Some of the ideas that sparked off this workshop can be found in: Unifying Reasoning and Search to Web Scale, Dieter Fensel and Frank van Harmelen, IEEE Internet Computing, March/April 2007 (Vol. 11, No. 2), pp. 94-96