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Abstract:
More and more semantic information has become available as RDF data recently,
with the linked open data cloud as a prominent example. However, participating
in the Semantic Web is cumbersome. Typically several steps are involved in
using semantic knowledge. Information is first acquired, e.g. by information
extraction, crowd sourcing or human experts. Then ontologies are published and
distributed. Users may apply reasoning and otherwise modify their local
ontology instances.
However, currently these steps are treated separately and although each
involves human effort, nearly no synergy effect is used and it is also mostly a
one way process, e.g. user feedback hardly flows back into the main ontology
version. Similarly, user cooperation is low.
While there are approaches alleviating some of these limitations,
e.g. extracting information at query time, personalizing queries, and
integration of user feedback, this work combines all the pieces envisioning a
social knowledge network that enables collaborative knowledge generation and
exchange. Each aforementioned step is seen as a particular implementation
of a network node responding to knowledge queries in its own way, e.g. by
extracting it, applying reasoning or asking users,
and learning from knowledge exchanged with neighbours.
Original knowledge as well as user feedback is distributed over the network
based on similar trust and provenance mechanisms.
The extended query language we call for also allows for
personalization.
