Streaming Knowledge Bases
Wednesday, August 29, 2007, 9:00am - Wednesday, August 29, 2007, 11:00am
With the emergence of Semantic Web, we now have a universal medium for data, information and knowledge exchange. RDF graphs are used to denote relation and interaction between different entities or resources. Some popular and uniform data interchange formats are developed to support RDF graphs. Knowledge extraction in Semantic Web is carrying out inferencing on such RDF graphs. Existing tools like JENA, Sesame are used for this task.
As Semantic Web continues to grow, more and more data will be expressed in uniform formats recommended by Semantic Web, such as RDF/XML or n-triples. In a pervasive environment, performing reasoning on this streaming data becomes a challenging task. Existing reasoners use techniques that load the whole RDF graph in main memory and carry out queries on it. This approach is of little use in real-time reasoning for streaming scenarios and takes considerable amount of time.
We combine the continuous query processors with Semantic Web techniques to build an "rdfs:subClassOf" reasoner that can deal with streaming data. Given an ontology, we pre-compute the transitive closure of all classes on "rdfs:subClassOf" relationship and store the class-subclass relationships in a database table. At run-time we just need to query the database to identify subclass events of the event of concern. There are already many applications which describe data in RDF compatible formats. We feed streams of such RDF data to our query processor and carry out real-time rdfs:subClassOf reasoning on them.