Extracting events from Wikipedia as RDF triples linked to widespread semantic web datasets
Many attempts have been made to extract structured data from Web resources, exposing them as RDF triples and interlinking them with other RDF datasets: in this way it is possible to create clouds of highly integrated Semantic Web data collections. In this paper we describe an approach to enhance the extraction of semantic contents from unstructured textual documents, in particular considering Wikipedia articles and focusing on event mining. Starting from the deep parsing of a set of English Wikipedia articles, we produce a semantic annotation compliant with the Knowledge Annotation Format (KAF). We extract events from the KAF semantic annotation and then we structure each event as a set of RDF triples linked to both DBpedia and WordNet. We point out examples of automatically mined events, providing some general evaluation of how our approach may discover new events and link them to existing contents.
Aliprandi, C. , Ronzano, F. , Marchetti, A. , Tesconi, M. , Minutoli, S. (2011)., Extracting events from Wikipedia as RDF triples linked to widespread semantic web datasets, in A. Ant Ozok & P. Zaphiris (eds.), Online communities and social computing, Dordrecht, Springer, pp. 90-99.
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