LODifier: Generating Linked Data from Unstructured Text

Presented at: 9th Extended Semantic Web Conference (ESWC2012)

by Isabelle Augenstein, Sebastian Padó, Sebastian Rudolph

The automated extraction of information from text and its transformation into a formal description is an important goal of in both Semantic Web research and computational linguistics. The extracted information can be used for a variety of tasks such as ontology generation, question answering and information retrieval. LODifier is an approach that combines deep semantic analysis with named entity recognition, word-sense disambiguation and controlled Semantic Web vocabularies in order to extract named entities and relations between them from text and to convert them into an RDF representation which is linked to DBpedia and WordNet. We present the architecture of our tool and discuss design decisions made. Evaluations of the tool give clear evidence of its potential for tasks like information extraction and computing document similarity.

Keywords: Deep Semantic Analysis, Linked Data, NLP, Named Entity Recognition, RDF, Relation Extraction

Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2012/paper/research/73

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