Tech Reports
Tech Report kmi-06-17 Abstract
Relation Extraction for Semantic Intranet Annotations
Techreport ID: kmi-06-17
Date: 2006
Author(s): Lucia Specia, Claudio Baldassarre, Enrico Motta
We present an approach for ontology driven extraction of relations from texts aimed mainly to produce enriched semantic annotations for the Semantic Web. The approach exploits linguistic and empirical strategies, by means of a pipeline method involving processes such as a parser, part-of-speech tagger, named entity recognition system, and pattern-based classification, and resources including ontology, knowledge and lexical databases. A preliminary evaluation with 25 sentences showed that the use of knowledge intensive resources and strategies together with corpus-based techniques to process the input data allows identifying and discovering relevant relations between known and new entity pairs mentioned in the text. Besides semantic web annotations, the system can be used for other tasks, including ontology population, since it identifies new instantiations of existent relations and entities, and ontology learning, since it discovers new relations, which are not part of the ontology.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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