Tech Reports
Tech Report kmi-04-19 Abstract
Ontosophie: A Semi-Automatic System for Ontology Population from Text
Techreport ID: kmi-04-19
Date: 2004
Author(s): David Celjuska, Maria Vargas-Vera
This paper describes a system for semi-automatic population of ontologies with instances from unstructured text. It is based on supervised learning, learns extraction rules from annotated text and then applies those rules on new articles for ontology population. Therefore, the system classifies stories and populates a hand-crafted ontology with new instances of classes defined in it. It is based on three components: Marmot - a natural language processor; Crystal - a dictionary induction tool; and Badger - an information extraction tool. A part of the entire cycle is a user who accepts, rejects or modifies newly extracted and suggested instances to be populated. A description of experiments performed with text corpus consisting of 91 articles is given in turn. The results cover the paper and support a presented hypothesis of assigning a rule confidence value to each extraction rule for improving the performance.
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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