Tech Reports
Tech Report kmi-04-18 Abstract
Semi-Automatic Population of Ontologies from Text
Techreport ID: kmi-04-18
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. The system is based on supervised learning and therefore learns extraction rules from annotated text and then applies those rules on newly documents for ontology population. It is based on three componentes: Marmot, a natural language processor; Crystal, a dictionary induction tool; and Badger, an information extraction tool. The important 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 documents is given in turn. The results cover the paper and support a presented hypothesis of assigning a rule confi-dence value to each extraction rule to improve 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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