Tech Reports
Tech Report kmi-04-11 Abstract
Semi-Automatic Construction of Ontologies from Text
Techreport ID: kmi-04-11
Date: 2004
Author(s): David Celjuska
Supervisors: Maria Vargas-Vera, Jan Paralic
The Master's Thesis deals with semi-automatic construction of ontologies from text. While the core of the thesis was to develop an integrated system for ontology population with instances extracted from text, it also discusses and analyzes two major existing approaches in this area. The system is based on supervised learning and therefore learns extraction rules from annotated text and then applies those rules on new documents for the extraction. The important part of the entire cycle of ontology population is a user who accepts, rejects or modifies new extractions and suggested instances to be populated. An analysis of the possibility of automatically creation of new classes is discussed in turn.
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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