A quantitative evaluation of the enhanced topic-based vector space model

  • This contribution presents a quantitative evaluation procedure for Information Retrieval models and the results of this procedure applied on the enhanced Topic-based Vector Space Model (eTVSM). Since the eTVSM is an ontology-based model, its effectiveness heavily depends on the quality of the underlaying ontology. Therefore the model has been tested with different ontologies to evaluate the impact of those ontologies on the effectiveness of the eTVSM. On the highest level of abstraction, the following results have been observed during our evaluation: First, the theoretically deduced statement that the eTVSM has a similar effecitivity like the classic Vector Space Model if a trivial ontology (every term is a concept and it is independet of any other concepts) is used has been approved. Second, we were able to show that the effectiveness of the eTVSM raises if an ontology is used which is only able to resolve synonyms. We were able to derive such kind of ontology automatically from the WordNet ontology. Third, we observed that more poweThis contribution presents a quantitative evaluation procedure for Information Retrieval models and the results of this procedure applied on the enhanced Topic-based Vector Space Model (eTVSM). Since the eTVSM is an ontology-based model, its effectiveness heavily depends on the quality of the underlaying ontology. Therefore the model has been tested with different ontologies to evaluate the impact of those ontologies on the effectiveness of the eTVSM. On the highest level of abstraction, the following results have been observed during our evaluation: First, the theoretically deduced statement that the eTVSM has a similar effecitivity like the classic Vector Space Model if a trivial ontology (every term is a concept and it is independet of any other concepts) is used has been approved. Second, we were able to show that the effectiveness of the eTVSM raises if an ontology is used which is only able to resolve synonyms. We were able to derive such kind of ontology automatically from the WordNet ontology. Third, we observed that more powerful ontologies automatically derived from the WordNet, dramatically dropped the effectiveness of the eTVSM model even clearly below the effectiveness level of the Vector Space Model. Fourth, we were able to show that a manually created and optimized ontology is able to raise the effectiveness of the eTVSM to a level which is clearly above the best effectiveness levels we have found in the literature for the Latent Semantic Index model with compareable document sets.show moreshow less

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Metadaten
Author:Artem Polyvyanyy, Dominik Kuropka
URN:urn:nbn:de:kobv:517-opus-33816
Series (Serial Number):Technische Berichte des Hasso-Plattner-Instituts für Softwaresystemtechnik an der Universität Potsdam (19)
Document Type:Book
Language:English
Date of Publication (online):2009/08/11
Year of Completion:2007
Publishing Institution:Universität Potsdam
Release Date:2009/08/11
RVK - Regensburg Classification:ST 230
Organizational units:An-Institute / Hasso-Plattner-Institut für Softwaresystemtechnik GMBH
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):License LogoKeine Nutzungslizenz vergeben - es gilt das deutsche Urheberrecht
Notes extern:In Printform erschienen im Universitätsverlag Potsdam:

Polyvyanyy, Artem:
A quantitative evaluation of the enhanced topic-based vector space model / Artem Polyvyanyy, Dominik Kuropka. - Potsdam : Universitätsverlag Potsdam, 2007. - 88 S. - graph. Darst.
(Technische Berichte des Hasso-Plattner-Instituts für Softwaresystemtechnik an der Universität Potsdam ; 19)
ISSN 1613-5652
ISBN 978-3-939469-95-7
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