Abstract
This paper presents a new method that aims at improving semantic indexing while reducing the number of indexing terms. Indexing terms are determined using a minimum redundancy cut in a hierarchy of conceptual hypernyms provided by an ontology (e.g. WordNet, EDR). The results of some information retrieval experiments carried out on several standard document collections using the EDR ontology are presented, illustrating the benefit of the method.
Keywords
- Information Retrieval
- Average Precision
- Mean Average Precision
- Ambiguous Word
- Indexing Term
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This work was partially supported by the Swiss National Fund for Scientific Research (SNFSR) under grant #200020–103529.
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Seydoux, F., Chappelier, JC. (2005). Minimum Redundancy Cut in Ontologies for Semantic Indexing. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_64
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DOI: https://doi.org/10.1007/11595014_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
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