Abstract
Textual information is becoming increasingly available in electronic forms. Users need tools to sift through non-relevant information and retrieve only those pieces relevant to their needs. The traditional methods such as Boolean operators and key terms have somehow reached thek limitations. An emerging trend is to combine the traditional information retrieval and artificial intelligence techniques. This paper explores the possibility of extending traditional information retrieval systems with knowledge-based approaches to automatically expand natural language queries. Two types of knowledge-bases, a domain-specific and a general world knowledge, are used in the expansion process. Experiments are also conducted using different search strategies and various combinations of the knowledge-bases. Our results show that an increase in retrieval performance can be obtained using certain knowledge-based approaches.
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© 1996 Springer-Verlag Berlin Heidelberg
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Bodner, R.C., Song, F. (1996). Knowledge-based approaches to query expansion in information retrieval. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_48
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DOI: https://doi.org/10.1007/3-540-61291-2_48
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