Query expansion methods have been studied for a long time - with debatable success in many instances. In this paper we present a probabilistic query expansion model based on a similarity thesaurus which was constructed automatically. A similarity thesaurus reflects domain knowledge about the particular collection from which it is constructed. We address the two important issues with query expansion: the selection and the weighting of additional search terms. In contrast to earlier methods, our queries are expanded by adding those terms that are most similar to the concept of the query, rather than selecting terms that are similar to the query terms. Our experiments show that this kind of query expansion results in a notable improvement in the retrieval effectiveness when measured using both recall-precision and usefulness.

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This paper presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge-based systems and to alleviate the limitations of the manual browsing approach, we have developed two spreading activation-based algo-rithms for concept exploration in large, heterogeneous net-works of concepts (e.g., multiple thesauri). One algorithm, which is based on the symbolic Al paradigm, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The second algorithm, which is based on the neural network approach, executes the Hopfield net parallel relaxation and convergence pro-cess to identify “convergent ” concepts for some initial queries (a parallel, heuristic search process). Both algo-rithms can be adopted for automatic, multiple-thesauri consultation. We tested these two algorithms on a large text-based knowledge network of about 13,000 nodes (terms) and 80,000 directed links in the area of computing technologies. This knowledge network was created from two external thesauri and one automatically generated thesaurus. We conducted experiments to compare the be-haviors and performances of the two algorithms with the hypertext-like browsing process. Our experiment revealed that manual browsing achieved higher-term recall but lower-term precision in comparison to the algorithmic sys-tems. However, it was also a much more laborious and cog-nitively demanding process. In document retrieval, there were no statistically significant differences in document re-call and precision between the algorithms and the manual browsing process. In light of the effort required by the man-ual browsing process, our proposed algorithmic approach presents a viable option for efficiently traversing large-scale, multiple thesauri (knowledge network). 1

Automatic query expansion has been known to be the most important method in overcoming the word mismatch problem in information retrieval. Thesauri have long been used by many researchers as a tool for query expansion. However only one type of thesaurus has generally been used. In this paper we analyze the characteristics of different thesaurus types and propose a method to combine them for query expansion. Experiments using the TREC collection proved the effectiveness of our method over those using one type of thesaurus.

ueries using all search facets identified from requests, low complexity was achieved by formulating queries with major facets only. Query expansion was based on a thesaurus, from which the expansion keys were elicited for queries. There were five expansion types: (1) the first query version was an unexpanded, original query with one search key for each search concept (original search concepts) elicited from the test thesaurus; (2) the synonyms of the original search keys were added to the original query; (3) search keys representing the narrower concepts of the original search concepts were added to the original query; (4) search keys representing the associative concepts of the original search concepts were added to the original query; (5) all previous expansion keys were cumulatively added to the original query. Query structure refers to the syntactic structure of a query expression, marked with query operators and parentheses. The structure of queries was either weak (queries with n

This paper describe our method in automatic-adhoc task of TREC-7. We propose a method to improve the performance of information retrieval system by expanded the query using 3 different types of thesaurus. The expansion terms are taken from handcrafted thesaurus (WordNet), co-occurrence-based automatically constructed thesaurus, and syntactically predicate-argument based automatically constructed thesaurus.

For the sake of national security, very large volumes of data and information are generated and gathered daily. Much of this data and information is written in different languages, stored in different locations, and may be seemingly unconnected. Crosslingual semantic interoperability is a major challenge to generate an overview of this disparate data and information so that it can be analyzed, shared, searched, and summarized. The recent terrorist attacks and the tragic events of September 11, 2001 have prompted increased attention on national security and criminal analysis. Many Asian countries and

This paper proposes the use of multiple thesaurus types for query expansion in information retrieval. Hand-crafted thesaurus, corpus-based co-occurrence-based thesaurus and syntactic-relation-based thesaurus are combined and used as a tool for query expansion. A simple word sense disambiguation is performed to avoid misleading expansion terms. Experiments using TREC-7 collection proved that this method could improve the information retrieval performance significantly. Failure analysis was done on the cases in which the proposed method fail to improve the retrieval effectiveness. We found that queries containing negative statements and multiple aspects might cause problems in the proposed method.

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This paperdesc0fl e our method inautomatic#fiB3 c task of TREC-7. We propose a method to improve the performanc of information retrieval system by expanded the query using 3di#ferent types of thesaurus. The expansion terms are taken from handcand -3 thesaurus (WordNet),cdN cNet),30fi2S234 automatic2S2 ctomatic2 thesaurus, and syntacfi88fl34 predicfl34fifl5flS3c t basedautomatic34fi ctomatic3 thesaurus. 1 Introduction A cSBflS34 problem in information retrieval is that the vocfi6fl2fi3 that thesearc hers use is not the same as the one bywhic h the doc300 ts have been indexed. The word synonymy is one example of this problem. If a user use a synonym of a wordwhic h docfi88 t has been indexed in his/her query, then that doc3fl8 tsc30B not be retrieved. Query expansion [ ] is one method in information retrieval to avoid this problem. The expansion termscm be taken from thesaurus [4, 9, 11]. There are manyresearc h of query expansion using thesaurus in the literature. Briefly there are two...