Abstract
Query expansion techniques are used to find the desired set of query terms to improve retrieval performance. One of the limitations with the query expansion techniques is that a query is often expanded only by the linguistic features of terms. This paper presents a novel semantic query expansion technique that combines association rules with ontologies and information retrieval techniques. We propose to use the association rule discovery to find good candidate terms to improve the retrieval performance. These candidate terms are automatically derived from collections and added to the original query. Our method is differentiated from others in that 1) it utilizes the semantics as well as linguistic properties of unstructured text corpus and 2) it makes use of contextual properties of important terms discovered by association rules. Experiments conducted on a subset of TREC collections give quite encouraging results. We achieve from 15.49% to 20.98% improvement in term of P@20 with TREC5 ad hoc queries.
| Original language | English |
|---|---|
| Pages (from-to) | 326-335 |
| Number of pages | 10 |
| Journal | Lecture Notes in Computer Science |
| Volume | 3589 |
| Publication status | Published - 2005 Oct 24 |
| Event | 7th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2005 - Copenhagen, Denmark Duration: 2005 Aug 22 → 2005 Aug 26 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
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Semantic query expansion combining association rules with ontologies and information retrieval techniques. / Song, Min; Song, Il Yeol; Hu, Xiaohua; Allen, Robert.
In: Lecture Notes in Computer Science, Vol. 3589, 24.10.2005, p. 326-335.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Semantic query expansion combining association rules with ontologies and information retrieval techniques
AU - Song, Min
AU - Song, Il Yeol
AU - Hu, Xiaohua
AU - Allen, Robert
PY - 2005/10/24
Y1 - 2005/10/24
N2 - Query expansion techniques are used to find the desired set of query terms to improve retrieval performance. One of the limitations with the query expansion techniques is that a query is often expanded only by the linguistic features of terms. This paper presents a novel semantic query expansion technique that combines association rules with ontologies and information retrieval techniques. We propose to use the association rule discovery to find good candidate terms to improve the retrieval performance. These candidate terms are automatically derived from collections and added to the original query. Our method is differentiated from others in that 1) it utilizes the semantics as well as linguistic properties of unstructured text corpus and 2) it makes use of contextual properties of important terms discovered by association rules. Experiments conducted on a subset of TREC collections give quite encouraging results. We achieve from 15.49% to 20.98% improvement in term of P@20 with TREC5 ad hoc queries.
AB - Query expansion techniques are used to find the desired set of query terms to improve retrieval performance. One of the limitations with the query expansion techniques is that a query is often expanded only by the linguistic features of terms. This paper presents a novel semantic query expansion technique that combines association rules with ontologies and information retrieval techniques. We propose to use the association rule discovery to find good candidate terms to improve the retrieval performance. These candidate terms are automatically derived from collections and added to the original query. Our method is differentiated from others in that 1) it utilizes the semantics as well as linguistic properties of unstructured text corpus and 2) it makes use of contextual properties of important terms discovered by association rules. Experiments conducted on a subset of TREC collections give quite encouraging results. We achieve from 15.49% to 20.98% improvement in term of P@20 with TREC5 ad hoc queries.
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M3 - Conference article
AN - SCOPUS:26844573379
VL - 3589
SP - 326
EP - 335
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -