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Article Dans Une Revue Lecture Notes in Computer Science Année : 2014

SemIndex: Semantic-Aware Inverted Index

Joe Tekli
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Kokou Yetongnon
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Carlos Raymundo Ibañez
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Yi Luo
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Marc Al Assad
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Résumé

This paper focuses on the important problem of semantic-aware search in textual (structured, semi-structured, NoSQL) databases. This problem has emerged as a required extension of the standard containment keyword based query to meet user needs in textual databases and IR applications. We provide here a new approach, called SemIndex, that extends the standard inverted index by constructing a tight coupling inverted index graph that combines two main resources: a general purpose semantic network, and a standard inverted index on a collection of textual data. We also provide an extended query model and related processing algorithms with the help of SemIndex. To investigate its effectiveness, we set up experiments to test the performance of SemIndex. Preliminary results have demonstrated the effectiveness, scalability and optimality of our approach.
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Dates et versions

hal-01090672 , version 1 (04-12-2014)

Identifiants

  • HAL Id : hal-01090672 , version 1

Citer

Joe Tekli, Kokou Yetongnon, Carlos Raymundo Ibañez, Yi Luo, Agma J. M. Traina, et al.. SemIndex: Semantic-Aware Inverted Index. Lecture Notes in Computer Science, 2014, pp.290-307. ⟨hal-01090672⟩
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