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Modeling concepts and their relationships for corpus-based query auto-completion

Rossiello, Gaetano, Caputo, Annalina orcid logoORCID: 0000-0002-7144-8545, Basile, Pierpaolo orcid logoORCID: 0000-0002-0545-1105 and Semeraro, Giovanni orcid logoORCID: 0000-0001-6883-1853 (2019) Modeling concepts and their relationships for corpus-based query auto-completion. Open Computer Science, 9 . p. 212. ISSN 2299-1093

: Query auto-completion helps users to formulate their information needs by providing suggestion lists at every typed key. This task is commonly addressed by exploiting query logs and the approaches proposed in the literature fit well in web scale scenarios, where usually huge amountsofpastuserqueriescanbeanalyzedtoprovidereliablesuggestions.However,whenquerylogsarenotavailable, e.g. in enterprise or desktop search engines, these methods are not applicable at all. To face these challenging scenarios, we present a novel corpus-based approach which exploits the textual content of an indexed document collection in order to dynamically generate query completions. Our method extracts informative text fragments from the corpus and it combines them using a probabilistic graphical model in order to capture the relationships between the extracted concepts. Using this approach, it is possible to automatically complete partial queries with significant suggestions related to the keywords already entered by the user without requiring the analysis of the past queries. We evaluate our system through a user study on two different real-world document collections. The experiments show that our method is able to provide meaningful completions outperforming the state-of-the art approach
Item Type:Article (Published)
Uncontrolled Keywords:query auto-completion; information extraction; probabilistic graphical model
Subjects:Computer Science > Information retrieval
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Publisher:De Gruyter
Official URL:https://dx.doi.org/10.1515/comp-2019-0015
Copyright Information:© 2019 The Authors. Open Access (CC-BY 4.0)
Funders:Science Foundation Ireland Research Centres Programme (Grant SFI 13/RC/2106), European Regional Development Fund and by the EU2020-EuropeanUnions Horizon2020 under the Marie Skodowska-Curie grant agreement No.: EU2020-713567
ID Code:27594
Deposited On:22 Aug 2022 11:42 by Thomas Murtagh . Last Modified 22 Aug 2022 11:49

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