Query generation as result aggregation for knowledge representation

Matthew Mitsui, Chirag Shah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Knowledge representations have greatly enhanced the fundamental human problem of information search, profoundly changing representations of queries and database information for various retrieval tasks. Despite new technologies, little thought has been given in the field of query recommendation - recommending keyword queries to end users - to a holistic approach that recommends constructed queries from relevant snippets of information; pre-existing queries are used instead. Can we instead determine relevant information a user should see and aggregate it into a query? We construct a general framework leveraging various retrieval architectures to aggregate relevant information into a natural language query for recommendation. We test this framework in text retrieval, aggregating text snippets and comparing output queries to user generated queries. We show that an algorithm can generate queries more closely resembling the original and give effective retrieval results. Our simple approach shows promise for also leveraging knowledge structures to generate effective query recommendations.

Original languageEnglish (US)
Title of host publicationProceedings of the 50th Annual Hawaii International Conference on System Sciences, HICSS 2017
EditorsTung X. Bui, Ralph Sprague
PublisherIEEE Computer Society
Pages4365-4374
Number of pages10
ISBN (Electronic)9780998133102
StatePublished - 2017
Externally publishedYes
Event50th Annual Hawaii International Conference on System Sciences, HICSS 2017 - Big Island, United States
Duration: Jan 3 2017Jan 7 2017

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2017-January
ISSN (Print)1530-1605

Conference

Conference50th Annual Hawaii International Conference on System Sciences, HICSS 2017
Country/TerritoryUnited States
CityBig Island
Period1/3/171/7/17

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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