Learning to query: Focused web page harvesting for entity aspects

Yuan Fang, Vincent W. Zheng, Kevin Chen Chuan Chang

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

Abstract

As the Web hosts rich information about real-world entities, our information quests become increasingly entity centric. In this paper, we study the problem of focused harvesting of Web pages for entity aspects, to support downstream applications such as business analytics and building a vertical portal. Given that search engines are the de facto gateways to assess information on the Web, we recognize the essence of our problem as Learning to Query (L2Q)-to intelligently select queries so that we can harvest pages, via a search engine, focused on an entity aspect of interest. Thus, it is crucial to quantify the utilities of the candidate queries w.r.t. some entity aspect. In order to better estimate the utilities, we identify two opportunities and address their challenges. First, a target entity in a given domain has many peers. We leverage these peer entities to become domain aware. Second, a candidate query may overlap with the past queries that have already been fired. We account for these past queries to become context aware. Empirical results show that our approach significantly outperforms both algorithmic and manual baselines by 16% and 10% in F-scores, respectively.

Original languageEnglish (US)
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1002-1013
Number of pages12
ISBN (Electronic)9781509020195
DOIs
StatePublished - Jun 22 2016
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: May 16 2016May 20 2016

Publication series

Name2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016

Other

Other32nd IEEE International Conference on Data Engineering, ICDE 2016
CountryFinland
CityHelsinki
Period5/16/165/20/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Fang, Y., Zheng, V. W., & Chang, K. C. C. (2016). Learning to query: Focused web page harvesting for entity aspects. In 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 (pp. 1002-1013). [7498308] (2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2016.7498308