Query-specific knowledge summarization with entity evolutionary networks

Carl Yang, Lingrui Gan, Zongyi Wang, Jiaming Shen, Jinfeng Xiao, Jiawei Han

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

Abstract

Given a query, unlike traditional IR that finds relevant documents or entities, in this work, we focus on retrieving both entities and their connections for insightful knowledge summarization. For example, given a query “computer vision” on a CS literature corpus, rather than returning a list of relevant entities like “cnn”, “imagenet” and “svm”, we are interested in the connections among them, and furthermore, the evolution patterns of such connections along particular ordinal dimensions such as time. Particularly, we hope to provide structural knowledge relevant to the query, such as “svm” is related to “imagenet” but not “cnn”. Moreover, we aim to model the changing trends of the connections, such as “cnn” becomes highly related to “imagenet” after 2010, which enables the tracking of knowledge evolutions. In this work, to facilitate such a novel insightful search system, we propose SetEvolve, which is a unified framework based on nonparanomal graphical models for evolutionary network construction from large text corpora. Systematic experiments on synthetic data and insightful case studies on real-world corpora demonstrate the utility of SetEvolve.

Original languageEnglish (US)
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2121-2124
Number of pages4
ISBN (Electronic)9781450369763
DOIs
StatePublished - Nov 3 2019
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: Nov 3 2019Nov 7 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
CountryChina
CityBeijing
Period11/3/1911/7/19

Keywords

  • Evolution analysis
  • Knowledge summaries
  • Network construction

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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  • Cite this

    Yang, C., Gan, L., Wang, Z., Shen, J., Xiao, J., & Han, J. (2019). Query-specific knowledge summarization with entity evolutionary networks. In CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2121-2124). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3357384.3358068