Swanson linking revisited: Accelerating literature-based discovery across domains using a conceptual influence graph

Gus Hahn-Powell, Marco Valenzuela-Escárcega, Mihai Surdeanu

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

7 Scopus citations

Abstract

We introduce a modular approach for literature-based discovery consisting of a machine reading and knowledge assembly component that together produce a graph of influence relations (e.g., “A promotes B”) from a collection of publications. A search engine is used to explore direct and indirect influence chains. Query results are substantiated with textual evidence, ranked according to their relevance, and presented in both a table-based view, as well as a network graph visualization. Our approach operates in both domain-specific settings, where there are knowledge bases and ontologies available to guide reading, and in multi-domain settings where such resources are absent. We demonstrate that this deep reading and search system reduces the effort needed to uncover “undiscovered public knowledge”, and that with the aid of this tool a domain expert was able to drastically reduce her model building time from months to two days.

Original languageEnglish (US)
Title of host publicationACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations
PublisherAssociation for Computational Linguistics (ACL)
Pages103-108
Number of pages6
ISBN (Print)9781945626715
DOIs
StatePublished - 2017
Event55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, Canada
Duration: Jul 30 2017Aug 4 2017

Publication series

NameACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations

Other

Other55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
Country/TerritoryCanada
CityVancouver
Period7/30/178/4/17

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence
  • Software

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