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"A context-driven subgraph model for literature-based discovery" by Delroy Cameron with Prateek Jain as coordinator

Published: 19 February 2015 Publication History

Abstract

Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature using the LBD paradigm, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. Rather, they only allude to the existence of meaningful underlying associations. To gain in-depth insights into the meaning of hidden (and other) connections, complementary methods have often been employed. Some of these methods include: 1) the use of domain expertise for concept filtering and knowledge exploration, 2) leveraging structured background knowledge for context and to supplement concept filtering, and 3) developing heuristics a priori to help eliminate spurious connections.

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  • (2018)SemaTyP: a knowledge graph based literature mining method for drug discoveryBMC Bioinformatics10.1186/s12859-018-2167-519:1Online publication date: 30-May-2018
  • (2017)Emerging approaches in literature-based discovery: techniques and performance reviewThe Knowledge Engineering Review10.1017/S026988891700004232Online publication date: 16-May-2017

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Published In

cover image ACM SIGWEB Newsletter
ACM SIGWEB Newsletter  Volume 2015, Issue Winter
Winter 2015
37 pages
ISSN:1931-1745
EISSN:1931-1435
DOI:10.1145/2719943
Issue’s Table of Contents
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 February 2015
Published in SIGWEB Volume 2015, Issue Winter

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Cited By

View all
  • (2018)SemaTyP: a knowledge graph based literature mining method for drug discoveryBMC Bioinformatics10.1186/s12859-018-2167-519:1Online publication date: 30-May-2018
  • (2017)Emerging approaches in literature-based discovery: techniques and performance reviewThe Knowledge Engineering Review10.1017/S026988891700004232Online publication date: 16-May-2017

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