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J-REED: Joint Relation Extraction and Entity Disambiguation

Published: 06 November 2017 Publication History

Abstract

Information extraction (IE) from text sources can either be performed as Model-based IE (i.e, by using a pre-specified domain of target entities and relations) or as Open IE (i.e., with no particular assumptions about the target domain). While Model-based IE has limited coverage, Open IE merely yields triples of surface phrases which are usually not disambiguated into a canonical set of entities and relations. This paper presents J-REED: a joint approach for entity disambiguation and relation extraction that is based on probabilistic graphical models. J-REED merges ideas from both Model-based and Open IE by mapping surface names to a background knowledge base, and by making surface relations as crisp as possible.

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  • (2020)Linking chemical and disease entities to ontologies by integrating PageRank with extracted relations from literatureJournal of Cheminformatics10.1186/s13321-020-00461-412:1Online publication date: 21-Sep-2020
  • (2020)Joint Entity Linking and Relation Extraction with Neural Networks for Knowledge Base Population2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207021(1-8)Online publication date: Jul-2020
  • (2019)FarsBaseSemantic Web10.3233/SW-19036910:6(1169-1196)Online publication date: 1-Jan-2019
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    cover image ACM Conferences
    CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
    November 2017
    2604 pages
    ISBN:9781450349185
    DOI:10.1145/3132847
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 06 November 2017

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    Author Tags

    1. entity disambiguation
    2. joint inference
    3. open relation extraction

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    Cited By

    View all
    • (2020)Linking chemical and disease entities to ontologies by integrating PageRank with extracted relations from literatureJournal of Cheminformatics10.1186/s13321-020-00461-412:1Online publication date: 21-Sep-2020
    • (2020)Joint Entity Linking and Relation Extraction with Neural Networks for Knowledge Base Population2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207021(1-8)Online publication date: Jul-2020
    • (2019)FarsBaseSemantic Web10.3233/SW-19036910:6(1169-1196)Online publication date: 1-Jan-2019
    • (2019)From Big Data to Big KnowledgeSOFSEM 2019: Theory and Practice of Computer Science10.1007/978-3-030-10801-4_5(50-53)Online publication date: 11-Jan-2019

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