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        <title>Scalable Knowledge Graph Construction from Text Collections</title>
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    <name type="personal">
        <namePart type="given">Ryan</namePart>
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        <namePart type="family">Ilyas</namePart>
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            <namePart type="given">James</namePart>
            <namePart type="family">Thorne</namePart>
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        <name type="personal">
            <namePart type="given">Andreas</namePart>
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    <abstract>We present a scalable, open-source platform that “distills” a potentially large text collection into a knowledge graph. Our platform takes documents stored in Apache Solr and scales out the Stanford CoreNLP toolkit via Apache Spark integration to extract mentions and relations that are then ingested into the Neo4j graph database. The raw knowledge graph is then enriched with facts extracted from an external knowledge graph. The complete product can be manipulated by various applications using Neo4j’s native Cypher query language: We present a subgraph-matching approach to align extracted relations with external facts and show that fact verification, locating textual support for asserted facts, detecting inconsistent and missing facts, and extracting distantly-supervised training data can all be performed within the same framework.</abstract>
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%0 Conference Proceedings
%T Scalable Knowledge Graph Construction from Text Collections
%A Clancy, Ryan
%A Ilyas, Ihab F.
%A Lin, Jimmy
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F clancy-etal-2019-scalable
%X We present a scalable, open-source platform that “distills” a potentially large text collection into a knowledge graph. Our platform takes documents stored in Apache Solr and scales out the Stanford CoreNLP toolkit via Apache Spark integration to extract mentions and relations that are then ingested into the Neo4j graph database. The raw knowledge graph is then enriched with facts extracted from an external knowledge graph. The complete product can be manipulated by various applications using Neo4j’s native Cypher query language: We present a subgraph-matching approach to align extracted relations with external facts and show that fact verification, locating textual support for asserted facts, detecting inconsistent and missing facts, and extracting distantly-supervised training data can all be performed within the same framework.
%R 10.18653/v1/D19-6607
%U https://aclanthology.org/D19-6607
%U https://doi.org/10.18653/v1/D19-6607
%P 39-46

Markdown (Informal)

[Scalable Knowledge Graph Construction from Text Collections](https://aclanthology.org/D19-6607) (Clancy et al., 2019)

• Scalable Knowledge Graph Construction from Text Collections (Clancy et al., 2019)

ACL

• Ryan Clancy, Ihab F. Ilyas, and Jimmy Lin. 2019. Scalable Knowledge Graph Construction from Text Collections. In Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), pages 39–46, Hong Kong, China. Association for Computational Linguistics.