Open-Domain Contextual Link Prediction andits Complementarity with Entailment Graphs

Mohammad Javad Hosseini, Shay B. Cohen, Mark Johnson, Mark Steedman

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

Abstract / Description of output

An open-domain knowledge graph (KG) has entities as nodes and natural language relations as edges, and is constructed by extracting (subject, relation, object) triples from text. The task of open-domain link prediction is to infer missing relations in the KG. Previous work has used standard link prediction for the task. Since triples are extracted from text, we can ground them in the larger textual context in which they were originally found. However, standard link prediction methods only rely on the KG structure and ignore the textual context that each triple was extracted from. In this paper, we introduce the new task of open-domain contextual link prediction which has access to both the textual context and the KG structure to perform link prediction. We build a dataset for the task and propose a model for it. Our experiments show that context is crucial in predicting missing relations. We also demonstrate the utility of contextual link prediction in discovering context-independent entailments between relations, in the form of entailment graphs (EG), in which the nodes are the relations. The reverse holds too: context-independent EGs assist in predicting relations in context.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Lingustics: EMNLP 2021
EditorsMarie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Place of PublicationStroudsburg, PA, United States
PublisherAssociation for Computational Linguistics (ACL)
Number of pages13
ISBN (Electronic)978-1-955917-10-0
Publication statusPublished - 7 Nov 2021
Event2021 Conference on Empirical Methods in Natural Language Processing - Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021


Conference2021 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2021
Country/TerritoryDominican Republic
CityPunta Cana
Internet address


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