Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs Article Swipe
Mohammad Javad Hosseini
,
Shay B. Cohen
,
Mark Johnson
,
Mark Steedman
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2021.findings-emnlp.238
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2021.findings-emnlp.238
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.
Related Topics
Concepts
Computer science
Link (geometry)
Logical consequence
Complementarity (molecular biology)
Context (archaeology)
Natural language processing
Artificial intelligence
Domain (mathematical analysis)
Task (project management)
Textual entailment
Extension (predicate logic)
Relation (database)
Theoretical computer science
Data mining
Mathematics
Programming language
Management
Genetics
Paleontology
Computer network
Economics
Biology
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2021.findings-emnlp.238
- https://aclanthology.org/2021.findings-emnlp.238.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3212160013
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3212160013Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18653/v1/2021.findings-emnlp.238Digital Object Identifier
- Title
-
Open-Domain Contextual Link Prediction and its Complementarity with Entailment GraphsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Mohammad Javad Hosseini, Shay B. Cohen, Mark Johnson, Mark SteedmanList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2021.findings-emnlp.238Publisher landing page
- PDF URL
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https://aclanthology.org/2021.findings-emnlp.238.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://aclanthology.org/2021.findings-emnlp.238.pdfDirect OA link when available
- Concepts
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Computer science, Link (geometry), Logical consequence, Complementarity (molecular biology), Context (archaeology), Natural language processing, Artificial intelligence, Domain (mathematical analysis), Task (project management), Textual entailment, Extension (predicate logic), Relation (database), Theoretical computer science, Data mining, Mathematics, Programming language, Management, Genetics, Paleontology, Computer network, Economics, Biology, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 5, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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