OpenFact: Factuality Enhanced Open Knowledge Extraction
We focus on the property during the extraction of an OpenIE corpus named , which contains more than 12 million high-quality knowledge triplets. We break down the property into two important aspects— and —and we propose a comprehensive framework to handle both aspects. To enhance expressiveness, we f...
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Veröffentlicht in: | Transactions of the Association for Computational Linguistics 2023-06, Vol.11, p.686-702 |
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Hauptverfasser: | , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
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Zusammenfassung: | We focus on the
property during the extraction of an OpenIE corpus named
, which contains more than 12 million high-quality knowledge triplets. We break down the
property into two important aspects—
and
—and we propose a comprehensive framework to handle both aspects. To enhance expressiveness, we formulate each knowledge piece in OpenFact based on a semantic frame. We also design templates, extra constraints, and adopt human efforts so that most OpenFact triplets contain enough details. For groundedness, we require the main arguments of each triplet to contain linked Wikidata
entities. A human evaluation suggests that the OpenFact triplets are much more accurate and contain denser information compared to OPIEC-Linked (Gashteovski et al.,
), one recent high-quality OpenIE corpus grounded to Wikidata. Further experiments on knowledge base completion and knowledge base question answering show the effectiveness of OpenFact over OPIEC-Linked as supplementary knowledge to Wikidata as the major KG. |
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ISSN: | 2307-387X 2307-387X |
DOI: | 10.1162/tacl_a_00569 |