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Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction
Broscheit, Samuel
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Gashteovski, Kiril
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Wang, Yanjie
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Gemulla, Rainer
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Can We Predict New Facts with Open Knowledge Graph Embeddings A Benchmark for Open Link Prediction.pdf
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URL:
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https://madoc.bib.uni-mannheim.de/55724
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Additional URL:
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https://www.aclweb.org/anthology/2020.acl-main.209...
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URN:
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urn:nbn:de:bsz:180-madoc-557240
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Document Type:
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Conference or workshop publication
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Year of publication:
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2020
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Book title:
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ACL 2020 : the 58th Annual Meeting of the Association for Computational Linguistics, proceedings of the conference, ACL 2020The 58th Annual Meeting of theAssociation for Computational LinguisticsProceedings of the Conference, July 5 - 10, 2020
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Page range:
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2296-2308
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Conference title:
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ACL 2020
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Location of the conference venue:
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Online
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Date of the conference:
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05.-10.07.2020
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Publisher:
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Jurafsky, Dan
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Place of publication:
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Stroudsburg, PA
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Publishing house:
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Association for Computational Linguistics
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ISBN:
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978-1-952148-25-5
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Practical Computer Science I: Data Analytics (Gemulla 2014-)
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License:
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Creative Commons Attribution 4.0 International (CC BY 4.0)
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Subject:
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004 Computer science, internet
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Abstract:
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Open Information Extraction systems extract(“subject text”, “relation text”, “object text”)triples from raw text. Some triples are textualversions of facts, i.e., non-canonicalized men-tions of entities and relations. In this paper, weinvestigate whether it is possible to infernewfacts directly from theopen knowledge graphwithout any canonicalization or any supervi-sion from curated knowledge. For this pur-pose, we propose the open link prediction task,i.e., predicting test facts by completing(“sub-ject text”, “relation text”, ?)questions. Anevaluation in such a setup raises the question ifa correct prediction is actually anewfact thatwas induced by reasoning over the open knowl-edge graph or if it can be trivially explained.For example, facts can appear in different para-phrased textual variants, which can lead to testleakage. To this end, we propose an evaluationprotocol and a methodology for creating theopen link prediction benchmark OLPBENCH.We performed experiments with a prototypicalknowledge graph embedding model for openlink prediction. While the task is very chal-lenging, our results suggests that it is possibleto predict genuinely new facts, which can notbe trivially explained.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
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 Search Authors in
BASE:
Broscheit, Samuel
;
Gashteovski, Kiril
;
Wang, Yanjie
;
Gemulla, Rainer
Google Scholar:
Broscheit, Samuel
;
Gashteovski, Kiril
;
Wang, Yanjie
;
Gemulla, Rainer
ORCID:
Broscheit, Samuel, Gashteovski, Kiril, Wang, Yanjie and Gemulla, Rainer  ORCID: https://orcid.org/0000-0003-2762-0050
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