Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction


Broscheit, Samuel ; Gashteovski, Kiril ; Wang, Yanjie ; Gemulla, Rainer


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URL: https://madoc.bib.uni-mannheim.de/55724
Additional URL: https://www.aclweb.org/anthology/2020.acl-main.209...
URN: urn:nbn:de:bsz:180-madoc-557240
Document Type: Conference or workshop publication
Year of publication: 2020
Book title: 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
Page range: 2296-2308
Conference title: ACL 2020
Location of the conference venue: Online
Date of the conference: 05.-10.07.2020
Author/Publisher of the book
(only the first ones mentioned)
:
Jurafsky, Dan
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics
ISBN: 978-1-952148-25-5
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik I (Gemulla 2014-)
License: CC BY 4.0
Subject: 004 Computer science, internet
Abstract: 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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Broscheit, Samuel ; Gashteovski, Kiril ; Wang, Yanjie ; Gemulla, Rainer ORCID: 0000-0003-2762-0050 Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction. Open Access Jurafsky, Dan 2296-2308 In: 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 (2020) Stroudsburg, PA ACL 2020 (Online) [Conference or workshop publication]
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