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Hyte: Hyperplane-based temporally aware knowledge graph embedding

Dasgupta, SS and Ray, SN and Talukdar, P (2020) Hyte: Hyperplane-based temporally aware knowledge graph embedding. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 31 Oct-4 Nov 2018, Brussels, Belgium, pp. 2001-2011.

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Official URL: https://doi.org/10.18653/v1/D18-1225


Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods. Relational facts in KG often show temporal dynamics, e.g., the fact (Cristiano Ronaldo, playsFor, Manchester United) is valid only from 2003 to 2009. Most of the existing KG embedding methods ignore this temporal dimension while learning embeddings of the KG elements. In this paper, we propose HyTE, a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. HyTE not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations. Through extensive experimentation on temporal datasets extracted from real-world KGs, we demonstrate the effectiveness of our model over both traditional as well as temporal KG embedding methods. © 2018 Association for Computational Linguistics

Item Type: Conference Paper
Publication: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Publisher: Association for Computational Linguistics
Additional Information: The copyright of this article belongs to Association for Computational Linguistics
Keywords: Geometry; Natural language processing systems, Active area; Embedding method; Knowledge graphs; Manchester; Real-world; Temporal dimensions; Temporal dynamics; Time-stamp, Embeddings
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 05 Apr 2021 06:19
Last Modified: 05 Apr 2021 06:19
URI: http://eprints.iisc.ac.in/id/eprint/64979

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