Jain, Prachi ; Rathi, Sushant ; Mausam, ; Chakrabarti, Soumen (2020) Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).
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Official URL: http://doi.org/10.18653/v1/2020.emnlp-main.305
Related URL: http://dx.doi.org/10.18653/v1/2020.emnlp-main.305
Abstract
Research on temporal knowledge bases, which associate a relational fact (s,r,o) with a validity time period (or time instant), is in its early days. Our work considers predicting missing entities (link prediction) and missing time intervals (time prediction) as joint Temporal Knowledge Base Completion (TKBC) tasks, and presents TIMEPLEX, a novel TKBC method, in which entities, relations and, time are all embedded in a uniform, compatible space. TIMEPLEX exploits the recurrent nature of some facts/events and temporal interactions between pairs of relations, yielding state-of-the-art results on both prediction tasks. We also find that existing TKBC models heavily overestimate link prediction performance due to imperfect evaluation mechanisms. In response, we propose improved TKBC evaluation protocols for both link and time prediction tasks, dealing with subtle issues that arise from the partial overlap of time intervals in gold instances and system predictions.
Item Type: | Conference or Workshop Item (Paper) |
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Source: | Copyright of this article belongs to Association for Computational Linguistics |
ID Code: | 130887 |
Deposited On: | 01 Dec 2022 06:03 |
Last Modified: | 27 Jan 2023 09:34 |
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