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        <title>Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols</title>
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        <namePart type="given">Prachi</namePart>
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        <namePart type="given">Sushant</namePart>
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        <namePart>Mausam</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
%A Jain, Prachi
%A Rathi, Sushant
%A Chakrabarti, Soumen
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%A Mausam
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F jain-etal-2020-temporal
%X 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.
%R 10.18653/v1/2020.emnlp-main.305
%U https://aclanthology.org/2020.emnlp-main.305
%U https://doi.org/10.18653/v1/2020.emnlp-main.305
%P 3733-3747

Markdown (Informal)

[Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols](https://aclanthology.org/2020.emnlp-main.305) (Jain et al., EMNLP 2020)

• Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols (Jain et al., EMNLP 2020)

ACL

• Prachi Jain, Sushant Rathi, Mausam, and Soumen Chakrabarti. 2020. Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3733–3747, Online. Association for Computational Linguistics.