Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols

Prachi Jain, Sushant Rathi, Mausam, Soumen Chakrabarti


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.
Anthology ID:
2020.emnlp-main.305
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3733–3747
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.305
DOI:
10.18653/v1/2020.emnlp-main.305
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.305.pdf