Fact Discovery from Knowledge Base via Facet Decomposition

Zihao Fu, Yankai Lin, Zhiyuan Liu, Wai Lam


Abstract
During the past few decades, knowledge bases (KBs) have experienced rapid growth. Nevertheless, most KBs still suffer from serious incompletion. Researchers proposed many tasks such as knowledge base completion and relation prediction to help build the representation of KBs. However, there are some issues unsettled towards enriching the KBs. Knowledge base completion and relation prediction assume that we know two elements of the fact triples and we are going to predict the missing one. This assumption is too restricted in practice and prevents it from discovering new facts directly. To address this issue, we propose a new task, namely, fact discovery from knowledge base. This task only requires that we know the head entity and the goal is to discover facts associated with the head entity. To tackle this new problem, we propose a novel framework that decomposes the discovery problem into several facet discovery components. We also propose a novel auto-encoder based facet component to estimate some facets of the fact. Besides, we propose a feedback learning component to share the information between each facet. We evaluate our framework using a benchmark dataset and the experimental results show that our framework achieves promising results. We also conduct an extensive analysis of our framework in discovering different kinds of facts. The source code of this paper can be obtained from https://github.com/thunlp/FFD.
Anthology ID:
N19-1297
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2892–2901
Language:
URL:
https://aclanthology.org/N19-1297
DOI:
10.18653/v1/N19-1297
Bibkey:
Cite (ACL):
Zihao Fu, Yankai Lin, Zhiyuan Liu, and Wai Lam. 2019. Fact Discovery from Knowledge Base via Facet Decomposition. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2892–2901, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Fact Discovery from Knowledge Base via Facet Decomposition (Fu et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1297.pdf
Code
 thunlp/FFD