Reasoning Over Paths via Knowledge Base Completion

Saatviga Sudhahar, Andrea Pierleoni, Ian Roberts


Abstract
Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60% of the time within the top 10 ranked paths and achieve 49% mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used.
Anthology ID:
D19-5320
Volume:
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | TextGraphs | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
164–171
Language:
URL:
https://aclanthology.org/D19-5320
DOI:
10.18653/v1/D19-5320
Bibkey:
Cite (ACL):
Saatviga Sudhahar, Andrea Pierleoni, and Ian Roberts. 2019. Reasoning Over Paths via Knowledge Base Completion. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 164–171, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
Reasoning Over Paths via Knowledge Base Completion (Sudhahar et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5320.pdf