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
- Editors:
- Dmitry Ustalov, Swapna Somasundaran, Peter Jansen, Goran Glavaš, Martin Riedl, Mihai Surdeanu, Michalis Vazirgiannis
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 164–171
- Language:
- URL:
- https://aclanthology.org/D19-5320
- DOI:
- 10.18653/v1/D19-5320
- 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., TextGraphs 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D19-5320.pdf