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 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.
@article{arxiv.1911.00492,
title = {Reasoning Over Paths via Knowledge Base Completion},
author = {Saatviga Sudhahar and Ian Roberts and Andrea Pierleoni},
journal= {arXiv preprint arXiv:1911.00492},
year = {2019}
}
Comments
Submitted at the TextGraphs2019 Workshop at EMNLP 2019 Conference