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arXiv · 1911.00492

Reasoning Over Paths via Knowledge Base Completion

Saatviga Sudhahar·Ian Roberts·Andrea Pierleoni
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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 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.

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Saatviga Sudhahar, Ian Roberts, Andrea Pierleoni. 2019-11-01. Reasoning Over Paths via Knowledge Base Completion. https://arxiv.org/abs/1911.00492

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