Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding

Thomas Gebhart, Jakob Hansen, Paul Schrater

Research output: Contribution to journalConference articlepeer-review


Knowledge graph embedding involves learning representations of entities-the vertices of the graph-and relations-the edges of the graph-such that the resulting representations encode the known factual information represented by the knowledge graph and can be used in the inference of new relations. We show that knowledge graph embedding is naturally expressed in the topological and categorical language of cellular sheaves: a knowledge graph embedding can be described as an approximate global section of an appropriate knowledge sheaf over the graph, with consistency constraints induced by the knowledge graph's schema. This approach provides a generalized framework for reasoning about knowledge graph embedding models and allows for the expression of a wide range of prior constraints on embeddings. Further, the resulting embeddings can be easily adapted for reasoning over composite relations without special training. We implement these ideas to highlight the benefits of the extensions inspired by this new perspective.

Original languageEnglish (US)
Pages (from-to)9094-9116
Number of pages23
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event26th International Conference on Artificial Intelligence and Statistics, AISTATS 2023 - Valencia, Spain
Duration: Apr 25 2023Apr 27 2023

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Copyright © 2023 by the author(s)


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