Ryan Wisnesky | How To Optimally Merge Knowledge Graphs With Category Theory
KGC 2021
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19m
In this talk we describe a new technique for merging knowledge graphs: translating the knowledge graph schemas into categories and the knowledge graph data into functors, then applying the "co-limit/pushout" construction from a branch of mathematics called category theory to merge these categories and functors, and then converting the categories and functors back into knowledge graph schemas and data. We show how this process is mathematically optimal (results in the highest possible data quality in the merge), and describe several real-world use cases of knowledge graph merge that have been implemented in an open-source tool.
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