Debayan Mukherjee
ABSTRACT
Data in enterprises resides largely under relational schemas. Annotating such schemas with a knowledge graph (KG) that represents knowledge of the domain is useful for semantic understanding of the data as well as downstream processing by machines and humans. Existing approaches annotate only individual tables using small and simple KGs, and also fail to generalize to unseen KG entities and relationships during test. We propose a probabilistic model that generates complex relational schemas — tables, grouping of tables into neighborhoods, foreign key connections between tables and fields associated with tables — by traversing over paths in a knowledge graph. An efficient two-pass inference algorithm based on inverting this model jointly annotates schema elements such as fields, tables and neighborhoods with entities, and the associations between schema elements with relational paths in the KG. The algorithm also generalizes to unseen paths at test time. We show using experiments on a real-world schema and domain knowledge graph, in addition to benchmark datasets, that the proposed approach significantly out-performs existing approaches while demonstrating better scalability.
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Index Terms
- Semantic Annotation of Relational Schemas Using a Probabilistic Generative Model
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