Proceedings of the 12th International Semantic Web Conference

Semantic Message Passing for Generating Linked Data from Tables

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We describe work on automatically inferring the intended meaning of tables and representing it as RDF linked data, making it available for improving search, interoperability and integration. We present implementation details of a joint inference module that uses knowledge from the linked open data (LOD) cloud to jointly infer the semantics of column headers, table cell values (e.g., strings and numbers) and relations between columns. The framework generates linked data by mapping column headers to classes, cell values to LOD entities (existing or new) and by identifying relations between columns. We also implement a novel Semantic Message Passing algorithm which uses LOD knowledge to improve existing message passing schemes. We evaluate our implemented techniques on tables from the Web and Wikipedia.


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learning, linked data, message passing, semantic web, tables

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Springer

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  1. Tables to Linked Data