UMBC ebiquity

Automatically Generating Linked Data from Tables

Description: Evidence for a table’s meaning can be found in its metadata but currently requires human interpretation. We describe techniques grounded in graphical models and probabilistic reasoning to infer meaning associated with a table. Using background knowledge from the Linked Open Data cloud, we automatically infer the semantics of column headers, table cell values (e.g., strings and numbers) and relations between columns and represent the inferred meaning as graph of RDF triples.

Type: Presentation

Authors: Varish Mulwad

Date: November 15, 2011

Tags: semantic web, learning

Format: Microsoft PowerPoint (Need a reader? Get one here)

Number of downloads: 517

Access Control: Publicly Available


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size: 2507147 bytes

Related Projects:

Past Project

 Tables to Linked Data.



  1. (Resource) Automatically Generating Linked Data from Tables is the PowerPoint slides of (Event) Semantic Web Meetup