DC Proposal: Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables

  • Varish Mulwad
Conference paper

DOI: 10.1007/978-3-642-25093-4_24

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7032)
Cite this paper as:
Mulwad V. (2011) DC Proposal: Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables. In: Aroyo L. et al. (eds) The Semantic Web – ISWC 2011. ISWC 2011. Lecture Notes in Computer Science, vol 7032. Springer, Berlin, Heidelberg


Vast amounts of information is encoded in tables found in documents, on the Web, and in spreadsheets or databases. Integrating or searching over this information benefits from understanding its intended meaning and making it explicit in a semantic representation language like RDF. Most current approaches to generating Semantic Web representations from tables requires human input to create schemas and often results in graphs that do not follow best practices for linked data. Evidence for a table’s meaning can be found in its column headers, cell values, implicit relations between columns, caption and surrounding text but also requires general and domain-specific background knowledge. 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 jointly 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. A table’s meaning is thus captured by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations between columns.


Linked Data Tables Entity Linking Machine Learning Graphical Models 

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Varish Mulwad
    • 1
  1. 1.Computer Science and Electrical EngineeringUniversity of MarylandUSA

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