Tables to Linked Data

September 1, 2010 - May 1, 2016

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.

entity recognition, learning, linked data, rdf, semantic web, tables

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Students

  1. Varish Mulwad

Refereed Publications

2015

  1. V. Mulwad, "TABEL - A Domain Independent and Extensible Framework for Inferring the Semantics of Tables", PhdThesis, University of Maryland, Baltimore County, January 2015, 1248 downloads.

2014

  1. V. Mulwad, T. Finin, and A. Joshi, "Interpreting Medical Tables as Linked Data to Generate Meta-Analysis Reports", InProceedings, Proceedings of the 15th IEEE International Conference on Information Reuse and Integration, August 2014, 624 downloads.

2013

  1. V. Mulwad, T. Finin, and A. Joshi, "Semantic Message Passing for Generating Linked Data from Tables", InProceedings, Proceedings of the 12th International Semantic Web Conference, October 2013, 1477 downloads.

2012

  1. V. Mulwad, T. Finin, and A. Joshi, "A Domain Independent Framework for Extracting Linked Semantic Data from Tables", InCollection, Search Computing - Broadening Web Search, July 2012, 1147 downloads.

2011

  1. V. Mulwad, T. Finin, and A. Joshi, "Automatically Generating Government Linked Data from Tables", InCollection, Working notes of AAAI Fall Symposium on Open Government Knowledge: AI Opportunities and Challenges, November 2011, 2389 downloads.
  2. V. Mulwad, "DC Proposal: Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables", InProceedings, Proceedings of Tenth International Semantic Web Conference, Part II, October 2011, 1676 downloads.
  3. V. Mulwad, T. Finin, and A. Joshi, "Generating Linked Data by Inferring the Semantics of Tables", InProceedings, Proceedings of the First International Workshop on Searching and Integrating New Web Data Sources, September 2011, 2169 downloads.

2010

  1. V. Mulwad, T. Finin, Z. Syed, and A. Joshi, "Using linked data to interpret tables", InProceedings, Proceedings of the the First International Workshop on Consuming Linked Data, November 2010, 2086 downloads, 7 citations.
  2. V. Mulwad, T. Finin, Z. Syed, and A. Joshi, "T2LD: Interpreting and Representing Tables as Linked Data", InProceedings, Proceedings of the Poster and Demonstration Session at the 9th International Semantic Web Conference, CEUR Workshop Proceedings, November 2010, 3440 downloads, 2 citations.
  3. V. Mulwad, "T2LD - An automatic framework for extracting, interpreting and representing tables as Linked Data", MastersThesis, UMBC, August 2010, 2510 downloads.
  4. Z. Syed, T. Finin, V. Mulwad, and A. Joshi, "Exploiting a Web of Semantic Data for Interpreting Tables", InProceedings, Proceedings of the Second Web Science Conference, April 2010, 3969 downloads, 6 citations.

2008

  1. L. Han, T. Finin, C. Parr, J. Sachs, and A. Joshi, "RDF123: from Spreadsheets to RDF", InProceedings, Seventh International Semantic Web Conference, October 2008, 4998 downloads, 26 citations.

2007

  1. C. Parr, J. Sachs, L. Han, and T. Wang, "RDF123 and Spotter: Tools for generating OWL and RDF for biodiversity data in spreadsheets and unstructured text", InProceedings, Proceedings of Biodiversity Information Standards Annual Conference (TDWG 2007), October 2007, 1613 downloads, 3 citations.

Non-Refereed Publications

2007

  1. L. Han, C. Parr, J. Sachs, A. Joshi, and T. Finin, "RDF123: a mechanism to transform spreadsheets to RDF", TechReport, University of Maryland, Baltimore County, August 2007, 7689 downloads, 9 citations.

Google group

  1. RDF123 Google Group