UMBC ebiquity

Tables to Linked Data

Status: Active project

Project Description:
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.

Start Date: September 2010

End Date: May 2016

Tim Finin
Anupam Joshi

Varish Mulwad

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


There are 12 associated publications:  Hide the list...

11 Refereed Publications


1. Varish Mulwad et al., "Semantic Message Passing for Generating Linked Data from Tables", InProceedings, Proceedings of the 12th International Semantic Web Conference, October 2013, 760 downloads.


2. Varish Mulwad et al., "A Domain Independent Framework for Extracting Linked Semantic Data from Tables", InCollection, Search Computing - Broadening Web Search, July 2012, 642 downloads.


3. Varish Mulwad et al., "Automatically Generating Government Linked Data from Tables", InCollection, Working notes of AAAI Fall Symposium on Open Government Knowledge: AI Opportunities and Challenges, November 2011, 1132 downloads.

4. Varish 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, 905 downloads.

5. Varish Mulwad et al., "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, 1019 downloads.


6. Varish Mulwad et al., "Using linked data to interpret tables", InProceedings, Proceedings of the the First International Workshop on Consuming Linked Data, November 2010, 1250 downloads.

7. Varish Mulwad et al., "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, 1795 downloads.

8. Varish Mulwad, "T2LD - An automatic framework for extracting, interpreting and representing tables as Linked Data", MastersThesis, UMBC, August 2010, 1468 downloads.

9. Zareen Syed et al., "Exploiting a Web of Semantic Data for Interpreting Tables", InProceedings, Proceedings of the Second Web Science Conference, April 2010, 2650 downloads.


10. Lushan Han et al., "RDF123: from Spreadsheets to RDF", InProceedings, Seventh International Semantic Web Conference, October 2008, 3611 downloads.


11. Cynthia Parr et al., "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, 1249 downloads.

1 Non-Refereed Publication


1. Lushan Han et al., "RDF123: a mechanism to transform spreadsheets to RDF", TechReport, University of Maryland, Baltimore County, August 2007, 5760 downloads.


There are 13 associated resources:  Hide the list...

1. Automatically Generating Linked Data from Tables, Presentation.

2. Generating Linked Data by inferring the semantics of tables , Poster.

3. Generating Linked Data by inferring the semantics of tables, Poster.

4. Generating Linked Data by inferring the semantics of tables, Presentation.

5. Making the Semantic Web Easier to Use , Presentation.

6. Making the Semantic Web Easier to Use for Sharing Science Data, Presentation.

7. RDF123 Google Group, Google group.

8. RDF123 java application v1.0, Executable.

9. RDF123 linux application v1.0, Executable.

10. RDF123 presentation, Presentation.

11. RDF123 windows application v1.0, Executable.

12. Tables to Linked Data, Presentation.

13. Tables to Linked Data, Presentation.


Research Areas:
 Machine learning
 Semantic Web
 Web based information systems