UMBC ebiquity

Tables to Linked Data

Status: Past 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

Faculty:
Tim Finin
Anupam Joshi

Students:
Varish Mulwad

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

 

There are 14 associated publications:
 Click here for a full list...

 

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 (With Java VM self-contained), Executable.

10. RDF123 presentation, Presentation.

11. RDF123 windows application v1.0 (With Java VM self-contained), Executable.

12. Tables to Linked Data, Presentation.

13. Tables to Linked Data, Presentation.

 

Research Areas:
 Machine learning
 Semantic Web
 Web based information systems