Abstract
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.
Advisor: Tim Finin, University of Maryland, Baltimore County. This research was supported in part by NSF awards 0326460 and 0910838, MURI award FA9550-08-1-0265 from AFOSR, and a gift from Microsoft Research.
Chapter PDF
Similar content being viewed by others
References
Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia - a crystallization point for the web of data. Journal of Web Semantics 7(3), 154–165 (2009)
Cafarella, M.J., Halevy, A.Y., Wang, Z.D., Wu, E., Zhang, Y.: Webtables: exploring the power of tables on the web. PVLDB 1(1), 538–549 (2008)
Ding, L., DiFranzo, D., Graves, A., Michaelis, J.R., Li, X., McGuinness, D.L., Hendler, J.A.: Twc data-gov corpus: incrementally generating linked government data from data.gov. In: Proc 19th Int. Conf. on the World Wide Web, pp. 1383–1386. ACM, New York (2010)
Han, L., Finin, T., McNamee, P., Joshi, A., Yesha, Y.: Improved pmi utility on word similarity using estimates of word polysemy. TKDE (2011) (under review)
Han, L., Finin, T., Parr, C., Sachs, J., Joshi, A.: RDF123: From Spreadsheets to RDF. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 451–466. Springer, Heidelberg (2008)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)
Langegger, A., Wöß, W.: XLWrap – Querying and Integrating Arbitrary Spreadsheets with SPARQL. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 359–374. Springer, Heidelberg (2009)
Limaye, G., Sarawagi, S., Chakrabarti, S.: Annotating and searching web tables using entities, types and relationships. In: Proc. 36th Int. Conf. on Very Large Databases (2010)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, 1st edn. Cambridge University Press (July 2008)
Mulwad, V., Finin, T., Syed, Z., Joshi, A.: T2LD: Interpreting and Representing Tables as Linked Data. In: Proc. Poster and Demonstration Session at the 9th Int. Semantic Web Conf. (November 2010)
Mulwad, V., Finin, T., Syed, Z., Joshi, A.: Using linked data to interpret tables. In: Proc. 1st Int. Workshop on Consuming Linked Data, Shanghai (2010)
Polfliet, S., Ichise, R.: Automated mapping generation for converting databases into linked data. In: Proc. 9th Int. Semantic Web Conf. (November 2010)
Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11(1), 95–130 (1999)
Sackett, D., Rosenberg, W., Gray, J., Haynes, R., Richardson, W.: Evidence based medicine: what it is and what it isn’t. Bmj 312(7023), 71 (1996)
Sahoo, S.S., Halb, W., Hellmann, S., Idehen, K., Thibodeau Jr., T., Auer, S., Sequeda, J., Ezzat, A.: A survey of current approaches for mapping of relational databases to rdf. Tech. rep., W3C (2009)
Salton, G., Mcgill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)
Syed, Z., Finin, T.: Creating and Exploiting a Hybrid Knowledge Base for Linked Data. Springer, Heidelberg (April 2011)
Syed, Z., Finin, T., Mulwad, V., Joshi, A.: Exploiting a Web of Semantic Data for Interpreting Tables. In: Proc. 2nd Web Science Conf. (April 2010)
Vavliakis, K.N., Grollios, T.K., Mitkas, P.A.: Rdote - transforming relational databases into semantic web data. In: Proc. 9th Int. Semantic Web Conf. (2010)
Venetis, P., Halevy, A., Madhavan, J., Pasca, M., Shen, W., Wu, F., Miao, G., Wu, C.: Recovering semantics of tables on the web. In: Proc. 37th Int. Conf. on Very Large Databases (2011)
Wang, J., Shao, B., Wang, H., Zhu, K.Q.: Understanding tables on the web. Tech. rep., Microsoft Research Asia (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mulwad, V. (2011). DC Proposal: Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables. In: Aroyo, L., et al. The Semantic Web – ISWC 2011. ISWC 2011. Lecture Notes in Computer Science, vol 7032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25093-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-25093-4_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25092-7
Online ISBN: 978-3-642-25093-4
eBook Packages: Computer ScienceComputer Science (R0)