Linked Data
Linked Data

To a user, the web is an exciting world of interconnected links. Yet to a computer, large portions of the web remains a flat, boring world devoid of meaning, because there is not enough machine-readable information about the semantic relationship between the data. This is a pity, as documents online describe real objects objects and imaginary concepts, and give particular relationships between them.

The Linked Data project develops Tim Berners-Lee's vision of the 'Semantic Web' in which all documents contain semantic information in machine-readable form, and links can be created with relationship values between them. This extra level of semantics enables us to use computer power to exploit the information to a greater extent than through human reading. Today, the concept of linked data has evolved from Berners-Lee's original proposal and plays an increasingly important role on the web.

Our work on the Linked Data project explores various topics from ranking methods for entity-oriented semantic web search, through the exploration of linked open government data, to how machine and human driven computation can be combined to create, manage and use semantic information more effectively.

Investigators

Publications
Buneman, P., & Staworko S. (2016).  RDF Graph Alignment with Bisimulation. Proc. VLDB Endow.. 9, 1149–1160.
Kostylev, E. V., & Reutter J. L. (2015).  Complexity of answering counting aggregate queries over DL-Lite. Web Semantics: Science, Services and Agents on the World Wide Web. 33, 94 - 111.
Story, H. (2015).  Linked Data Platform 1.0..
Koumenides, C. L., & Shadbolt N. (2014).  Ranking methods for entity-oriented semantic web search. Journal of the Association for Information Science and Technology. 65, 1091–1106.
Hall, W. (2014).  Linked Data: The Quiet Revolution.
Shadbolt, N., & O'Hara K. (2013).  Linked Data in Government. IEEE Internet Computing. 17, 72-77.
Simperl, E., Cuel R.., & Stein M.. (2013).  Incentive-Centric Semantic Web Application Engineering. 4.
Kostylev, E. V., & Reutter J. L. (2013).  Answering Counting Aggregate Queries over Ontologies of the DL-lite Family. Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. 534–540.
Gianluca, C., & Shadbolt N. (2013).  Linked Nomenclature of Territorial Units for Statistics. Semantic Web. 4, 251–256.
Gutiérrez-Basulto, V., Ibañez-García Y., Kontchakov R., & Kostylev E. V. (2013).  Conjunctive Queries with Negation over DL-Lite: A Closer Look. Proceedings of the 7th International Conference on Web Reasoning and Rule Systems. 109–122.
Grau, B. Cuenca, Kharlamov E., Kostylev E. V., & Zheleznyakov D. (2013).  Controlled Query Evaluation over OWL 2 RL Ontologies. Proceedings of the 12th International Semantic Web Conference - Part I. 49–65.
Simperl, E., Norton B.., Acosta M., Maleshkova M.., Domingue J.., Mikroyannidis A.., et al. (2013).  Using Linked Data Effectively.
Berners-Lee, T., & O'Hara K. (2013).  The read–write Linked Data Web. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. 371,
Correndo, G., Shadbolt N., & Hitzler P(ed.). (2013).  Linked nomenclature of territorial units for statistics.
Correndo, G., Penta A., Gibbins N., & Shadbolt N. (2012).  Statistical Analysis of the owl:sameAs Network for Aligning Concepts in the Linking Open Data Cloud. (Liddle, S. W., Schewe K-D., A. Tjoa M., & Zhou X., Ed.).Database and Expert Systems Applications: 23rd International Conference, DEXA 2012, Vienna, Austria, September 3-6, 2012. Proceedings, Part II. 215–230.
Correndo, G., Salvadores M., Millard I., Glaser H., & Shadbolt N. (2010).  SPARQL Query Rewriting for Implementing Data Integration over Linked Data.
Shadbolt, N., Berners-Lee T., & Hall W. (2006).  The Semantic Web Revisited. IEEE Intelligent Systems. 21, 96–101.