Contact Student: Zachary Wemlinger, zachary.wemlinger@email.wsu.edu

Details:

Mining the Semantic Web Graph is a complex and challenging problem. The Semantic Web is a suite of standards meant to enable the creation of a machine readable web of data. This web of data could then be used by intelligent agents to share and learn on a global scale. Graphs are the underlying data structure used throughout the Semantic Web.

This presents a number of challenges:

- How can we retrieve not just information but also knowledge on the Semantic Web?

- How can we leverage a heterogeneous set of data sources in order to solve a local problem?

- How can knowledge and information be best represented on the Semantic Web?

Finding answers to these problems will not only enable better distribution of information, it will also bring us closer solving some of the Grand Challenges in Artificial Intelligence such as natural language understanding and intelligent reasoning.

Datasets:

RDF data sets (https://www.w3.org/wiki/DataSetRDFDumps)

Billion Triple Challenge 2009 (https://vmlion25.deri.ie/)

Publications:

Berners-Lee, T., & Hendler, J. (2001). Scientific publishing on the semantic web. Nature, 410, 1023–1024.  

Chandrasekaran, B., Josephson, J. R., & Benjamins, V. R. (2002). What are ontologies, and why do we need them? Intelligent Systems and Their Applications, IEEE, 14(1), 20–26.  

Hendler, J. (2001). Agents and the semantic web. IEEE Intelligent systems, 16(2), 30–37.  

Cook, D. J., & Holder, L. B. (2007). Mining graph data. Wiley-Blackwell.  

Shadbolt, N., Hall, W., & Berners-Lee, T. (2006). The semantic web revisited. Intelligent Systems, IEEE, 21(3), 96–101.  

Tong, Y., & Chen, H. (2008). Semantic graph mining for biomedical network analysis. In Proceedings of the WWW Workshop on Semantic Web for Health Care and life Science.