- Jiawei Han, Professor, Department of Computer Science, University of Illinois at Urbana-Champaign.
Bio:Jiawei Han, Professor, Department of Computer Science, University of Illinois at Urbana-Champaign. He has been working on research into data mining, data warehousing, database systems, data mining from spatiotemporal data, multimedia data, stream and RFID data, social network data, and biological data, with over 350 journal and conference publications. He has chaired or served in over 100 program committees of international conferences and workshops, including PC co-chair of 2005 (IEEE) International Conference on Data Mining (ICDM), Americas Coordinator of 2006 International Conference on Very Large Data Bases (VLDB), and senior PC member for 2008 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining. He is also serving as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data. He is an ACM Fellow and has received 2004 ACM SIGKDD Innovations Award and 2005 IEEE Computer Society Technical Achievement Award. His book "Data Mining: Concepts and Techniques" (2nd ed., Morgan Kaufmann, 2006) has been popularly used as a textbook worldwide.
The workshop aims to discuss key issues and practices of semantic mining as more and more semantic data is available thanks to the initiatives of the Linked Open Data (LOD) and robust techniques for semantic annotation of Web, social, sensor and mobile data. Many research efforts have been directed toward developing and demonstrating semantic techniques to analyze and mine this growing resource, and the workshop will provide a forum for researchers to showcase their efforts. Ontologies and background knowledge provide a shared conceptualization of a domain that can be use to go beyond syntactic and structural processing, and empower mining and analysis of the semantics of the data. In particular, as semantic data is organized as labeled graphs and as semantic reasoning derives new knowledge in the forms of ranked semantic associations, subgraph extraction and pattern mining. These trigger diverse research challenges, leading to investigation in novel algorithms for mining data semantics from the graph data, provenance in graph mining, multi-level semantic visualization, just to name a few.
Mining and analyzing data semantics will foster a cross-disciplinary forum to further enhance existing bounds and create new connections among these communities. This workshop solicits contributions on researches and practices of mining data semantics including theory, algorithms, and mining applications from computer science, life science, healthcare and other domains. Topics of interest include but are not limited to:
- Large scale enriched semantic metadata integration for mining from heterogeneous data sources (social networks, sensors, mobile devices) and publication of such semantic data (e.g., to LOD);
- Semantic-based query formulation and understanding;
- Large-scale common-sense taxonomy or domain model creation;
- Semantic search, browsing and exploration with human guidance and domain knowledge/or;
- Algorithms for semantic association and graph mining; light-weight reasoning;
- Extending LOD and Quality of LOD—disambiguation, identity, provenance, integration; Representation and extraction of rich relationships, use of schema and background knowledge for enriching semantics of Web of Data
- Performance and scalability for semantic graph mining;
- Personalized semantic graph mining;
- Domain specific mining (e.g., Life Science and Health Care);
- Multi-scale semantic visualization;
- Semantic-based social network analysis and collective intelligence mining.
Submission deadline: May 15th, 2011
Notification of acceptance: June 15th, 2011
Final papers due: June 30th, 2011
All papers submitted should have a maximum length of 8 pages and must be prepared using the ACM camera-ready template http://www.acm.org/sigs/pubs/proceed/template.html. Authors are required to submit their papers electronically in PDF format. The submission site URL will be available on our website shortly. All submissions should clearly present the author information including the names of the authors, the affiliations and the emails. The best papers will be invited to submit their extensions to the TKDD special issues.