MDS 2011 - International Workshop on Mining Data Semantics (MDS2011)

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.