SEMDIS: Knowledge Discovery in the Semantic Web

Anupam Joshi, Tim Finin, and Yelena Yesha

June 13, 2004

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semantic web

Our research will focus on designing, prototyping, and evaluating a system called SemDIS (Semantic Discovery) that supports indexing and querying of complex semantic relationships and is driven by notions of information trust and provenance and models of hypotheses and arguments under investigation. This poster was prepared for the June 2004 NSF ITE grantees meeting for the project ITR-SemDIS: Discovering Complex Relationships in the Semantic Web..

Research in search techniques was a critical component of the first generation of the Web, and it has gone from academia to mainstream. A second-generation Semantic Web will be built by adding semantic annotations that software can understand and from which humans can benefit. Modeling, discovering, and reasoning about complex relationships on the Semantic Web will enable this vision and transform the hunt for documents into a more automated analysis enabled by semantic technology. The beginnings of this shift from search to analysis can be observed in research and industry as users look beyond finding relevant documents based on keywords to find actionable information leading to decision-making and insights. Large-scale semantic annotation of data (domain-independent and domain-specific) is now possible because of an accumulation of advances in entity identification, automatic classification, taxonomy and ontology development, and metadata extraction. The next frontier, which fundamentally changes how we acquire and use knowledge, is automatically identifying complex relationships between entities in this semantically annotated data. Instead of a search engine that returns documents containing terms of interest, we envision a system that returns actionable information (with the associated sources and supporting evidence) to a user or application. The user interacts with the information universe through a hypothesis-driven approach that combines search and inferencing, enabling more complex analysis and deeper insight. The examples in our narrative show that such a capability also dramatically enhances the capacity of intelligence analysts to obtain (in time) information, leading to a more secure homeland and world.




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