Collaborative Proposal: ITR-SemDIS: Discovering Complex Relationships in the Semantic Web

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Research in search techniques was a critical component of the first generation of the Web, and has gone from academe to mainstream. A second generation "Semantic Web" is being built by adding semantic annotations that machines 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 finding actionable information leading to decision making and insights. Large scale semantic annotation of data (both 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 the way we acquire and use knowledge, is to automatically identify complex relationships between entities in this semantically annotated data. Instead of a search engine that returns documents containing terms of interest, there will be a system that returns actionable information (with the associated sources and supporting evidence) to a user or application. The user interacts with information universe through a hypothesis driven approach that combines search and inferencing, enabling more complex analysis and deeper insight. The research will focus on the design, prototyping and evaluation of 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. Such a capability greatly enhances the capacity of intelligence analysts to obtain (in time) information leading to a more secure homeland and world. Corresponding to the breadth and depth of the topics involved in the challenge undertaken, this is a collaborative project involving researchers at UGA's LSDIS lab and UMBC. SemDIS will have broader impacts beyond the education and training of graduate students, and the publication of research findings. Results from the research will be integrated with courses, both existing and new. Institutional mechanisms in place will seek participation of students from underrepresented groups. The work will also gain from several academic-industry collaborations of the investigators. There will be an opportunity to leverage commercial infrastructure and raw metadata provided by Semagix. The researchers will collaborate with industry, and the students will be encouraged to intern at collaborating industrial labs. Within a broader social context, emerging knowledge-centric technologies raise legitimate privacy and civil liberties concerns. Building upon past policy making experience, the investigators will comment on potential implications of their scientific progress. More information can be found at http://http://lsdis.cs.uga.edu/SemDIS/ and at http://www.cs.umbc.edu/SemDIS/