Proceedings and Publicity Chair

Researchers in many fields use dozens of different rapidly growing on-line knowledge sources, each with its own structure and access methods. Successful research often depends on a researcher's ability to discover connections among many different sources of information. The popularity of Google suggests that high-quality indexing would provide a uniform method of access, although it still leaves researchers with vast, undifferentiated lists of results. Hence, the research challenge for semantic web designers: can a knowledge-based approach provide a better way for researchers to explore knowledge and discover useful insights for their research? In this talk, I will use the example of bio-medical knowledge discovery to explore the key issues in semantic indexing of large amounts of heterogeneous information. I will propose a method and architecture for the creation of practical tools for semantic indexing and exploration. The example I'll be using is the Knowledge Discovery Tool, or KDT, which contains a knowledge model of a large number of bio-medical concepts and their relationships: from genes, proteins, biological targets and diseases to articles, researchers and research organizations. Based on this model, the KDT index identifies over 2.5 million bio-medical entities with two billion relationships among those entities spanning 15 different knowledge sources. Clearly, the creation and maintenance of such an index cannot be done manually. KDT utilizes an extensive set of rules that cleanse, analyze and integrate data to create a uniform index. Using its index, KDT presents the user with a uniform graphical browsing space integrating all underlying knowledge sources. This space is "warped" and filtered based on domain-specific rules customized for the needs of various groups of users, such as pharmaceutical researchers, clinicians, etc. Another customized set of rules discovers and graphically highlights potential indirect relationships among various entities that might be worth exploring (e.g., relationships between genes or between diseases). Finally, the tool enables several modes of collaboration among its users from annotations to activities tracking. Currently, KDT is undergoing testing in two pilot settings: an early stage of the drug discovery process in a pharmaceutical company and a bio-medial academic research group. About The Speaker Anatole Gershman joined Accenture Technology Labs in 1989 and in 1997 became its overall Director of Research. Under his leadership, research at the laboratories is focusing on early identification of potential business opportunities and the design of innovative applications for the home, commerce and work place of the future. These include electronic commerce, high-performance virtual enterprise, knowledge management, and human performance support. To achieve these goals, the laboratories are conducting research in the areas of ubiquitous computing, human-computer interaction, interactive multimedia, information access and visualization, intelligent agents, and simulation and modeling. Prior to joining Accenture, Anatole spent over 15 years conducting research and building commercial systems based on Artificial Intelligence and Natural Language processing technology. He held R&D positions at Coopers & Lybrand, Cognitive Systems, Inc., Schlumberger, and Bell Laboratories. In 1997, Anatole was named among the top 100 technologists in the Chicago area by Crain's Chicago Business. In 2000, Industry Week named Anatole one of the "R&D stars to watch." Anatole studied Mathematics and Computer Science at Moscow State Pedagogical University and received his Ph.D. in Computer Science from Yale University in 1979. Generic Model Management: A Database Infrastructure for Schema Manipulation Philip A. Bernstein Microsoft Research USA Abstract Meta data management problems are pervasive in the development and maintenance of semantic web applications. Although solutions to these problems are similar to each other, today they are solved in an applicationspecific way and usually require much object-at-a-time programming. To make solutions more generic and easier to program, we propose a higher level interface, called Model Management. The main abstractions are models and mappings between models. It treats these abstractions as bulk objects and offers such operators as Match, Merge, Diff, Compose, Extract, and ModelGen. We will present an overview of Model Management and recent results about some of the operators. About The Speaker Phil Bernstein is a researcher at Microsoft Corporation. Over the past 25 years, he has been a product architect at Microsoft and at Digital Equipment Corp., a professor at Harvard University and Wang Institute of Graduate Studies, and a VP Software at Sequoia Systems. During that time, he has published over 100 articles on the theory and implementation of database systems, and coauthored three books, the latest of which is "Principles of Transaction Processing for the System Professional" (Morgan Kaufmann, 1997). He holds a B.S. from Cornell University and a Ph.D. from University of Toronto. A summary of his current research on meta data management can be found at http://www.research.microsoft.com/~philbe. From Semantic Search to Analytics and Discovery on Heterogeneous Content: Changing Focus from Documents and Entities to Relationships Amit Sheth LSDIS Lab, The University of Georgia and Semagix, Inc. USA Abstract Research in search techniques was a critical component of the first generation of the Web, and has gone from academe to mainstream. Research and products supporting Semantic Search also look promising. A second generation ”Semantic Web” is being realized in one form of a scalable ontology-driven information system, where semantic metadata allow software to associate meaning with heterogeneous content. This is enabling a fundamental shift in focus from documents and entities within documents to discovering and reasoning about relationships. And it will transform the hunt for documents that humans can examine or analyze into a more automated content analysis, resulting in actionable information and insights into heterogeneous content. In this talk, we juxtapose the following shifts, to paint the exciting new possibilities: • From documents and entities to relationships • From techniques that focus on either unstructured data (text) or structured content to both types and semi-structured data • From directly analyzing data to ontology based processes of creating high quality metadata and analyzing metadata • From search and browsing for delivering relevant documents and locating entities within contents to discovering complex relationships and delivering actionable information with insights; from semantic search to analytics and discovery-based semantic applications This talk will interleave academic research with state-of-the-art commercial uses, including tools and real-world applications and experiences. The critical challenge in dealing with the Web scale of ontologies (with huge description base/assertion set), metadata (very large RDF graphs), and their analysis in discovering relationship will be discussed. About The Speakers Amit Sheth is a Professor at the University of Georgia and CTO of Semagix, Inc. He started the LSDIS lab at Georgia in 1994. Earlier he served in R&D groups at Bellcore, Unisys, and Honeywell. He founded his second company, Taalee, in 1999 based on technology developed at the LSDIS lab, and managed it as CEO until June 2001. Following Taalee's acquisition/merger, he currently serves as CTO and a co-founder of Semagix, Inc. His research has led to three significant commercial products, several deployed applications and over 150 publications. More: http://lsdis.cs.uga.ed/~amit Spatially Navigating the Semantic Web for User Adapted Presentations of Cultural Heritage Information in Mobile Environments