|First International Workshop on
Semantic Web and Databases
Accenture Technology Labs
|Can we do better than Google? Using semantics to explore large heterogeneous knowledge sources|
|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.
|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.
|Back to the SWDB Program|