III: EAGER: Data Integration as a Dialogue with the User

University Of Pennsylvania, Philadelphia PA

Investigators

Abstract

This work establishes a new approach to providing ad hoc ("discovery") queries requiring integration and structuring: such queries help scientists learn possible relationships between topics, and help decision-makers or consumers explore options. The work develops a new system and underlying architecture based on an iterative process, where the system and user engage in a dialogue until the user has answers meeting his or her information need. The resulting system takes sources on the Web, discovers semantic relationships among them, and allows users to pose discovery queries. It leverages existing extraction, matching, and recommendation algorithms as sources of evidence to generate hypotheses and corresponding queries, and adjusts these hypotheses based on user feedback over the query results. Innovations include scalable models for combining features and learning to re weight hypotheses; query and source recommendation techniques; and means of generalizing tuple-based feedback to support or refute hypotheses. The research impact is a new paradigm for data integration by end users, which scalably combines machine learning and database concepts. The broader impact includes better discovery tools for scientific users and other users who sorely need them; improved integration of existing Web data resources; and new educational material on how networks of data can be as important as networks of systems and people. The PI is incorporating the research concepts into courses in the University of Pennsylvania's new Market and Social Systems Engineering Program, focused on the interface between people, protocols, and systems on the Internet, especially through social and data networks, as well as markets. More information on the project can be found on the project website at http://www.cis.upenn.edu/~zives/dialogue/