An increasing amount of data is stored in an interconnected manner. Such data range from the Web; hyperlinked pages; to bibliographical data; graph of citations; to biological data; associations between proteins, genes, and publications; to clinical data; associations between patients, hospitalizations, exams and diagnoses. A critical need in order to leverage the available data is the enablement of information discovery, i.e., given a question (query) find pieces of data or associations between them in the data graph that are "good" (relevant, authoritative and specific) for the query, and rank them according to their "goodness". Submitting such queries should not require knowledge of a complex query language (e.g., SQL) or of the details of the data (e.g., schema). Unfortunately, little has been done to provide high-quality information discovery on data graphs in domains other than the Web, where search engines have been successful. This project is expected to have the following broader impacts: (a) Promote participation of FIU (one of the largest Hispanic institutes in the country) minority students in the research process, in the form of independent or senior class projects. (b) Facilitate effective information discovery on biological and clinical data, which can lead to cost savings, and increased research productivity in these domains. The results will be disseminated through publications, public Web demo systems, and the project Web site (http://dbir.cis.fiu.edu/DGID).
