Discovering and integrating semantic information of heterogeneous data sources for
various business applications.
Using the semantic information for query expansion and information visualization.
Making data more meaningful for industrial data integration, data warehousing, and E-science scenarios.
Develop analytical and probabilistic methods to discover the semantics of various data
sources, including structured databases, plain text files, and the “Deep Web” (Drexel).
Develop an environment that empowers users to aggregate, visualize, and search both structured and unstructured data (Drexel).
Develop analytical tools that will be used to build domain knowledge in the form of concept hierarchy or lightweight ontologies by utilizing a collaborative semi-structured knowledge base, such as Wikipedia (UL Lafayette).
A semantic discovery tool called SemIntegrator that can extract structured records for given relational databases from unstructured data and annotate text documents using given ontology concepts and relationships.
An Ontology-based Annotation, Integration, and Visualization framework, or the OAIV framework, for visualizing and exploring semantic information in large document collections.
A method for extracting lightweight ontology from Wikipedia.
Accomplished most of the objectives of the project.
Developed working prototype tools and interfaces with innovative methods for
annotation, integration, and visualization.
Enhance the CVDI capability of working with big data.