Information Integration and Knowledge Acquisition from Semantically Heterogeneous Biological Data Sources

Abstract. We present INDUS (Intelligent Data Understanding System), a federated, query-centric system for knowledge acquisition from autonomous, distributed, semantically heterogeneous data sources that can be viewed (conceptually) as tables. INDUS employs ontologies and inter-ontology mappings, to enable a user or an application to view a collection of such data sources (regardless of location, internal structure and query interfaces) as though they were a collection of tables structured according to an ontology supplied by the user. This allows INDUS to answer user queries against distributed, semantically heterogeneous data sources without the need for a centralized data warehouse or a common global ontology. We used INDUS framework to design algorithms for learning probabilistic models (e.g., Naive Bayes models) for predicting GO functional classification of a protein based on training sequences that are distributed among SWISSPROT and MIPS data sources. Mappings such as EC2GO and...

• Type: Conference
• Year: 2005
• Where: DILS
• Authors: Doina Caragea, Jyotishman Pathak, Jie Bao, Adrian Silvescu, Carson M. Andorf, Drena Dobbs, Vasant Honavar
• Keywords: Data Sources, DILS 2005, Heterogeneous Data Sources, INDUS