An Approach for Biological Data Integration and Knowledge Retrieval based on Ontology, Semantic Web Services Composition, and AI Planning

Abstract

This chapter describes an approach involved in two knowledge management processes in biological fields, namely data integration and knowledge retrieval based on ontology, Web services, and Artificial Intelligence (AI) planning. For the data integration, Semantic Web combining with ontology is promising several ways to integrate a heterogeneous biological database. The goal of this work is to construct an integration approach for gram-positive bacteria organism that combines gene, protein, and pathway, thus allowing biological questions to be answered. The authors present a new perspective to retrieve knowledge by using Semantic Web services composition and Artificial Intelligence (AI) planning system, Simple Hierarchical Order Planner 2 (SHOP2). A Semantic Web service annotated with domain ontology is used to describe services for biological pathway knowledge retrieval at Kyoto Encyclopedia of Gene and Genomes (KEGG) database. The authors investigate the effectiveness of this approach by applying a real world scenario in pathway information retrieval for an organism where the biologist needs to discover the pathway description from a given specific gene of interest. Both of these two processes (data integration and knowledge retrieval) used ontology as the key role to achieve the biological goals.