We conduct the first systematical adoption of the Semantic Web solution in the integration, management, and utilization of TCM information and knowledge resources. As the results, the largest TCM Semantic Web ontology is engineered as the uniform knowledge representation mechanism; the ontology-based query and search engine is deployed, mapping legacy and heterogeneous relational databases to the Semantic Web layer for query and search across database boundaries; the first global herb-drug interaction network is mapped through semantic integration, and the semantic graph mining methodology is implemented for discovering and interpreting interesting patterns from this network. The platform and underlying methodology are proved effective in TCM-related drug usage, discovery, and safety analysis.

BACKGROUND: Recent advances in Web and information technologies with the increasing decentralization of organizational structures have resulted in massive amounts of information resources and domain-specific services in Traditional Chinese Medicine. The massive volume and diversity of information and services available have made it difficult to achieve seamless and interoperable e-Science for knowledge-intensive disciplines like TCM. Therefore, information integration and service coordination are two major challenges in e-Science for TCM. We still lack sophisticated approaches to integrate scientific data and services for TCM e-Science. RESULTS: We present a comprehensive approach to build dynamic and extendable e-Science applications for knowledge-intensive disciplines like TCM based on semantic and knowledge-based techniques. The semantic e-Science infrastructure for TCM supports large-scale database integration and service coordination in a virtual organization. We use domain ontologies to integrate TCM database resources and services in a semantic cyberspace and deliver a semantically superior experience including browsing, searching, querying and knowledge discovering to users. We have developed a collection of semantic-based toolkits to facilitate TCM scientists and researchers in information sharing and collaborative research. CONCLUSION: Semantic and knowledge-based techniques are suitable to knowledge-intensive disciplines like TCM. It's possible to build on-demand e-Science system for TCM based on existing semantic and knowledge-based techniques. The presented approach in the paper integrates heterogeneous distributed TCM databases and services, and provides scientists with semantically superior experience to support collaborative research in TCM discipline.