R&D information has become highly fragmented and scientists continually struggle with how to access all of the data necessary to make decisions. While Web technology improvements have helped to simplify access to scientific data via Web pages, aggregating and making useful connections between datasets requires manual integration by scientists at the desktop. With Semantic Web technologies, machines link or combine data at the server level, alleviating manual efforts to unify data, and enabling scientists to reliably share datasets and annotations.

Implemented using standards developed by the World Wide Web Consortium (W3C), a Semantic Web allows for the reliable exchange of data in a dynamic database framework. By extending XML with key standards including Resource Description Framework (RDF), Uniform Resource Identifier (URI) Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL), an organization's network of information can become interconnected and viewable through server-based applications, while accommodating changing data requirements. A Semantic Web whether implemented on an intranet, extranet, or the Internet can dynamically unify cheminformatics, bioinformatics and laboratory data, making R&D information searchable, reusable and historically relevant for organizations.

Semantic Web data stores utilize RDF to record scientific data using a "triple", which contains a subject, property and value. Unlike pre-defined relational or XML schemas, a Semantic Web data stores unlimited types of data in the form of "triples" thereby allowing associations between multiple data sources, experiment parameters and findings to occur on the fly.

Using OWL and SWRL technologies, Semantic Web servers can aggregate inter-related data by using specific rules, meaning and relations. OWL enables Semantic Web-based applications to process meta-data contained within data stores, making searches more extensive and relevant. OWL, used in conjunction with SRWL, allows diverse data and formats to be mapped across applications, making meaningful connections throughout the value chain.

With RDF, organizations can integrate biological pathway analysis and experiment data and processes within a centralized network without the restrictive data management rules of corporate data warehouses.