The Semantic Web is a group of methods and technologies designed to allow machines to understand the meaning – or "semantics" – of information on the World Wide Web (WWW). It comprises standards and tools associated with XML, XML Schema, RDF, RDF Schema, and OWL, and organized in the Semantic Web Stack. During the last decade the number and scope of Semantic Web applications has remarkably increased.
The objective of the Linked Open Data (LOD) community is to extend the Web by publishing various open datasets as Resource Description Framework (RDF) links on the Web. These RDF links between data items can come from different data sources and be accessed anywhere through the Web.
To ensure that computers can understand the semantics of terms and to support the LOD movement, machine-readable ontologies can be used. Biomedical ontologies are consensus-based controlled biomedical vocabularies of terms and relations with associated definitions, which are logically formulated to promote automated reasoning. Biomedical ontologies play important roles such as (a) knowledge management, including the indexing and retrieval of data and information; (b) data integration, exchange and semantic interoperability; and (c) decision support and reasoning. While various LOD data are primarily instance data, ontologies provide classifications and relations among these instance data.
Ontobat is a web-based Semantic Web tool that aims to support ontology-based biological linked data generation, uploading, query, and statistical analysis. Ontobee is designed to have a suite of tools to integratively analyze instance experimental data using standardized ontology format.
We have developed a feasible Ontobat workflow that contains many relatively independent components, which can also be integrated together to perform various LOD data processing and analysis. Currently, the Lodquery, Ontovert, and OntoCOG work fine with our limited testing. The other programs, including Loadbee, Ontoload, and Ontostat, are still being developed and evaluated. Stay tuned.