19 December 2012

Semantic Web

Semantic Web is all about making the web navigation more connected by treating every aspect of the web as a resource. This also inspires the concepts of linked data or the web of data. Semantic Web essentially utilizes XML based extensible languages specifically catering for the creation of and management of resources, knowledge, reasoning, as well as querying. Semantic Web is driven from the theoretical underpinnings of Knowledge Representation and Reasoning, one of the core branches of Artificial Intelligence.

The following books are valuable sources of information:
  • Logic in Computer Science: Modelling and Reasoning About Systems
  • Knowledge Representation and Reasoning
  • Semantic Web Programming
  • Semantic Web Primer
  • Semantic Web for the Working Ontologist
  • Practical Semantic Web and Linked Data Applications
  • Practical RDF
  • Explorer's Guide to Semantic Web
  • Linked Data Evolving the Web into a Global Data Space
  • Programming the Semantic Web
  • Learning SPARQL
As well as, learning Prolog especially the SWI-Prolog can be quite valuable asset which has a detailed background in Semantic Web experimental work especially for curation and archiving. Especially, the W3C site is a valuable reference point for keeping up with the progress - SemanticWeb@W3C. And, a list of tools associated for different language implementations - SemanticWebTools@W3C. Another context of work for which Semantic Web is often connected to is the concept of Semantic Tagging for Findability. The applications of Semantic Web are numerous as it is more about the resource context and utilizing the concepts of HTTP and REST. In general, intelligence in any aspect of application has a reference point to the context in which it is used. The more broad a context one incorporates into the application for aspects of knowledge and reasoning the lower the level of accuracy. This is because intelligence is learned and is best utilized by way of contextual specialization of concepts from which patterns and logical deductions for inference can further be derived dynamically. This can be equated to every aspect of Artificial Intelligence.