The Semantic Web (or Web 3.0) promises to “organize the world’s information” in a dramatically more logical way than Google can ever achieve with their current engine design. This is specially true from the point of view of machine comprehension as opposed to human comprehension.The Semantic Web requires the use of a declarative ontological language like OWL to produce domain-specific ontologies that machines can use to reason about information and make new conclusions, not simply match keywords. However, the Semantic Web, which is still in a development phase where researchers are trying to define the best and most usable design models, would require the participation of thousands of knowledgeable people over time to produce those domain-specific ontologies necessary for its functioning. Machines (or machine-based reasoning, aka AI software or ‘info agents’) would then be able to use those laboriously –but not entirely manually– constructed ontologies to build a view (or formal model) of how the individual terms within the information relate to each other. Those relationships can be thought of as the axioms (basic assumptions), which together with the rules governing the inference process both enable as well as constrain the interpretation (and well-formed use) of those terms by the info agents to reason new conclusions based on existing information, i.e. to think. In other words, theorems (formal deductive propositions that are provable based on the axioms and the rules of inference) may be generated by the software, thus allowing formal deductive reasoning at the machine level. And given that an ontology, as described here, is a statement of Logic Theory, two or more independent info agents (an excellent article from evolving trends)
