Abstract
Of all possible intelligent NL applications and semantic artifacts, a special value is today ascribed to building the question answering systems (Q&A) with broad and wide ontological learning (Onto Query Project, 2004), classified as open-domain Q&A knowledge systems [Question Answering, From Wikipedia, 2006]. This line of research is considered as upgrading of a traditional keyword query processing in database systems, as endowing the Web search engines with answering deduction capacities. Ideally, such a general-purpose Q&A agent should be able to cover questions (matters, subjects, topics, issues, themes) from any branch of knowledge and domain of interest by giving answers to any meaningful questions, like the Digital Aristotle, “an application that will encompass much of the world’s scientific knowledge and be capable of answering novel questions and advanced problemsolving” (Project Halo, 2004). The trade name of the Digital Aristotle was inspired by the scholar mostly admired for the depth and width of his perception, whose mind spread over ontology, physics, logics, epistemology, biology, zoology, medicine, psychology, literary theory, politics, and art.