For over thirty years, the complexity of knowledge acquisition has been the greatest obstacle to widespread use of semantic systems. The task of translating information from a textbook to a computable semantic form requires the combined skills of a linguist, logician, computer scientist, and subject-matter expert. Any system that requires its users to have all those skills will have few, if any, users. The challenge is to design automated tools that can combine the contributions from multiple experts with different kinds of skills. This article surveys systems with different levels of semantics: lightweight, middleweight, and heavyweight. Linked data systems with lightweight semantics are easy to develop, but they can't interpret the data they link. The heavyweight systems of traditional AI can perform deep reasoning, but they place too many demands on the knowledge engineers. No one can predict what innovations will be discovered in the future, but commercially successful systems must satisfy two criteria: first, they must solve problems for which a large number of people need solutions; second, they must have automated and semi-automated methods for acquiring, analyzing, and organizing the required knowledge. © Springer-Verlag Berlin Heidelberg 2011.
Mendeley saves you time finding and organizing research
Choose a citation style from the tabs below