AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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On Representing Activity Context via Semantic Rule Methods (Summary of Invited Talk)
Benjamin N. Grosof

Last modified: 2013-06-28


We analyze several of the key technical and practical challenges involved in representing activity context across a large variety of knowledge, components, and applications. We present two novel broad methods that enable semantic knowledge capture and interchange, and suggest how they can be used for activity context-awareness. The first is knowledge representation and reasoning (KRR) in Rulelog, an expressively extended form of declarative logic programs that features defeasible higher-order logic formulas yet is computationally tractable, and is a draft dialect of W3C RIF. Rulelog's expressiveness enables representation of exceptions and change, and thus processes, agreements, and policies, e.g., for confidentiality. The second broad method is Textual Logic, an approach to mapping between natural language (text) and logic, where the mapping itself is logic-based. Textual Logic leverages Rulelog's expressiveness to enable relatively rapid text-based authoring of rich knowledge, reducing the knowledge acquisition bottleneck. Together, Rulelog and Textual Logic help address the potential for ontological and KRR Babel that lurks when representing activity context using previous semantic technologies.


knowledge representation, rules, semantic, logic programs, knowledge acquisition, natural language

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