On Representing Activity Context via Semantic Rule Methods ( Summary of Invited Talk )

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 contextawareness. 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. Introduction and Requirements Architecting activity context-aware systems requires representing context knowledge across large and heterogeneous variety of information, components, and applications. The Workshop’s Call For Participation ably gives a list, but it can seem nigh overwhelming. An immediately apparent key challenge is: • Problem 1: How is one to avoid Babel? I.e., how can one to achieve enough reusability of the knowledge and reasoning involved in order to make useful and sophisticated activity context-awareness become economically feasible? Logical knowledge representation and reasoning (KRR) has advantages in regard to accuracy and explicability, as compared to techniques based on inductive (i.e., statistical) Copyright c © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. machine learning / data mining. The high-level vision of semantic technologies and semantic web, founded upon KRR, is attractive as a means of capturing and interchanging the context, insofar as it is largely focused on reusability via knowledge interchange. However, there is as yet a long way still to go in order to achieve that vision. Some of the key underlying technical challenges include: • How to acquire needed/useful logical knowledge (K) that is expressively rich? When specified manually, in past this has been quite costly. A key goal in the field of expressive knowledge representation and reasoning (KRR) is to reduce the cost of authoring rich logical knowledge. • How to represent processes? • How to represent policies and agreements? E.g., about confidentiality. • What logical ontologies? E.g., that can be agreed upon that are widely useful yet sufficiently comprehensive? • How to map between ontologies? E.g., that are developed by different organizations or communities? • What KRR: what forms of K and what kinds of reasoning? • How to interchange between multiple KRR’s? E.g., between different semantic industry standards that are based on different fundamental logics? Those logics include classical logic (notably, first order logic), declarative logic programs (used in SQL, SPARQL, and many rule-based systems), and others. • How to integrate deductive reasoning and inductive reasoning? Particularly relevant for addressing those challenges are two novel broad methods we have recently developed for semantic rules, that enable semantic knowledge capture and interchange. The research on them was done largely in the Halo Advanced Research (HalAR) program (SILK 2013) (part of overall Project Halo) funded by Vulcan, Inc. These methods build on a longer line of R&D work that goes back more than 20 years: in declarative logic programs, web rules, semantic web services, and commercial rule systems. In this presentation we give a sense of where the frontier is in regard to these semantic methods, and make some fairly high-level suggestions on several aspect of their potential applicability to activity context-awareness. 11 Activity Context-Aware System Architectures: Papers from the AAAI 2013 Workshop