ACLK 2015 - Tutorial: “Acquisition of Complex Logical Knowledge: Authoring, Assimilation, and Assembly”

Machine learning (e.g., text extraction) and graph databases (e.g., SPARQL/RDF) are currently hot topics in the industry. However, to deliver business/social value more effectively ? e.g., for policy/legal compliance in finance, marketing, and health; personalized tutors in science; and human-computer interaction ? these technologies need to be combined with more complex human-authored knowledge and deeper reasoning. Recently new expressively powerful yet computationally affordable techniques have emerged for such knowledge representation and reasoning, particularly for Rulelog, a rich form of semantic rules and ontologies that can integrate closely with natural language (NL) as well as machine learning. A large subset of Rulelog is in draft as an industry standard. It overcomes many of the limitations of classical logic.
In this tutorial, we cover the fundamentals and latest progress in acquiring such complex logical knowledge. Recent techniques for human authoring starting from source text have significantly reduced the effort and skill required per sentence; these techniques rely on explanations, NL interpretation, logic-based NL generation, and collaborative curation. Recent techniques for assimilating and automatically importing knowledge from structured sources have significantly reduced the cost and increased the scale of knowledge base assembly, using standards-centric semantic interoperability plus ontological mapping knowledge.