Coherent Knowledge Systems will be presenting a 90 minute tutorial on Rulelog, Powerful Practical Semantic Rules in Rulelog: Fundamentals and Recent Progress on August 2, 2015 as part of the 9th International Web Rule Symposium – RuleML and the Reasoning Web Summer School RW-2015: http://www.csw.inf.fu-berlin.
Hope you can join us there!
Coherent is a proud sponsor of RuleML 2015.
by Benjamin Grosof Michael Kifer Paul Fodor
Coherent Knowledge Stony Brook Stony Brook
Systems, USA University, USA University, USA
In this tutorial, we cover the fundamental concepts and recent progress in the area of Rulelog, a leading approach to semantic rules knowledge representation and reasoning. Rulelog is expressively powerful, computationally affordable, and has capable efficient implementations. A large subset of Rulelog is in draft as an industry standard to be submitted to RuleML and W3C as a dialect of Rule Interchange Format (RIF).
“Textual” Rulelog, in which Rulelog is closely combined with natural language processing by using Rulelog to interpret and generate English, is a key area of ongoing research and development (R&D).
Rulelog extends well-founded declarative logic programs (LP) with:
- strong meta-reasoning, including higher-order syntax (Hilog), reification, and rule id’s (within the logical language)
- explanations of inferences
- efficient higher-order defaults, including “argumentation rules”
- flexible probabilistic reasoning, including evidential probabilities and tight integration with inductive machine learning
- this is a key area of recent technology progress and ongoing R&D
- bounded rationality, including restraint ‒ a “control knob” to ensure that the computational complexity of inference is worst-case polynomial time
- “omni-directional” disjunction in the head (of a rule)
- existential quantifiers (mixed with universal quantifiers) in the head
- sound tight integration of first-order-logic ontologies including OWL
- frame syntax, similar to RDF triples and object-orientation
- and several other lesser features, including aggregation operators and integrity constraints
Implementation techniques for Rulelog inferencing include transformational compilations and extensions of LP “tabling” algorithms. “Tabling” here includes: smart cacheing of conclusions; and incrementally revising the cached conclusions when rules are dynamically added or deleted. “Tabling” is thus a mixture of backward-direction and forward-direction inferencing. There are both open-source and commercial tools for Rulelog that vary in their range of expressive completeness and of user convenience. They are interoperable with databases and spreadsheets, and complement inductive machine learning and natural language processing techniques.
The most complete system today for Rulelog is Ergo from Coherent Knowledge Systems. Using Ergo, we will illustrate that Rulelog technology has applications in a wide range of tasks and domains in business, government, and science. We will tour areas of recent applications progress that include: legal/policy compliance, e.g., in financial services; education/tutoring; and e-commerce marketing. This tutorial will provide a comprehensive and up-to-date introduction to these developments and to the fundamentals of the key technologies and outstanding research issues involved.