Reasoning and Inference techniques

I found that the number of inference techniques is almost L * I_s , where

L is element {varieties of variations of (description) logics},

I is an inference technique element {Resolution, Magic sets, Tableaux calculus} and

s element IN representing the number of slight variations of the inference techniques, which are made to improve performance regarding one logic.

Thus I decided to focus on relevance for the Semantic Web.

I plan the following structure for my presentation:

• Example “A Query and Inference Service for RDF”
• Example query
• Database, rules
• Inference engine
• What can’t be expressed in RDF
• From RDF to logic
• Lots of logic: Frame-Logic, DLs, ALC, FOL, Horn clauses, HOL
• Example Ontobroker -> translation of Frame-logic in Horn clauses
• Inference techniques: Resolution, Magic sets, tableaux algorithms

Slides

Literature

Introductory, relevance to Semantic Web and an example:

http://www.semanticweb.org/inference.html (Overview, 1-2 pages)

http://www.diffuse.org/semantic-web.html

http://www.mozilla.org/rdf/doc/inference.html

http://www.w3.org/TandS/QL/QL98/pp/queryservice.html (useful as introductory reading, although is doesn’t tell much about inference techniques)

Knowledge Representation and Inferencing

Reasoning with DLs - Horrocks

MagicSets vs SLD Resolution - Brass

Technical Core of Ontobroker - Decker et al. (I want to show their approach translating from Frame Logic to FOL)

Logical Foundations of F-Logic - Kifer

tableaux-calculus (from some lecture notes, I lost track from where …)

Subsumption Algorithms - Hollunder

(I have got a bunch of more papers, but they all focus on very specific aspects of certain logics or algorithms)