Change Paths In Reasoning !

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Information about Change Paths In Reasoning !

Published on November 11, 2007

Author: loffenauer

Source: slideshare.net

Description

Presentation of my position paper published at the "New forms of reasoning" ISWC workshop.

Change paths in reasoning! Raphael Volz FZI Forschungszentrum Informatik Universität Karlsruhe (TH) Karlsruhe, Germany 11.11.2007

Proposition We need a consensus benchmark Approximate is better than nothing Tractable languages make speed Incremental reasoning is smart

We need a consensus benchmark

Approximate is better than nothing

Tractable languages make speed

Incremental reasoning is smart

We need a consensus benchmark (1) Recent Performance Benchmark @ FZI Note: Joint work with Jürgen Bock, Qiu Ji, Peter Haase, Pellet performance close to Racer, Sesame preformance close to OWLIM Is this evaluation representative???

We need a consensus benchmark (2) Shortcomings of various benchmarks Source: Timo Weithörner et al., What‘s wrong with owl benchmarks, Proc. of 2nd int. Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS 2006)

Need for a consensus benchmark (3) Improvements Achieved with TREC Source: E. M. Voorhees, TREC: Improving Information Access through Evaluation, American Society for Information Science and Technology   Vol. 32, No. 1  Oct/Nov 2005 Consensus Metric Consensus Data Sets

Approximate is better than nothing (1) Ontologies in WATSON Corpus Source: Mathieu d‘Acquin et al., Characterizing Knowledge on the Semantic Web with Watson, EON 2007 Workshop, Busan, Korea We need to approximate with OWL DL reasoners

Approximate is better than nothing (2) Idea derived from FOL work Source: Hitzler and Vrandecic, Resolution-based approximate reasoning for OWL DL, in Y. Gil et al. (Eds.): Proc. of ISWC 2005, LNCS 3729, pp. 383–397, 2005.

Idea derived from FOL work

Approximate is better than nothing (3) Idea derived from FOL work Source: Hitzler and Vrandecic, Resolution-based approximate reasoning for OWL DL, in Y. Gil et al. (Eds.): Proc. of ISWC 2005, LNCS 3729, pp. 383–397, 2005.

Idea derived from FOL work

Approximate is better than nothing (3) Syntactic approaches ( possibly unsound ) Stuckenschmidt & van Harmelen 2002 Wache, Groot, & Stuckenschmidt 2005 Hitzler & Vrandecic 2005 Groot, Stuckenschmidt & Wache 2005 Hurtado, Poulovassilis, & Wood 2006 Approaches based on Knowledge Compilation Selman & Kautz 1991 Pan & Thomas 2007

Syntactic approaches ( possibly unsound )

Stuckenschmidt & van Harmelen 2002

Wache, Groot, & Stuckenschmidt 2005

Hitzler & Vrandecic 2005

Groot, Stuckenschmidt & Wache 2005

Hurtado, Poulovassilis, & Wood 2006

Approaches based on Knowledge Compilation

Selman & Kautz 1991

Pan & Thomas 2007

Tractable languages make speed (1) Dez 2003 - DAML.ORG Corpus Source: Mathieu d‘Acquin et al., Characterizing Knowledge on the Semantic Web with Watson, EON 2007 Workshop, Busan, Korea Jul 2007 - WATSON Corpus Source: Raphael Volz, Web Ontology Reasoning with logic databases, dissertation, university of karlsruhe, 2004 Tractable Languages dominate (and will continue to do so)

Tractable languages make speed (2) Source: Markus Krötzsch, Sebastian Rudolph, and Pascal Hitzler; Complexity of Horn Description Logics Technical Report, Institute AIFB, University of Karlsruhe, 2007 Combined complexity of various DLs 1 1 1 1 3 # Available Reasoners (known to me)

Incremental reasoning is smart (1) Incremental answers is what we expect on the web Standard expectation for query answering on the web

Incremental reasoning is smart (2) Alternate possible interpretations of incremental reasoning Maintain state information from previous reasoning cycles when dealing with change to the KB (Pellet interpretation) Provide answers / results incrementally (anytime behaviour) Incremental reasoning saves work Source: C. Halaschek-Wiener et al. Description Logic Reasoning for Dynamic A-Boxes, 2006 DL Workshop, CEUR WS 189

Alternate possible interpretations of incremental reasoning

Maintain state information from previous reasoning cycles when dealing with change to the KB (Pellet interpretation)

Provide answers / results incrementally (anytime behaviour)

The ideal Semantic WEB reasoning approach time Quality as a combination of soundness and completness Functional Qualities Measurable quality Recognizable quality Monotonicity Consistency Diminishing returns Interruptability Preemtability Source: Shlomo Zilberstein, Using Anytime Algorithms in Intelligent Systems, AI Magazine, Fall 1996 HAPPY TO DISCUSS WITH YOU !!!

Functional Qualities

Measurable quality

Recognizable quality

Monotonicity

Consistency

Diminishing returns

Interruptability

Preemtability

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