Zhisheng Huang, Frank van Harmelen Vrije University Amsterdam Karlsruhe , Oct 28 th 200 8
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Transcript of Zhisheng Huang, Frank van Harmelen Vrije University Amsterdam Karlsruhe , Oct 28 th 200 8
Using Semantic Distances for Reasoning with Inconsistent
Ontologies
Zhisheng Huang,Frank van Harmelen
Vrije University Amsterdam
Karlsruhe, Oct 28th 2008
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One cannot live without
inconsistency.
Carl Jung (1875-1961)
There is nothing constant in this world but inconsistency.
Jonathan Swift (1667-1745)
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The importance of the inconsistency problem
• A key ingredient of the Semantic Web vision is avoiding to impose a single ontology. Hence, merging ontologies is a key step.
• Merging multiple ontologies can quickly lead to inconsistencies[Hameed 2003].
• Migration and evolution also lead to inconsistencies.[Schlobach et al.2003, Haase et al. 2005]
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The importance of the inconsistency problem (cont.)
• Many ontologies are semantically so lightweight (e.g. expressible in RDF Schema only that the inconsistency problem doesn't arise.)
• Many of these semantically lightweight ontologies make implicit assumptions such as the Unique Name Assumption, or assuming that sibling classes are disjoint.
• If such assumptions are made explicit, many ontologies turn out to be inconsistent.
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Outline of This Talk
• Framework of Reasoning with Inconsistent ontologies
• Syntactic Approach
• Semantic Approach
• Implementation, Test, and Evaluation
• Conclusions
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Processing Inconsistent Ontologies
• Debugging inconsistent ontologies– diagnose and repair it when we encounter
inconsistencies (Schlobach, IJCAI 2003).
• Reasoning with inconsistent ontologies– simply avoid the inconsistency and apply a non-
standard reasoning method to obtain meaningful answers (Huang, van Harmelen, and ten Teije, IJCAI 2005).
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What an inconsistency reasoner is expected
– Given an inconsistent ontology, return meaningful answers to queries.
– General solution: Use non-standard reasoning to deal with inconsistency
|= : the standard inference relations
| : nonstandard inference relations
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Formal notions of Reasoning with Inconsistent Ontologies
• Various Answers• Accepted:• Rejected:• Over-determined:• Undetermined:
• Soundness: (only classically justified results)
• Meaningful: (sound & never over-determined)
soundness +
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Reasoning with inconsistent ontologies: Main Idea
Starting from the query,
1. select consistent sub-theory by using a relevance-based selection function.
2. apply standard reasoning on the selected sub-theory to find meaningful answers.
3. If it cannot give a satisfying answer, the selection function would relax the relevance degree to extend consistent sub-theory for further reasoning.
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Over-determined Processing
• If selected data set is too large so that it leads to inconstenties, we need some kinds of backtracking, called over-determined processing.
• Blind over-determined processing vs. Informed over-determined processing with threshold
• First Maximal consistent Set (FMC) approach
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Syntactic Relevance
Direct Syntactic relevance (0-relevance). – there is a common name in two formulas:
C() C() R() R() I() I().
K-relevance: there exist formulas 0, 1,…, k such that
and 0,
0 and 1 ,
…, k and
are directly relevant.
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Semantic Relevance
• Relevance is measured by using semantic information of data.
• Selection functions are defined in terms of Semantic Distance SD(x,y).
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Postulates for Semantic Distances
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Using Semantic Distances for Reasoning with Inconsistent Ontologies
• Google distances are used to develop semantic relevance functions to reason with inconsistent ontologies.
• Assumption: two concepts appear more frequently in the same web page, they are semantically more relevant.
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Google Distances (Cilibrasi and Vitanyi 2004)
• Google distance is measured in terms of the co-occurrence of two search items in the Web by Google search engine.
• Normalized Google Distance (NGD) is introduced to measure the similarity/light-weight semantic relevance
• NGD(x,y)= (max{log f(x), log f(y)}-log f(x,y))/(log M-min{log f(x),log f(y)}
where
f(x) is the number of Google hits for x
f(x,y) is the number of Google hits for the tuple of search items x and y
M is the number of web pages indexed by Google.
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Normalized Google Distances
• NGD(x, y) can be understood intuitively as a measure for the symmetric conditional probability of co-occurrence of the search terms x and y.
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Semantic Distances between two formulas
• Define semantic distances (SD) between two formulas in terms of semantic distances between two concepts/roles/individuals (NGD)
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Semantic Distances by NGD
Semantic distance are measured by the ratio of the summed distance of the difference between two formulae to the maximal distance between two formulae.
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Proposition
• The semantic distance SD satisfies the properties Range, Reflexivity, Symmetry, Maximum Distance, and Intermediate Values.
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Example: MadCow
NGD(MadCow, Grass)=0.7229
NGD(MadCow, Sheep)=0.6120
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Implementation: PION
PION: Processing Inconsistent ONtologies
http://wasp.cs.vu.nl/sekt/pion
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Answer Evaluation• Intended Answer (IA):
Query answer = Intuitive Answer • Cautious Answer (CA):
Query answer is ‘undetermined’, but Intutitve answer is ‘accepted’ or ‘rejected’.
• Reckless Answer (RA): Query answer is ‘accepted’ or ‘rejected’, but Intutive answer is ‘undetermined’.
• Counter Intuitive Answer (CIA): Query answer is ‘accepted’ but Intuitive answer is ‘rejected’, or vice versa.
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Syntactic approach vs. Semantic approach: quality of query answers
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Syntactic approach vs. Semantic approach: Time Performance
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Summary
• The run-time of the semantic approach is much better than the syntactic approach, while the quality remains comparable.
• The semantic approach can be parameterised so as to stepwise further improve the run-time with only a very small drop in quality.
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Summary (cont.)
• The semantic approach for reasoning with inconsistent ontologies trade-off computational cost for inferential completeness, and provide attractive scalability.
Questions?
Thank you for your attention!