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Inexact Matching of ontology graphs using expectation maximization Prashant Doshi, Ravikanth Kolli, Christopher Thomas Web Semantics: Science, Services.

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Presentation on theme: "Inexact Matching of ontology graphs using expectation maximization Prashant Doshi, Ravikanth Kolli, Christopher Thomas Web Semantics: Science, Services."— Presentation transcript:

1 Inexact Matching of ontology graphs using expectation maximization Prashant Doshi, Ravikanth Kolli, Christopher Thomas Web Semantics: Science, Services and Agents on the World Wide Web 2009 Keywords: ontology, matching, expectation- maximization Universidad Autónoma de Madrid -15 Enero 2010

2 Agenda  Introduction  Expectation Maximization  Ontology Schema Model  Graph Matching with GEM Random sampling and Heuristics Computational complexity Initial Results  Large ontologies  Benchmarks  Conclusions Universidad Autónoma de Madrid -15 Enero 2010

3 Introduction  Growing usefulness of semantic web based on the increasingly number of ontologies  OWL and RDF are labeled-directed-graph ontology representation languages  Formulation ‘Find the most likely map between the two ontologies’* Universidad Autónoma de Madrid -15 Enero 2010

4 Expectation Maximization  Technique to find the maximum likelihood estimate of the underlying model from observed data in the presence of missing data.  E-Step Formulation of the estimate  M-Step Search for the maximum of the estimate Relaxed search using: GEM Universidad Autónoma de Madrid -15 Enero 2010

5 Ontology Schema Model  OWL y RDF (labeled directed graphs)  Labels are removed, constructing a bipartite graph. Universidad Autónoma de Madrid -15 Enero 2010

6 Graph matching GEM  Maximum likelyhood estimate problem Hidden variables: mapping matrix  Local search guided by GEM Search-Space Universidad Autónoma de Madrid -15 Enero 2010

7 Graph matching GEM  M * gives the maximum conditional probability of the data graph O d given O m.  Only many-one matching Focused on homeomorphisms Universidad Autónoma de Madrid -15 Enero 2010

8 Graph matching GEM  MLE problem with respect to map hidden variables

9 Graph matching GEM  Need to maximize:

10 Graph matching GEM  Probability that x a is in correspondence with y a given the assignment model  Each of the hidden variables

11 Graph matching GEM  Graph constraints  And Smith-Waterman

12 Graph matching GEM  Exhaustive search not possible  Problem: local maxima  Use K random models + heuristics If two classes are mapped, map their parents + Random restart Universidad Autónoma de Madrid -15 Enero 2010

13 Computational complexity  SW technique is O(L 2 )  EM mapping is O(K*(|V m |*|V d |) 2 ) Universidad Autónoma de Madrid -15 Enero 2010

14 Initial Experiments Universidad Autónoma de Madrid -15 Enero 2010

15 Large Ontologies Universidad Autónoma de Madrid -15 Enero 2010

16 Benchmarks Universidad Autónoma de Madrid -15 Enero 2010

17 Conclusions  Structure and Syntactic vs External Resources Weak performance: dissimilar names and structure Good performance: extensions and flattening  Not scalable : partitioning and extension No longer GEM, but converges Future work: Markov Chain MonteCarlo methods Extensible algorithm: can include other aproaches Universidad Autónoma de Madrid -15 Enero 2010


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