Eswc2009
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Eswc2009 Presentation Transcript

  • 1. ReduCE: A Reduced Coulomb Energy Network Method for Approximate Classification Nicola Fanizzi Claudia d'Amato Floriana Esposito Dipartimento di Informatica Università degli studi di Bari ReduCE Fanizzi, d'Amato, Esposito
  • 2. Table of Contents Motivation Learning RCE Networks Approximate Classifications of Individuals Experiments Conclusions & Outlook ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 2
  • 3. Motivation Classic ML techniques for building inductive classifiers for SW representations explicit models: new concepts implicit models: neural networks, support vector machines, graphic probabilistic models Inductive methods for classification often more efficient and noise-tolerant than standard methods enables approximation better exploitation of the inherently incomplete information in Kbs for specific tasks ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 3
  • 4. Applications of Inductive Models Approximate instance-checking This can be also exploited for approximate retrieval subsumption ... It also provides alternative methods for ontology population Ultimately, may be used for completing ontologies with probabilistic assertions enabling more sophisticate approaches to dealing with uncertainty ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 4
  • 5. Learning Problem Given a target concept Train a model (hypothesis) hQ using: Set of pre-classified individuals: examples A knowledge base K as background knowledge then Use the learned model to classify all other individuals: Given hQ and x0 Output hQ(x0) and possibly the likelihood of this assertion ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 5
  • 6. Training Set A limited number of individuals for which the intended classification is known Hypothesis: the function to be approximated ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 6
  • 7. The Inductive Model: RCE Networks category Q ¬Q layer acj pattern λ1 λ2 λ3 ... λN (radii) layer wjk ... input x1 x2 xd layer ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 7
  • 8. Training the Model ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 8
  • 9. RCE Model Construction 1 2 3 ▪ ▪ ▪ ▪ ▪ ▪ 4 5 6 ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 9
  • 10. Similarity Measure Generalization of a pseudo-distance d [ESWC2008] ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 10
  • 11. RCE Final Model Prototypes (examples) ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 11
  • 12. (Vanilla) Classification Procedure ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 12
  • 13. Extensions generalizing the decision-making step: likelihood: ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 13
  • 14. Experiments For each ontology 100 satisfiable query concepts randomly generated by composition (conjunction / disjunction) of (2 through 8) primitive and defined concepts Evaluation comparing inductive responses to those returned by a standard reasoner (Pellet 2) Indices match rate: rate: identical classification omission error rate: rate: 0 vs. ±1 commission error rate: rate: +1 vs. -1 or -1 vs. +1 induction rate: rate: ±1 vs. 0 Several runs: rates averaged according to the 632+ bootstrap procedure ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 14
  • 15. Experiments: Ontologies Ontologies employed in the experiments: ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 15
  • 16. Outcomes ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 16
  • 17. Outcomes / 2 ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 17
  • 18. Outcomes / 3 ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 18
  • 19. Conclusions & Outlook ML method Extensions transposed to SeWeb force binary response representations Expected to augment induction rate Experiments: Pre-computation of good performance prototypical individuals: High match rate Medoids from Low induction rate Clustering Some omissions Use likelihood for Very limited ranking commissions adding probabilities to Low variance wrt to assertions past inductive methods ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 19
  • 20. The End Questions ? For offline contacts: Nicola Fanizzi fanizzi@di.uniba.it Claudia d'Amato claudia.damato@di.uniba.it Floriana Esposito esposito@di.uniba.it Other methods / systems http://lacam.di.uniba.it:8000/~nico/research/ontologymining.html ESWC 2009 ReduCE Fanizzi, d'Amato, Esposito 20