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Result of Ontology Alignment with RiMOM at OAEI’06

Result of Ontology Alignment with RiMOM at OAEI’06. Yi Li , Juanzi Li, Duo Zhang, Jie Tang Knowledge Engineer Group Tsinghua University Nov. 5 th 2006. Outline. RiMOM Principles Process Similarity Factor Calculation Multiple Strategy Execution Similarity Propagation Results refinement

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Result of Ontology Alignment with RiMOM at OAEI’06

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  1. Result of Ontology Alignment with RiMOM at OAEI’06 Yi Li, Juanzi Li, Duo Zhang, Jie Tang Knowledge Engineer Group Tsinghua University Nov. 5th 2006

  2. Outline • RiMOM Principles • Process • Similarity Factor Calculation • Multiple Strategy Execution • Similarity Propagation • Results refinement • Evaluation Results • Conclusions

  3. RiMOM -- Risk Minimization based Ontology Mapping • Multiple strategies using different types of linguistic information • Propagation using structural information • Strategy selection for different alignment tasks • Refinement using a priori knowledge

  4. Processing Flow

  5. Multiple Linguistic Strategies • Edit distance on entity’s label • KNN on entity’s description and instances’ text • Add some structural features

  6. Similarity Propagation Ontology 1 Ontology 2 The construction of an intermediate graph from original ontologies

  7. Similarity Propagation (cont.) • Propagate similarities along edges • Three types of edges: • Class to Class (CCP) • Class to Property (CPP) • Property to Property (PPP) 0.7 weight=0.5 0.3 0.6 0.5 0.2 0.6+0.7*0.5+0.9*0.5=1.4 0.9

  8. Strategy Selection—Similarity factor Ontology 1 Ontology 2 • Label similarity factor • Structure similarity factor F_LS = 6/10 F_SS = 1/2 max(#c1, #c2) = 10 max(#nonleaf_c1, #nonleaf_c2) = 2

  9. Strategy Selection • Strategy Selection • Selection with the two similarity factors • Determining whether a strategy is to be used in the alignment process • E.g. if F_SS>0.25, we use CCP, CPP, and PPP for propagation. … • Linguistic Strategy • Adding structural features in KNN

  10. Refinement • Using heuristic rules • Remove the alignments of external and anonymous entities (basic refinement) • Remove “Unbelievable” alignments • Indistinguishable entities • Pick up 1:1 alignments • …

  11. Outline • RiMOM Principles • Process • Similarity Factor Calculation • Multiple Strategy Execution • Similarity Propagation • Results refinement • Evaluation Results • Conclusions

  12. Evaluation Results • Benchmark task

  13. Analysis of the Evaluation Results • Linguistic (with KNN new features) • P: 0.94 R:0.77 • Linguistic + Propagation + Refinement • P: 0.89 R: 0.83 • Our Approach • P: 0.96 R:0.88

  14. Other Evaluation Results • Food task • Directory task • Prec: 39.25%, Rec: 40.40%, F: 39.82% • Conference task • Prec: 38%, Rec: 62%

  15. Conclusions • Implemented multiple strategies for ontology alignment • Proposed utilizing strategy selection for different alignment tasks • Our approach can improve the accuracy of ontology alignment effectively

  16. THANK YOU! http://keg.cs.tsinghua.edu.cn/project/RiMOM

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