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An Empirical Study of Instance-Based Ontology Mapping

An Empirical Study of Instance-Based Ontology Mapping. Antoine Isaac, Lourens van der Meij, Stefan Schlobach , Shenghui Wang STITCH@CATCH funded by NWO Vrije Universiteit Amsterdam Koninklijke Bibliotheek Den Haag Max Planck Instutute Nijmegen. Metamotivation. Ontology mapping in practise

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An Empirical Study of Instance-Based Ontology Mapping

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  1. An Empirical Study of Instance-Based Ontology Mapping Antoine Isaac, Lourens van der Meij, Stefan Schlobach, Shenghui Wang STITCH@CATCH funded by NWO Vrije Universiteit Amsterdam Koninklijke Bibliotheek Den Haag Max Planck Instutute Nijmegen

  2. Metamotivation • Ontology mapping in practise • Based on real problems in the host institution at the Dutch Royal Library • Task-driven • Annotation support • Merging of thesauri • Real thesauri (100 years of tradition) • Really messy • Conceptually difficult • Inexpressive • Generic Solutions to Specific Questions & Tasks • Using Semantic Web Standards (SKOSification)

  3. Overview • Use-case • Instance-based mapping • Evaluation • Experiments • Results • Conclusions

  4. The Alignment Task: Context • National Library of the Netherlands (KB) • 2 main collections • Legal Deposit: all Dutch printed books • Scientific Collections: history, language… • Each described (indexed) by its own thesaurus

  5. A need for thesaurus mapping • The KB wants • (Scenario 1) Possibly discontinue one of both annotation and retrieval methods. • (Scenario 2) Possibly merge the thesauri • We try to explore mapping • (Task 1) In case of single/new/merged retrieval system, find books annotated with old system, facilitated by using mappings • (Task 2) Candidate terms for merged thesaurus • We make use of the doubly annotated corpus to calculate Instance-Based mappings

  6. Overview • Use-case • Instance-based mapping • Evaluation • Experiments • Results • Conclusions

  7. how much are they related? Calculating mappings using Concept Extensions

  8. Standard approach (Jaccard) • Use co-occurrence measure to calculate similarity between 2 concepts: e.g. Elements of B B G Elements of G Joint Elements Set of books in the library Similarity = 5/9 = 55 % (overlap, e.g. Degree of Greenness ) Similarity = 1/7 = 14 % (overlap, e.g. Degree of Greenness )

  9. Issues with this measure (sparse data) • What is more reliable? • We need • more reliable measures • Or thresholds (at least n doubly annotated books) Or ? Jacc = 1/1 = 100 % Jacc = 18/21 = 86 % The second solution is worse: bB = {MemberOfParliament} and bG = {Cricket}

  10. Issue with measure (hierarchy): Consider a hierarchy Jacc(B’,G) = ½ = 50% B’ Jacc(B’,G) = 2/6 = 33% · G B Non hierarchical Hierarchical Elements Set of books in the library

  11. An empirical study of instance-based OM • We experimented with three dimensions Jaccard Corrected Jaccard Pointwise Mutual Information Log Likelihood Ratio Information Gain 0 10 Similarity measure Threshold Yes No Hierarchy Why only 2 thresholds? Because of evaluation costs!

  12. Overview • Use-case • Instance-based mapping • Evaluation • Experiments • Results • Conclusions

  13. Evaluation: building a gold standard Possible Thesaurus relations (~ SKOS) GTT Brinkman

  14. User Evaluation Statistics • 3 evaluators with 1500 evaluations • 90% agreement ONLYEQ • If some evaluator says "equivalent", 73% of other evaluators say the same • Comparing two evaluators, correspondence in assignment is best for equivalence, followed by "No Link", "Narrower than", "Broader than", at or above 50% agreement, "Related To" has 35% agreement. • There are correlations between evaluators. • For example, Ev1 and Ev2 agreed much more on saying that there is no link than the Ev3.

  15. Evaluation Interpretation: What is a good mapping? • Is use case specific. We considered: • ONLYEQ: Only Equivalent answer → correct • NOTREL: EQ, BT,NT → correct • ALL: EQ, BT, NT, RT → correct ONLYEQ NOTREL ALL • The question is obviously: do they produce the same results

  16. Evaluation: validity of the (different) methods Answer is: yes All evaluations produce the same results (in different scales)

  17. A remark about Evaluation • Use of mappings strongly task dependant • Scenario 1 (legacy data/annotation support) and Scenario 2 (thesaurus merging) require different mappings. • Our evaluation is useful (correct) for Scenario 2 (intensional) • Scenario 1 can be evaluated differently (e.g. cross-validation on test-data) • See our paper at the Cultural Heritage Workshop.

  18. Overview • Use-case • Instance-based mapping • Evaluation • Experiments • Results • Conclusions

  19. Experiments: Setup, Data and Thesauri • We calculated • 5 different similarity measures with • Threshold: 0 and 10 • Hierarchy: yes or no. • Based on on • 24.061 GTT concepts with • 4.990 Brinkman concepts based on • 243.886 books with double annotations

  20. Experiments: Result calculation • Average precision at similarity position i: • Pi = Ngood,i/Ni (take the first i mappings, and return the percentage of correct ones) Example: This means that from the first 798 mappings 86% were correct • Recall is estimated based on lexical mappings • F-measure is calculated as usual 100% 86 % 798th mapping

  21. Overview • Use-case • Instance-based mapping • Evaluation • Experiments • Results • Conclusions

  22. Results: Three research questions • What is the influence of the choice of threshold? • What is the influence of hierarchical information? • What is the best measure and setting for instance-based mapping?

  23. What is the influence of the choice of threshold? Threshold needed for Jaccard Threshold NOT needed for LLR

  24. What is the influence of hierarchical information? Results are inconclusive!

  25. Best measure and setting for instance-based mapping? We have two winners! 10 The corrected Jaccard measures

  26. Conclusion • Summary • About 80% precision at estimated 80% recall • Simple measures perform better, if statistical correction applied, (threshold or explicit statistical correction) • Hierarchical aspects unresolved • Some measures really unsuited • Future work: • Generalize results • Other use cases, web directories, … • Study other measures

  27. Thank you.

  28. Similarity measures Formulae • Jaccard: • Corrected Jaccard: assign a smaller score to less frequently co-occurring annotations.

  29. Information Theoretic Measures • Pointwise Mutual Information: • Measures the reduction of uncertainty that the annotation of one concept yields for the annotation with another concept. • -> disadvantage: inadequate for spare data • LogLikelihoodRatio: • Information Gain: • Information gain is the difference in entropy, • determine the attribute that distinguishes best between positive an negative example

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