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

ISWC 2007 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)

ISWC 2007 Overview Use-case Instance-based mapping Evaluation Experiments Results Conclusions

ISWC 2007 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

ISWC 2007 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

ISWC 2007 Overview Use-case Instance-based mapping Evaluation Experiments Results Conclusions

ISWC 2007 Calculating mappings using Concept Extensions how much are they related?

ISWC 2007 Standard approach (Jaccard) Use co-occurrence measure to calculate similarity between 2 concepts: e.g. B G Elements of B Elements of G Joint Elements Similarity = 5/9 = 55 % (overlap, e.g. Degree of Greenness )Similarity = 1/7 = 14 % (overlap, e.g. Degree of Greenness ) Set of books in the library

ISWC 2007 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 = 18/21 = 86 % Jacc = 1/1 = 100 % The second solution is worse: b B = {MemberOfParliament} and b G = {Cricket}

ISWC 2007 Issue with measure (hierarchy): B G Non hierarchical Set of books in the library · Hierarchical Elements B’ Jacc(B’,G) = ½ = 50% Jacc(B’,G) = 2/6 = 33% Consider a hierarchy

ISWC 2007 An empirical study of instance-based OM We experimented with three dimensions Similarity measure Threshold Hierarchy Jaccard Corrected Jaccard Pointwise Mutual Information Log Likelihood Ratio Information Gain 0 10 Yes No Why only 2 thresholds? Because of evaluation costs!

ISWC 2007 Overview Use-case Instance-based mapping Evaluation Experiments Results Conclusions

ISWC 2007 Evaluation: building a gold standard GTT Brinkman Possible Thesaurus relations (~ SKOS)

ISWC 2007 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.

ISWC 2007 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

ISWC 2007 Evaluation: validity of the (different) methods Answer is: yes All evaluations produce the same results (in different scales)

ISWC 2007 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.

ISWC 2007 Overview Use-case Instance-based mapping Evaluation Experiments Results Conclusions

ISWC 2007 Experiments: Setup, Data and Thesauri We calculated 5 different similarity measures with Threshold: 0 and 10 Hierarchy: yes or no. Based on on GTT concepts with Brinkman concepts based on books with double annotations

ISWC 2007 Experiments: Result calculation Average precision at similarity position i: P i = N good,i /N i (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% 798 th mapping 86 %

ISWC 2007 Overview Use-case Instance-based mapping Evaluation Experiments Results Conclusions

ISWC 2007 Results: Three research questions 1.What is the influence of the choice of threshold? 2.What is the influence of hierarchical information? 3.What is the best measure and setting for instance-based mapping?

ISWC 2007 What is the influence of the choice of threshold? Threshold needed for Jaccard Threshold NOT needed for LLR

ISWC 2007 What is the influence of hierarchical information? Results are inconclusive!

ISWC 2007 Best measure and setting for instance-based mapping? 10 We have two winners! The corrected Jaccard measures

ISWC 2007 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

ISWC 2007 Thank you.

ISWC 2007 Similarity measures Formulae Jaccard: Corrected Jaccard: assign a smaller score to less frequently co-occurring annotations.

ISWC 2007 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