Measuring Similarities between Ontologies Presentation at EKAW, Sigüenza, October 2002

1. Measuring Similarities between Ontologies Presentation at EKAW, Sigüenza, October 2002 Alexander Maedche FZI at the University of Karlsruhe Research Group WIM http://www.fzi.de/wim Steffen Staab Institute AIFB University of Karlsruhe http://www.aifb.uni-arlsruhe.de/

2. Agenda • Introduction • A Two-Layer View for Comparing Ontologies • Empirical Evaluation • Conclusion

3. Introduction • Ontologies now play an important role for many knowledge-intensive applications. • It is widely agreed that it will take quite a long time when we will have standard ontologies for specific applications and/or specific domains. • Thus, this results in a world where we have to deal with many ontologies being available. Typically, these ontologies will share some similar elements.

4. The Need for Similarity Measures • Measuring similarity between ontologies or parts of ontologies is an important technique in a multi-ontology world. • E.g. similarity measures are required in the following scenarios: • Search for ontologies • Reuse of ontologies • Mapping between ontologies • Similarity between ontologies is not pure graph matching!

5. Agenda • Introduction • A Two-Layer View for Comparing Ontologies • Empirical Evaluation • Conclusion

6. A Two-Layer View • Real-world ontologies consists of tow parts • Lexicon: Words within the lexicon refer to „concepts“ and conceptual relations. • Concept System: Concepts are formally represented and embedded in the concept system via relations (taxonomy, non-taxonomic relations) and axioms. • Thus, when trying to measure the similarity between two ontologies we have to pursue a two-layer view: • First, we have to deal with the lexical layer. • Second, we have to deal with the conceptual layer.

7. Conceptual Layer Lexical Layer taxonomic relations … person student researcher c4 c1 project research project c2 c5 c3 works in … non-taxonomic relations Example

8. Lexical Layer • Levensthein Edit Distance: • Well-established method for measuring the distance between two strings. • Measures the minimum number of token insertations, substitutions and deletions to transform one string into another using a dynamic programming approach. • Example: ed(TopHotel, Top Hotel) = 1 • Lexical similarity measure: SM

9. Conceptual Layer • Within this layer we focus on the conceptual structures of the ontologies, namely taxonomic and non-taxonomic relations. • Approach for measuring the similarity between two taxonomies: • Determine the extent two ontologies compare as seen from two particular identified concepts. • Average over all concepts to compute a semantic similarity • Approach for measuring the similarity between the set of non-taxonomic relations: • Compute for a given non-taxonomic relation the relation match based on domain and range concepts. • Average over all non-taxonomic relations

10. Taxonomic Relation Similarity … … accomodation youth hostel a4 c4 c1 hotel a1 area city c2 c3 c5 a5 wellness hotel a2 … … Concepts referring to „hotel“ in O1 and O2: Semantic cotopy O1: {hotel, accomodation} Semantic cotopy O2: {hotel, wellness hotel} => Taxonomic Overlap: 1/3

11. Non-Taxonomic Relation Similarity … … accomodation youth hostel located at a4 c4 c1 hotel a1 area city c2 c3 c5 a5 wellness hotel a2 … … Relation referred by „located at“ in O1 and O2: Concept Match Domain „hotel“/“hotel“: 0.5 Concept Match Range „area“/“city“: 0.5 => Relation Overlap:

12. Agenda • Introduction • A Two-Layer View for Comparing Ontologies • Empirical Evaluation • Conclusion

13. Empirical Evaluation Scenario • Domain: Tourism • Three step approach: • I: Based on a set of documents build ontology. • II: Based on a given lexicon, develop an ontology. • III: Based on a given taxonomy, develop an ontology. • The number of lexical entries, concepts, taxonomic and non-taxonomic relations that had to be modeled was predefined. • Resulted in 12 ontologies. Additional, one “expert modeler” developed one ontology for this domain (based on the set of documents). • Four undergraduate students within an ontology engineering seminar.

14. Results (I) • Lexical layer: • Human Subjects have a considerable higher agreement on lexical entries referring to concepts than lexical entries referring to relations. • Lexical structures correlate with conceptual structures. • Conceptual Layer • Human subjects tend to agree or disagree on taxonomic structures irrespective of the amount of material being defined (Phase I and II) • Taxonomy development is easier than defining non-taxonomic relations.

15. Results (II) – Conceptual Layer • Subjects find it easy to build on a pre-defined lexicon. • Subjects find it extremely difficult to build on a predefined taxonomy • There is an overall correlation between the agreements on the lexical and the conceptual layer.

16. Agenda • Introduction • A Two-Layer View for Comparing Ontologies • Empirical Evaluation • Conclusion

17. Conclusion • In a multi-ontology world we need means for measuring similarity between two given ontologies. • Within real-world ontologies we have to deal with lexical and conceptual structures. • In this paper we presented a two-layer approach for measuring similarity between two ontologies

18. Thanks! Any questions? Alexander Maedche FZI at the University of Karlsruhe Research Group WIM Steffen Staab Institute AIFB University of Karlsruhe