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Discovering Ontological Semantics using FCA and SOM

Discovering Ontological Semantics using FCA and SOM. Advisor ： Dr. Hsu Reporter ： Wen-Hsiang Hu Author ： Ching-Chieh Kiu and Chien-Sing Lee. M 2 USIC 2004. Outline. Motivation Objective Formal Concept Analysis (FCA) Ontology SOM Experiments Conclusion. Motivation.

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Discovering Ontological Semantics using FCA and SOM

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  1. Discovering Ontological Semantics using FCA and SOM Advisor ：Dr. Hsu Reporter：Wen-Hsiang Hu Author：Ching-Chieh Kiu and Chien-Sing Lee M2USIC 2004

  2. Outline • Motivation • Objective • Formal Concept Analysis (FCA) • Ontology • SOM • Experiments • Conclusion

  3. Motivation • Ontologies enable more intelligent interchange and reuse of data over the Internet. • However, not all applications use the same ontological model. Different sites have different contexts and different definitions for the concept. • This has caused the ontology interoperability problems to occur between sites.

  4. Objective • We use Formal Concept Analysis (FCA) and Self-Organizing Map (SOM) to discover and visualize the intrinsic relationship between ontological concepts.

  5. Formal Concept Analysis (1/2) • Formal Concept Analysis (FCA) is a conceptual clustering tool used for data analysis • In a formal concept k = (G, M, I) where G are objects, M are attributes and I is a binary relation between G and M. • The set of all formal concepts k is called concept lattice and is denoted by βk. objects relation attributes

  6. Formal Concept Analysis (2/2) • Concept lattice is the structured graph depicted according to the context (Figure 2).

  7. Ontology • Figure 1 illustrates the author and person concepts for the newspaper ontology obtained from Protégé.

  8. Self-Organizing Map • Visualization of the SOM can be represented using the U-matrix (unified distance matrix) method.

  9. Experiments (1/4) • Data • The ontology used in the experiment was a newspaper ontology obtained using Protégé. • matrix 26 * 72 • 26 concepts and 72 corresponding attributes were excerpted from the newspaper ontology using FCA

  10. Experiments (2/4) • FCA • All the concepts have the attributes of phone numbers and other information excluding the Author and News_Service concept. • Multiple inheritances of concepts can be easily viewed via the hierarchical structure of the concepts

  11. Experiments (3/4) • SOM • “Manager, Director, Columnist, Editor and Reporter” are grouped together to form a cluster and “Advertisement, Article, PersonalsAd and StandardAd” are grouped together to form another cluster.

  12. Experiments (4/4) • FCA • Advantages • The subconcept and superconcept relationships are clearly represented by the hierarchical structure. • Disadvantages • FCA is not viable to visualize the large ontology • SOM • Advantages • explain the concepts in the clusters are having the common attributes • Disadvantages • it is unable to visualize the inheritance relationship in between.

  13. Conclusion • We have presented the advantage of the FCA and SOM tools used for discovering and visualizing ontological semantics.

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