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Using activation spreading for ontology merging Miłosław L. Frey FGAN – FKIE Neuenahrer Str. 20

Using activation spreading for ontology merging Miłosław L. Frey FGAN – FKIE Neuenahrer Str. 20 53343 Wachtberg, Germany m.frey@fgan.de. Overview. Ontology Definition From ontology to network The network General idea Learning Creating hierarchy Taxonomy merging General Method

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Using activation spreading for ontology merging Miłosław L. Frey FGAN – FKIE Neuenahrer Str. 20

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  1. Using activation spreading for ontology merging Miłosław L. Frey FGAN – FKIE Neuenahrer Str. 20 53343 Wachtberg, Germany m.frey@fgan.de RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  2. Overview • Ontology • Definition • From ontology to network • The network • General idea • Learning • Creating hierarchy • Taxonomy merging • General • Method • Summary RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  3. Ontology • Ontology (def.): In computational sciences an ontology is an explicit representation of knowledge in a given thematic domain. Ontology as a network From: Brachman, Schmolze, An Overview of the KL-ONE Knowledge Representation System, 1985 RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  4. Ontology • Ontology (def.): In computational sciences an ontology is an explicit representation of knowledge in a given thematic domain. Ontology as a network From: Brachman, Schmolze, An Overview of the KL-ONE Knowledge Representation System, 1985 RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  5. Internal structure of a node Spreading activation The node’s state depend on the other nodes’ activations, connections weights and time: each node defines a point in the multidimensional space described by features Learning (Sowa, 2002) Rote learning Connection weights change Restructuring Generalization Improves the taxonomy Allows for classification of unknown objects The network : general idea RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  6. The network: creating hierarchy by example (1) Input data One-dimensional example: 7 ellipses differentiated in the ratio of axes. RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  7. Just input data Final network: “discovery” and pruning The network: creating hierarchy by example (2) RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  8. Taxonomy merging: general • Taxonomy merging (by analogy to ontology merging) is a procedure of blending two or more taxonomies into a single one. • Two methods: • union (used in the method presented), • intersection. • For simplicity: • merging is regarded as completion: one taxonomy complements the other one. RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  9. (1) Starting taxonomies (2) Joining by features sharing Taxonomy merging: method (1) RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  10. (3) Restructuring (4) Pruning Taxonomy merging: method (2) RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  11. Summary Shown • Preliminary ideas • Connectionist method to join two taxonomies • Illustration by an artificial example Further work: • Apply to real-world data • Extend to other than is-a relations • Make the method symmetrical (no need to identify the main root node) RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

  12. Thank you for your Attention • Questions and Comments • are appreciated RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

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