IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 21, NO. 11, NOVEMBER 2009
By.  P. Victer Paul   Dear, We planned to share our eBooks and project/seminar contents for free to all needed friends lik...
Authors <ul><li>Amal Zouaq,Member, IEEE </li></ul><ul><li>Roger Nkambou, Member, IEEE </li></ul><ul><li>University of Queb...
Ontology <ul><li>O= (C;R; A; Top) </li></ul><ul><li>C represents a non-empty set of concepts (including relation concepts ...
Knowledge Puzzle Project <ul><li>The Knowledge Puzzle, an ontology-based platform designed to facilitate domain knowledge ...
Why Automatic methods for Domain Ontologies? <ul><li>ONTOLOGIES are the backbone of knowledge representatio for the Semant...
Focussed Problems <ul><li>domain ontology learning and population from text  </li></ul><ul><ul><li>paper proposes a lexico...
<ul><li>domain ontology evaluation techniques. </li></ul><ul><ul><li>Structural: Based on a set of metrics, structural eva...
Metrics <ul><li>The CMM evaluates the coverage of an ontology for the given sought terms. </li></ul><ul><li>The density me...
Related Work <ul><li>M. Poesio and A. Almuhareb, “Identifying Concept AttributesUsing a Classifier,” Proc. Assoc. Computat...
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Evaluating the Generation of Domain ontologies in knowledge puzzle project

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Evaluating the Generation of Domain ontologies in knowledge puzzle project

  1. 1. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 21, NO. 11, NOVEMBER 2009
  2. 2. By. P. Victer Paul Dear, We planned to share our eBooks and project/seminar contents for free to all needed friends like u.. To get to know about more free computerscience ebooks and technology advancements in computer science. Please visit.... http://free-computerscience-ebooks.blogspot.com/ http://recent-computer-technology.blogspot.com/ http://computertechnologiesebooks.blogspot.com/ Please to keep provide many eBooks and technology news for FREE. Encourage us by Clicking on the advertisement in these Blog.
  3. 3. Authors <ul><li>Amal Zouaq,Member, IEEE </li></ul><ul><li>Roger Nkambou, Member, IEEE </li></ul><ul><li>University of Quebec at Montreal, </li></ul><ul><li>Montre´al, Canada </li></ul><ul><li>E-mail: {zouaq.amal, nkambou.roger}@uqam.ca </li></ul>
  4. 4. Ontology <ul><li>O= (C;R; A; Top) </li></ul><ul><li>C represents a non-empty set of concepts (including relation concepts and Top) </li></ul><ul><li>R the set of assertions in which two or more concepts are related to one another </li></ul><ul><li>A the set of axioms </li></ul><ul><li>Top the highest level concept in the hierarchy. </li></ul><ul><li>R, itself, includes two subsets: </li></ul><ul><ul><li>H depicts the set of assertions for which relations are taxonomic </li></ul></ul><ul><ul><li>N denotes those which are nontaxonomic </li></ul></ul>
  5. 5. Knowledge Puzzle Project <ul><li>The Knowledge Puzzle, an ontology-based platform designed to facilitate domain knowledge acquisition for knowledge-based systems and especially for intelligent tutoring systems. </li></ul><ul><li>One of the Goals of the Knowledge Puzzle Project is to automatically generate a domain ontology from plain text documents and use this ontology as the domain model in computer-based education. </li></ul><ul><li>TEXCOMON, the Knowledge Puzzle Ontology Learning Tool, to extract concept maps from texts. It also explains how these concept maps are exported into a domain ontology </li></ul>
  6. 6. Why Automatic methods for Domain Ontologies? <ul><li>ONTOLOGIES are the backbone of knowledge representatio for the Semantic Web. </li></ul><ul><li>manual methods used to build domain ontologies are not scalable. </li></ul><ul><li>time- and effort-consuming </li></ul><ul><li>represent knowledge as a set structure established at the time the ontology was conceived and built. </li></ul><ul><li>To minimize these drawbacks, automatic methods for domain ontology building must be adopted. </li></ul>
  7. 7. Focussed Problems <ul><li>domain ontology learning and population from text </li></ul><ul><ul><li>paper proposes a lexico-syntactic analysis </li></ul></ul><ul><ul><ul><li>to extract concept maps from texts and transform them into a domain ontology in a semiautomatic manner. </li></ul></ul></ul><ul><ul><ul><li>proposes a set of domain-independent patterns relying on dependency grammar. work differsfrom the existing techniques by the proposed patterns andthe methods used to transform instantiated patterns into semantic structures. </li></ul></ul></ul><ul><ul><li>aims to discover:domain terms, concepts, concept attributes, taxonomic relationships, nontaxonomic relationships, axioms, and rules. </li></ul></ul>
  8. 8. <ul><li>domain ontology evaluation techniques. </li></ul><ul><ul><li>Structural: Based on a set of metrics, structural evaluations consider ontologies as graphs. structural metrics are the Class Match Measure (CMM), the Density Measure (DEM), the Betweenness Measure (BEM), and finally, the Semantic Similarity Measure (SSM). </li></ul></ul><ul><ul><li>Semantic: rely on human expert judgment </li></ul></ul><ul><ul><li>Comparative: based on comparisons between the outputs of state-of-the-art tools and those of new tools such as TEXCOMON, using the very same set of documents inorder to highlight the improvements of new techniques </li></ul></ul>
  9. 9. Metrics <ul><li>The CMM evaluates the coverage of an ontology for the given sought terms. </li></ul><ul><li>The density measure expresses the degree of detail or the richness of the attributes of a given concept. </li></ul><ul><li>The BEM calculates the betweenness value of each search term in the generated ontologies </li></ul><ul><li>the SSM, computes the proximity of the classes that match the sought terms in the ontology. </li></ul>
  10. 10. Related Work <ul><li>M. Poesio and A. Almuhareb, “Identifying Concept AttributesUsing a Classifier,” Proc. Assoc. Computational Linguistics (ACL)Workshop Deep Lexical Acquisition, pp. 18-27, 2005. </li></ul><ul><li>P. Hayes, T. Eskridge, R. Saavedra, T. Reichherzer, M.Mehrotra, and D. Bobrovnikoff, “Collaborative KnowledgeCapture in Ontologies,” Proc. Third Int’l Conf. Knowledge Capture (K-CAP ’05), pp. 99-106, 2005. </li></ul><ul><li>D. Lin and P. Pantel, “Discovery of Inference Rules for Question Answering,” Natural Language Eng., vol. 7, no. 4, pp. 343-360, 2001. </li></ul><ul><li>Text to onto </li></ul>
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