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Learning to Extract Symbolic Knowledge from the World Wide Web

Learning to Extract Symbolic Knowledge from the World Wide Web

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Learning to Extract Symbolic Knowledge from the World Wide Web

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  1. Learning to Extract Symbolic Knowledge from the World Wide Web Changho Choi Source: http://www.cs.cmu.edu/~knigam/ Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum Carnegie Mellon University, J.Stefan Institute AAAI-98

  2. Abstract Information on the Web Unstandable to Human Knowledgable ???? Extract information KB Changho Choi, University at Buffalo

  3. Introduction (#1/4) • Two types of inputs of the information extraction system • Ontology • Specifying the classes and relations of interest • For example, a hierarchy of classes including Person, Student, Research.Project, Course, etc. • Training examples • Represent instances of the ontology classes and relations • For example, a course web page for Course classes, faculty web pages for Faculty classes, this pair of pages for Courses.Taught.By, etc. Changho Choi, University at Buffalo

  4. Classes Relations : value Changho Choi, University at Buffalo

  5. Introduction (#3/4) • Assumptions • about the mapping between the ontology and the Web 1. Each instance of an ontology class is • a single Web page, • a contiguous string of text, • or a collection of several Web pages. 2. Each instance of a relation is • a segment of hypertext, • a contiguous segment of text, • or t he hypertext segment. Changho Choi, University at Buffalo

  6. Introduction (#4/4) • Three primary learning tasks • Involved in extracting knowledge-base instances for the Web 1. Recognizing class instances by classifying bodies. 2. Recognizing relation instances by classifying chains of hyperlinks. 3. Recognizing class and relation instances by extracting small fields of text form Web pages. Changho Choi, University at Buffalo

  7. Experimental Testbed • Experiments • Based on the ontology • Classes:Department, faculty, staff, student, research_project, course, other • Relations: Instructors.Of.Course(251), Members.Of.Project(392), Department.Of.Person(748) • Data sets • A set of pages(4127) and hyperlinks(10945) from 4 CS dept. • A set of pages(4120) from numerous other CS dept. • Evaluation • Four-fold cross validation • 3 for training, 1 for testing Changho Choi, University at Buffalo

  8. Statistical Text Classification • Process • building a probabilistic model of each class using labeled training data • Classifying newly seen pages by selecting the class that that is most probable given the evidence of words describing the new page. • Train three classifiers • Full-text • Title/Heading • Hyperlink Changho Choi, University at Buffalo

  9. Statistical Text Classification • Approach • the naïve Bayes, with minor modifications • Based on Kullback-Leibler Divergence • Given a document d to classify, we calculate a score for each class c as follows: Changho Choi, University at Buffalo

  10. Statistical Text Classification • Experimental evaluation Changho Choi, University at Buffalo

  11. Accuracy/coverage • Coverage • The percentage of pages for a given class that are correctly classified as belonging to the class • accuracy • The percentage of pages classified into a given class that are actually members of that class Changho Choi, University at Buffalo

  12. Accuracy/coverage tradeoff 1. Full-text classifiers 2. Hyperlink classifiers 3. Title/heading classifiers “Hyperlink information can provide strong knowledge.” Changho Choi, University at Buffalo