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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. Abstract. Information on the Web. Unstandable to Human.

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learning to extract symbolic knowledge from the world wide web

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

abstract
Abstract

Information on

the Web

Unstandable to Human

Knowledgable

????

Extract information

KB

Changho Choi, University at Buffalo

introduction 1 4
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

slide4

Classes

Relations : value

Changho Choi, University at Buffalo

introduction 3 4
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

introduction 4 4
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

experimental testbed
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

statistical text classification
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

statistical text classification9
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

statistical text classification10
Statistical Text Classification
  • Experimental evaluation

Changho Choi, University at Buffalo

accuracy coverage
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

accuracy coverage tradeoff
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

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