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Ontology Learning Tools: A Survey of Existing Tools

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Rule/template base knowledge extraction. Workbench tools allow the use of multiple algorithms ... represented in the input ontology, taxonomy or knowledge base ... – PowerPoint PPT presentation

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Title: Ontology Learning Tools: A Survey of Existing Tools


1
Ontology Learning Tools A Survey of Existing
Tools
  • Patrick Cash

2
Outline
  • Ontology learning
  • Ontology learning from text
  • Learning from text alone
  • Learning from text and other resources
  • Ontology learning from structured data
  • Learning from a machine readable dictionary
  • Learning from existing ontologies
  • Conclusion

3
Ontology Learning
  • Ontology
  • An explicit formal specification of a shared
    conceptualization of a domain
  • Facilitates knowledge sharing and machine
    understanding of knowledge
  • Manually building ontologies is a tedious task
    that becomes a bottleneck to knowledge
    acquisition
  • Ontology learning techniques were created to
    address this problem

4
Ontology Learning
  • Uses machine learning and other AI techniques to
    learn ontology structure from input data
  • Fully automatic ontology learning remains in the
    distant future
  • Most tools are semi-automatic and require human
    (expert) intervention using cooperative learning
    approaches for ontology building

5
Ontology Learning from Text
  • Learning from text alone
  • Natural language processing of the input text to
    find lexical and syntactic structure
  • Machine learning algorithms used to derive
    ontology structure out of this structure
  • Clustering using user supplied similarity
    measure
  • Rule/template base knowledge extraction
  • Workbench tools allow the use of multiple
    algorithms

6
Ontology Learning from Text
  • Problems with these tools
  • Requires large amounts of user interaction in
    validation
  • Cooperative learning approach
  • Not practical for large scale ontologies
  • Requires hand created rules and operators
  • Requires large amount of high quality input for
    domain coverage and concept learning

7
Ontology Learning from Text
  • Learning from text and other resources
  • Natural language processing of the input text to
    find structure and extract domain keywords
  • Machine learning algorithms used to derive
    ontology structure out of the lexical and
    syntactic structure
  • Clustering, Rule/template base techniques
  • Uses structure in input ontologies like WordNet
  • Uses collocation and other attributes from the
    input ontology to form the new ontology

8
Ontology Learning from Text
  • Problems with these tools
  • Many of the systems are rule based and require
    many hand created rules
  • Not practical for large ontologies
  • Depends on enough of the ontology domain being
    represented in the input ontology, taxonomy or
    knowledge base
  • Assumption will often not hold for technical or
    specialized domains

9
Ontology Learning from Structured Data
  • Learning from a machine readable dictionary
  • Takes as input a machine readable dictionary and
    a set of domain keywords
  • Uses structure in machine readable dictionary
  • Creates initial ontology using information from
    machine readable dictionary
  • Adds domain keywords to the initial ontology
  • Trims initial ontology to create a domain
    specific ontology

10
Ontology Learning from Structured Data
  • Problems with these tools
  • Depend on enough of the ontology domain being
    represented in the machine readable dictionary
  • Assumption will often not hold for technical or
    specialized domains
  • Matching to dictionary is based only on simple
    text or regular expression matching

11
Ontology Learning from Structured Data
  • Learning from existing ontologies
  • Takes as input an existing ontology and enhances
    it
  • Uses classification techniques of the input data
    to determine where it is added to the input
    ontology
  • Supervised learning techniques with labeled
    training data
  • Takes as input two or more ontologies and
    translates or merges them by mapping between them
  • Simple text matching or mapping rules
  • Iterative labeling using statistical machine
    learning
  • Uses classifiers to map instances from one
    ontology into the other

12
Ontology Learning from Structured Data
  • Problems with these tools
  • Makes several assumptions about the structure of
    the input data
  • Several of the tools are based on extraction of
    knowledge from HTML pages with specific
    structures (ex. Forms)
  • Requires large amount of upfront work done by
    information provider
  • Translation into a tool specific representation
  • Explicit mapping of input datas structure using
    schemas and other hooks into the input knowledge
    base

13
Conclusion
  • Selecting an ontology learning tool
  • Types of input available
  • Types of knowledge learned
  • Combined workbench/framework
  • The most robust tools are workbench or framework
    based using a modular architecture so that
    different learning techniques can be used in
    different use cases
  • A common representation will be needed for tools
    to work together

14
Conclusion
  • Ontology Validation
  • Approaches using in current research
  • Validate against a gold standard ontology created
    by an expert
  • Not practical for large ontologies
  • Using machine learning validation techniques to
    validate the learned ontology
  • Precision ratio of relevant terms retrieved over
    the entire number of terms in the ontology
  • Recall ratio of relevant terms retrieved over
    the entire number of relevant terms

15
Conclusion
  • Ontology learning techniques are still a long way
    from a fully automatic solution
  • Ontology learning techniques are necessary to
    make wide spread ontology use and the
    possibilities that entails practical
  • Tools that implement these techniques in a user
    friendly way are necessary for making these
    techniques available to non-expert users creating
    ontologies

16
  • Questions ?
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