Chen Yirong, Lu Qin, Li Wenjie, Cui Gaoying

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Chinese Core Ontology Constructionfrom a
Bilingual Term Bank

• Chen Yirong, Lu Qin, Li Wenjie, Cui Gaoying
• Department of Computing
• The Hong Kong Polytechnic University

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Outline

• Introduction
• Related Works
• Algorithm Design COCA
• Performance Evaluation
• Conclusion

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Introduction

• What is a Core Ontology
• A mid-level ontology
• Bridges the gap between an upper ontology and a
  domain ontology

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Concepts and Terminologies

• Upper Ontology
• A general ontology to ensure reusability across
  different domains (e.g. Computer Program in
  SUMO)
• Domain Ontology
• An ontology conceptualize a specific domain
  (e.g. Free Software in IT domain)
• More application dependent, more extents of
  concepts
• Midlevel Ontology(Core Concept)
• Basic concepts of a domain
• More application independent, more intents of
  concepts.
• core ontology (e.g. Software)
• Frequently used, ability to form other concepts
• Core Terms
• Lexical units of core concepts

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Related Works

• Manually constructed ontologies
• SUMO
• Famous upper level ontology works based on
  lexicon
• CoreLex (Buitelaar, P., 1998)
• EuroWordnet (Rodríguez, 1998 )
• Ontology harmonization Core ontology
• Towards a Core Ontology for Information
  Integration (M. Doerr, 2003)
• A most similar work
• Enriching Core Ontology with Domain Thesaurus
  through Concept and Relation Classification
  (Huang, 2007)
• Use Concept and Relation Classification to Enrich
  core ontology

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Our Previous Works

• Chinese terminology extraction
• Chinese core term extraction(Ji et al, 2007)
• Preliminary work on automatic construction of
  core ontology construction using English-Chinese
  Term Bank (MRCOCA, Ontolex 2007, Chen, 2007)
• Bilingual lexicon
• Extended strings
• Frequency information in synset
• Weight from extended strings are integrated into
  final weight by simple addition
• Mapping to synset and SUMO can only achieve
  accuracy of about 50

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Issues

• What kind of concept should be included?
• How to identify core concepts
• If through core terms, disambiguation
• What and how to identify relations?
• Making use of available resources
• Chinese NLP resource scares
• English NLP resources abundant

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Requirements of Core Ontology

• The concepts must be widely accepted and commonly
  referenced
• Corresponding core terms must be highly used and
  productive
• The concepts/terms can be mapped to upper
  ontology. So the core ontology can inherit the
  attributes provided by upper ontology

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Core Ontology Construction Algorithm(COCA) for
Chinese

• Extract Chinese core terms from a bilingual term
  bank
• Mapped core term Tc to English terms
• Mapping English terms to WordNet
• Mapping synset to a upper ontology concept in
  SUMO

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COCA - Resources Used

• ITCTerm
• a domain specific core term list (Chen, 2007 )
• CETBank
• Chinese-English bilingual term bank
• 1,500 most productive core terms extracted can
  serve as suffixes to form more than 50 of the
  terms in CETBank)
• WordNet
• SUMO
• Mappings between WordNet and SUMO

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The Framework of COCA
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COCA Statistical Translation Module

• Translation ambiguity
• Each Chinese core term TC ? ITCTerm has a set
  of translations T_SetE , TE ?T_SetE
• Objective
• to estimate the likelihood of every translation
  using extended terms of TC
• P(TE TC) for all TE ? T_SetE.

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COCA - Sense Disambiguation Module

• Mapping a given TC to the Synset S through its
  translation set T_SetE (TC)
• Mapping probability of a English term TE to take
  a synset S using freq. info in WordNet
• Mapping probability of TC to take a particular
  synset S via an English translation TE

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COCA - Concept Selection Module

• Combining three features
• multi-path feature
• hypernyms feature
• part-of-speech feature
• Using Union Probability of Independent Events

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Feature 1 Multi-Paths to Synset

• Multiple paths is
• the path between Chinese core terms and synset
• via different English translations

The feature merges the probability of multiple
paths
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Feature 2 Hyponyms in domain

• Incorporate info on all the extended strings

Extended String uses the core term as headword
and is the hyponym of the core term
Length Ratio
Union Probability of Independent Events
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Feature 3 Part of Speech

• Probability of the POS tag pos(S)
• owned by a synset S
• given a core term Tc
• PoS Tag estimation Heuristics on Adj, Verb, and
  noun based on position

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Integrate Features

• Using Union Probability of Independent Events

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Evaluation

• Algorithm Output
• A pair of lt Tc_i, Synseti gt for each Chinese core
  term with the highest mapping weight
• Evaluation Standard
• For each Tc_i, whether their mappings to Synset
  are the best match with respect to this domain
• Answer Preparation
• Answer is manually made by two experts in IT
  domain respectively on the same set of data

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• Performance
• The evaluation conducted on the top N frequent
  core terms
• The algorithm COCA achieves 71 in accuracy (N is
  28 in this paper)
• Compared to the result of MRCOCA (Chen, 2007)
  which achieved only 50
• Two examples of core term to syntset mapping
  generated by the algorithm are given for ?? and
  ??.

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Conclusion

• Evaluation of COCA repeated on an English-Chinese
  bilingual Term bank with more than 130K entries
  show that the algorithm is
• 42 improved in accuracy compared to MRCOCA
  (Our Previous Works)
• The three features and the new algorithm based on
  probability made the improvement

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• Term bank can help to quickly construct domain
  core ontology by selecting the concept nodes and
  relations used in domain
• Bilingual term bank can further introduce the
  second language realization of the core ontology
  effectively and automatically

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Future Works

• Evaluation on three features
• how effective they are
• how much they contribute to the final performance
• Consideration of more features such as
  abbreviation, synset of head word of core term
  and etc.
• Use of other resources

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• QA

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Q

• A