DODDLE-OWL: A Domain Ontology
Construction Tool with OWL
Takeshi Morita 1)
,Naoki Fukuta 2)
, Noriaki Izumi 3)
,
and Takah...
Contents
• Motivation
• Related Works
• DODDLE-OWL Overview
• Implementation Architecture
• Case Studies
• Demonstration
•...
Contents
• Motivation
• Related Works
• DODDLE-OWL Overview
• Implementation Architecture
• Case Studies
• Demonstration
•...
Motivation
• Background
– The role of domain ontologies is important for the
Semantic Web
– Sharing common understanding a...
Semi-Automatic Construction
Set of
Concept Pairs
Quality Refinement
Domain
Specific
Documents
(English or
Japanese)
A Doma...
Contents
• Motivation
• Related Works
• DODDLE-OWL Overview
• Implementation Architecture
• Case Studies
• Demonstration
•...
Related WorksMehrnoush Shamsfard, Ahmad Abdollahzadeh Barforoush,
The State of the Art in Ontology Learning: A Framework f...
Learning System Degree of
Automation
DODDLE-OWL
(Keio University)
User Interaction,
Hand-made
modification
ASIUM
(Paris-Su...
Contents
• Motivation
• Related Works
• DODDLE-OWL Overview
• Implementation Architecture
• Case Studies
• Demonstration
•...
Set of
Concept Pairs
A Domain Ontology
(OWL format)
Initial
Concept Hierarchy
EDR
(general)
EDR
(technical)
WordNet
Genera...
Set of
Concept Pairs
A Domain Ontology
(OWL format)
Initial
Concept Hierarchy
EDR
(general)
EDR
(technical)
WordNet
Genera...
Procedure of Input Module
WordNet
EDR
(general)
EDR
(technical)
Ontology Selection
Domain specific documents
(English or J...
A Domain Ontology
(OWL format)
EDR
(general)
EDR
(technical)
WordNet
General
Ontologies
User (domain expert)
Taxonomic
Rel...
Construction Module
statistic
methods
matching
& trimming
Relationship
Construction
Hierarchy
Construction
Association
Rul...
Hierarchy Construction Module
Taxonomic Relationships
in the general ontologies
Merging
Input Concepts
Trimming
Root Root
...
Extract Concept Pairs
by different methods
Relationship Construction Module
Input concepts
Matching
Documents
WordSpace
As...
• Words and phrases in documents can be expressed
by vector representation containing co-occurrence
  statistics
WordSpace...
Association Rule
• Find associations between items in a set of
transaction
• In our research
– Each item is an input conce...
statistic
methods
matching
& trimming
Relationship
Construction
Construction Module
Set of Concept Pairs
Input Concept Sel...
Concept
Specification
Hierarchy Refinement
Module
Relationship Refinement
Module
Refinement Module
Concept
Hierarchy
Conce...
Hierarchy Refinement Module -
Concept Drift
A Domain Ontologygeneral ontologies
Reusable Part
No reusable part
because of
...
Hierarchy Refinement Module
strategy 1 Matched Result Analysis
Point out differences
of abstraction level
among sibling no...
Concept Drift Management
Matched Result Analysis
Trimmed Result Analysis
Visualization Module
Visualization Module
Parts o...
Relationship Refinement Module
Non-Taxonomic Relationship
Learning
Non-Taxonomic Relationships
Identify correct pairs from...
statistic
methods
matching
& trimming
Relationship
Construction
Construction Module
Set of Concept Pairs
Input Concept Sel...
Translation Module
owl:Class
owl:Class owl:Class
owl:Class
rdfs:subClassOf
owl:ObjectProperty
rdfs:domain rdfs:range
Taxon...
Contents
• Motivation
• Related Works
• DODDLE-OWL Overview
• Implementation Architecture
• Case Studies
• Demonstration
•...
Implementation Architecture
Java Virtual Machine
Jena2MR3
Visualization
Module
Translation
Module
Construction and
Refinem...
Contents
• Motivation
• Related Works
• DODDLE-OWL Overview
• Implementation Architecture
• Case Studies
• Demonstration
•...
Case Studies
• Purpose
– To check DODDLE-OWL can support the user in
constructing taxonomic and non-taxonomic relationship...
Results and Evaluation for
Taxonomic Relationships Construction
Initial
Concept Hierarcy
Business Ontology
Get paths relat...
Results and Evaluation for
Non-Taxonomic Relationships Construction
WS AR The union of WS & AR
# Extracted
concept pairs
4...
Results and Evaluation for
Non-Taxonomic Relationships Acquisition
WS AR The union of WS & AR
# Extracted
concept pairs
40...
Contents
• Motivation
• Related Works
• DODDLE-OWL Overview
• Implementation Architecture
• Case Studies
• Demonstration
•...
Demonstration
• Construct taxonomic and non-taxonomic
relationships for xCBL
Contents
• Motivation
• Related Works
• DODDLE-OWL Overview
• Implementation Architecture
• Case Studies
• Demonstration
•...
Conclusions
• Summary
– DODDLE-OWL: a Domain Ontology rapiD DeveLopment
Environment – OWL extension
• Focusing the quality...
Thank you for your attention.
DODDLE-OWL has been released .
Please visit this web site, if you like it.
about 100 user no...
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DODDLE-OWL: A Domain Ontology Construction Tool with OWL

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In this paper, we propose a domain ontology construction tool with OWL. The advantage of our tool is focusing the quality refinement phase of ontology construction. Through interactive support for refining the initial ontology, OWL-Lite level ontology, which consists of taxonomic relationships (defined as classes) and non-taxonomic relationships (defined as properties), is constructed effectively. The tool also provides semi-automatic generation of the initial ontology using domain specific documents and general ontologies.

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  • DODDLE-OWL: A Domain Ontology Construction Tool with OWL

    1. 1. DODDLE-OWL: A Domain Ontology Construction Tool with OWL Takeshi Morita 1) ,Naoki Fukuta 2) , Noriaki Izumi 3) , and Takahira Yamaguchi 1) 1) Keio University, Japan 2) Shizuoka University, Japan 3) National Institute of AIST, Japan
    2. 2. Contents • Motivation • Related Works • DODDLE-OWL Overview • Implementation Architecture • Case Studies • Demonstration • Conclusions
    3. 3. Contents • Motivation • Related Works • DODDLE-OWL Overview • Implementation Architecture • Case Studies • Demonstration • Conclusions
    4. 4. Motivation • Background – The role of domain ontologies is important for the Semantic Web – Sharing common understanding among people and software agents – Finding appropriate information on the web • Issues of large cost with building up domain ontologies – Many concepts in a domain – Each concept has high specific meaning – We need knowledge of domain experts – Cost-benefit performance of domain ontologies is lower than that of general ontologies (e.g. WordNet, EDR)
    5. 5. Semi-Automatic Construction Set of Concept Pairs Quality Refinement Domain Specific Documents (English or Japanese) A Domain Ontology (OWL format) Initial Concept Hierarchy Translation EDR (general) EDR (technical) WordNet General Ontologies Our Goal User (domain expert) Taxonomic Relationships Non-Taxonomic Relationships DODDLE-OWL: a Domain Ontology rapiD DeveLopment Environment – OWL extension Focusing the quality refinement phase of ontology construction
    6. 6. Contents • Motivation • Related Works • DODDLE-OWL Overview • Implementation Architecture • Case Studies • Demonstration • Conclusions
    7. 7. Related WorksMehrnoush Shamsfard, Ahmad Abdollahzadeh Barforoush, The State of the Art in Ontology Learning: A Framework for Comparison Learning System Element (s) Learned Prior Knowledge Input DODDLE-OWL (Keio University) Taxonomic and non-taxonomic conceptual Relations WordNet, EDR Unstructured domain specific texts (English and Japanese) ASIUM (Paris-Sud University ) Verb subcat. Frames + hierarchies Linguistic K. Unstructured (corpora) ( French ) HASTI (Amir Kabir University of Technology ) Words, Concepts, Taxonomic and Non-Taxonomic conceptual relations, axioms Almost empty (small kernel) Unstructured NL texts (Persian ) SVETLAN’ (CNRS laboratory) Noun Classes Structured + Unstructured input to SEGAPSITH (French ) SYNDIKATE (University of Albert- Ludwigs) Words , Concepts, Taxonomic and Non-Taxonomic conceptual relations Generic and domain lexicons and ontologies Unstructured   NL texts (German ) TEXT-TO-ONTO (University of Karlsruhe) Concepts, Taxonomic and Non- Taxonomic conceptual relations Lexical DB + domain lexicon NL texts , Web docs, Semi-structured (XML, DTD) and structured (German , HTML , XML, DTD ) WEB→KB Instances of classes and The ontology for An ontology + Training Support to construct Taxonomic and Non-Taxonomic Relationships.
    8. 8. Learning System Degree of Automation DODDLE-OWL (Keio University) User Interaction, Hand-made modification ASIUM (Paris-Sud University ) Cooperative HASTI (Amir Kabir University of Technology ) Both automatic and cooperative modes SVETLAN’ (CNRS laboratory) Automatic SYNDIKATE (University of Albert- Ludwigs) Automatic TEXT-TO-ONTO (University of Karlsruhe) Semi-automatic interactive, balanced cooperative WEB→KB (Distributed Systems Technology Centre) Automatic Related Works (cont.)Mehrnoush Shamsfard, Ahmad Abdollahzadeh Barforoush, The State of the Art in Ontology Learning: A Framework for Comparison Many ontology learning systems are focusing on automatic ontology construction. The user is difficult to refine the automatic generated ontologies. However Our system is focusing on high-level support for user interaction. The user is easy to refine semi- automatic generated ontologies and constructs high quality domain ontologies. Therefore
    9. 9. Contents • Motivation • Related Works • DODDLE-OWL Overview • Implementation Architecture • Case Studies • Demonstration • Conclusions
    10. 10. Set of Concept Pairs A Domain Ontology (OWL format) Initial Concept Hierarchy EDR (general) EDR (technical) WordNet General Ontologies System Overview User (domain expert) Taxonomic Relationships Non-Taxonomic Relationships Semi-Automatic Construction Quality Refinement Domain Specific Documents (English or Japanese) Translation DODDLE-OWL: a Domain Ontology rapiD DeveLopment Environment – OWL extension Focusing the quality refinement phase of ontology construction Documents Construction Module Refinement Module Visualization Module Input Module Translation Module
    11. 11. Set of Concept Pairs A Domain Ontology (OWL format) Initial Concept Hierarchy EDR (general) EDR (technical) WordNet General Ontologies System Overview User (domain expert) Taxonomic Relationships Non-Taxonomic Relationships Documents Construction Module Refinement Module Visualization Module Input Module Translation Module
    12. 12. Procedure of Input Module WordNet EDR (general) EDR (technical) Ontology Selection Domain specific documents (English or Japanese) Input Word Selection Word POS TF IDF TF-IDF W1 Noun ….. ….. ….. W2 Complex Word ….. ….. ….. Morphological Analysis Complex Word Extraction Document Selection ……………………………………….. Disambiguation Input Word Set Input Concepts W1 W2 W3 EDR (general): Ci WordNet: Cj EDR (technical): Ck ……………………………………….. Input Word Set Input Concept Set Select significant words for the domain (input words) Identify the sense of input words to map those words to concepts in the general ontologies
    13. 13. A Domain Ontology (OWL format) EDR (general) EDR (technical) WordNet General Ontologies User (domain expert) Taxonomic Relationships Non-Taxonomic Relationships Documents Refinement Module Visualization Module Translation Module statistic methods matching & trimming Relationship Construction Construction Module Set of Concept Pairs Input Concept Selection Hierarchy Construction Association Rule WordSpace Initial Concept Hierarchy Input concepts Input Module
    14. 14. Construction Module statistic methods matching & trimming Relationship Construction Hierarchy Construction Association Rule WordSpace Documents Set of Concept PairsInitial Concept Hierarchy Refinement Module Input Module Input concepts EDR (general) EDR (technical) WordNet General Ontologies Initial Domain Ontology
    15. 15. Hierarchy Construction Module Taxonomic Relationships in the general ontologies Merging Input Concepts Trimming Root Root unnecessary Internal Node Best Matched Node Salient Internal Node Initial Model initial concept hierarchy get paths related to input concepts generate an initial model trimming
    16. 16. Extract Concept Pairs by different methods Relationship Construction Module Input concepts Matching Documents WordSpace Association Rule Set of concept pairs method based on context similarity Popular method in the field of data mining
    17. 17. • Words and phrases in documents can be expressed by vector representation containing co-occurrence   statistics WordSpace Method WordSpace  ( Marti A. Hearst, Hinrich Schutze ) … wi … wj …C1 … wk … … wi … wj …C2 … wk … Context Similarity between concepts C1 and C2 • Inner products among the vectors work as the similarity between the words and phrases. High similarity Significant related concept pair for the domain
    18. 18. Association Rule • Find associations between items in a set of transaction • In our research – Each item is an input concept appearing in the document – One transaction is one sentence in the document • Parameters – Support = contain X and Y / All transaction – Confidence = contain X and Y / contain X X and Y: input concepts
    19. 19. statistic methods matching & trimming Relationship Construction Construction Module Set of Concept Pairs Input Concept Selection Hierarchy Construction Refinement Module Association Rule WordSpace Concept Specification Template Documents Visualization Module A Domain Ontology (OWL format) Input Module Initial Concept Hierarchy Matched Result Analysis Hierarchy Refinement Trimmed Result Analysis Translation Module Input conceptsEDR (general) EDR (technical) WordNet General Ontologies The value of co-concurrency Relationship Refinement
    20. 20. Concept Specification Hierarchy Refinement Module Relationship Refinement Module Refinement Module Concept Hierarchy Concept Drift Management Better performance by changing parameters with interaction of a userVisualization Module: MR3 RDF&RDFS Visual Editing Translation Module Set of Concept PairsInitial Concept Hierarchy support to refine the initial concept hierarchy graphically
    21. 21. Hierarchy Refinement Module - Concept Drift A Domain Ontologygeneral ontologies Reusable Part No reusable part because of concept drift an initial concept hierarchy constructed The position of particular concepts changes depending on the domain. adjust the initial concept hierarchy to the specific domain Concept Drift
    22. 22. Hierarchy Refinement Module strategy 1 Matched Result Analysis Point out differences of abstraction level among sibling nodes according to the Trimmed Result MOVE MOVE MOVE STAY A B C D Trimming Area Initial Model 0 0 3 B C D Trimmed Model A B C A D strategy 2 Trimmed Result Analysis Divide the initial concept hierarchy into reusable area and not reusable area according to the position of Best Matched Nodes suggest a user to move not reusable area Trimming Reconstructed by User Best Matched Node Internal Node Reconstructed by User
    23. 23. Concept Drift Management Matched Result Analysis Trimmed Result Analysis Visualization Module Visualization Module Parts of Modification are highlighted based on Matched result analysis and Trimmed Result analysis
    24. 24. Relationship Refinement Module Non-Taxonomic Relationship Learning Non-Taxonomic Relationships Identify correct pairs from generated candidates Setting parameters for WordSpace and Association Rule Construct non-taxonomic relationships by considering the relation with each concept pair
    25. 25. statistic methods matching & trimming Relationship Construction Construction Module Set of Concept Pairs Input Concept Selection Hierarchy Construction Refinement Module Association Rule WordSpace Concept Specification Template Documents Visualization Module A Domain Ontology (OWL format) Input Module Initial Concept Hierarchy Matched Result Analysis Hierarchy Refinement Trimmed Result Analysis Translation Module Input conceptsEDR (general) EDR (technical) WordNet General Ontologies The value of co-concurrency Relationship Refinement
    26. 26. Translation Module owl:Class owl:Class owl:Class owl:Class rdfs:subClassOf owl:ObjectProperty rdfs:domain rdfs:range Taxonomic Relationships Non-Taxonomic Relationships Export constructed domain ontology in OWL format relationship rdfs:subClassOf rdfs:subClassOf
    27. 27. Contents • Motivation • Related Works • DODDLE-OWL Overview • Implementation Architecture • Case Studies • Demonstration • Conclusions
    28. 28. Implementation Architecture Java Virtual Machine Jena2MR3 Visualization Module Translation Module Construction and Refinement Module Input Module Java WordNet Library (JWNL) Gensen Sen SS-Tagger JWNL: http://jwordnet.sourceforge.net/ Gensen: A Complex Word Extraction Tool Sen: A Japanese morpheme analyzer, http://ultimania.org/sen/ SS-Tagger: English Tagger MR3 : an RDF & RDFS graphical editor, http://mmm.semanticweb.org/mr3/ Jena Semantic Web Tool Framework: HP Labs, http://jena.sourceforge.net/
    29. 29. Contents • Motivation • Related Works • DODDLE-OWL Overview • Implementation Architecture • Case Studies • Demonstration • Conclusions
    30. 30. Case Studies • Purpose – To check DODDLE-OWL can support the user in constructing taxonomic and non-taxonomic relationships • Target Field   – Particular field of business   – xCBL (XML Common Business Library) • http://www.xcbl.org/ • Domain Specific Document – xCBL Document Description – about 150 sentences and 2500 words • Input Concepts – 57 business concepts from the document • User – Not an expert but has business knowledge
    31. 31. Results and Evaluation for Taxonomic Relationships Construction Initial Concept Hierarcy Business Ontology Get paths related to input concepts from WordNet Trimming Modification 57 Concepts Precision Recall ST1: Matched Result Analysis 5/25(=0.2)   5/7(=0.71) Evaluation of two strategies by the user 82 Concepts 152 Concepts 83 Concepts The number of concepts in each model Input Concepts Initial Model Constructed with Hierarchy Construction Module Constructed with Hierarchy Refinement Module
    32. 32. Results and Evaluation for Non-Taxonomic Relationships Construction WS AR The union of WS & AR # Extracted concept pairs 40 39 66 # Accepted concept pairs 30 20 39 # Rejected concept pairs 10 19 27 Precision 0.75 (30/40) 0.51 (20/39) 0.59 (39/66) Association Rule ( 20/39 )WordSpace ( 30/40 ) 8 17 1119 9 2 The union of WS & AR ( 39/66 ) Frequency of Extracted 4-gram Context Scope ( before : after ) threshold of similarity 2 10:10 0.6 Minimum Support Minimum Confidence 0.7 % 55 % For WordSpace parametersFor Association Rule parameters
    33. 33. Results and Evaluation for Non-Taxonomic Relationships Acquisition WS AR The union of WS & AR # Extracted concept pairs 40 39 66 # Accepted concept pairs 30 20 39 # Rejected concept pairs 10 19 27 Precision 0.75 (30/40) 0.51 (20/39) 0.59 (39/66) Association Rule ( 20/39 ) 8 17 1119 9 WordSpace ( 30/40 ) 2 The union of WS & AR ( 39/66 ) The precision of WS method is good, but the WS method has its bias so we cannot get certain types of concept pairs from it. we combine two different methods for getting wider range of concept pairs.
    34. 34. Contents • Motivation • Related Works • DODDLE-OWL Overview • Implementation Architecture • Case Studies • Demonstration • Conclusions
    35. 35. Demonstration • Construct taxonomic and non-taxonomic relationships for xCBL
    36. 36. Contents • Motivation • Related Works • DODDLE-OWL Overview • Implementation Architecture • Case Studies • Demonstration • Conclusions
    37. 37. Conclusions • Summary – DODDLE-OWL: a Domain Ontology rapiD DeveLopment Environment – OWL extension • Focusing the quality refinement phase of ontology construction – Case studies • construct a domain ontology for xCBL • Support the user in constructing and refining the domain ontology • Future Work – Reuse existing (domain) ontologies in any forms – Apply DODDLE-OWL to large scale domain ontology construction • Rocket operation ontology • About 40,000 concepts
    38. 38. Thank you for your attention. DODDLE-OWL has been released . Please visit this web site, if you like it. about 100 user now http://mmm.semanticweb.org/doddle/

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