building an operational product ontology system written by taehee lee, ig-hoon lee, suekyung lee,...
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Building an Operational Product Ontology System
Written by Taehee Lee, Ig-hoon Lee, Suekyung Lee, Sang-goo Lee (IDS Lab. SNU)
Dongkyu Kim, Jonghoon Chun (Prompt)
Hyunja Lee, Junho Shim (SWU)
ELSESEVIER, Electronic Commerce Research and Applications 5 (2006) 16–28
Presented by Dongjoo Lee
IDS Lab., CSE, SNU
Copyright 2008 by CEBT 2
Ontology Creation
Creating ontology for a domain gives chances to
Analyze domain knowledge
Make domain assumptions explicit
Separate domain knowledge from operational knowledge
Provide common understanding of the information structure
Enable reuse of domain knowledge
Created domain ontology can be used for
Searching, browsing, integration, and configuration
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Product Ontology
Product information is an essential component in e-commerce.
Distributed business data integration
Supply chain management
Spend analysis
E-procurement
Public Procurement Services (PPS) of Korea
G2B e-procurement service
Built in September 2002, 90% G2B transactions
KOCIS: Ontology based e-catalog System
http://www.g2b.go.kr:8100/index.jsp
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Participants of KOCIS
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Building Product Ontology
Modeling
Ontology Subsystems
Construction and maintenance
Search
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Models – meta modeling
A meta-model is yet another abstraction and highlighting properties of the model itself
3-level meta modeling
M0 meta-class level
– Products, classification schemes, attributes, Unit Of Measures (UOMs)
– Meta relationships
M1 class level
– a snapshot or instance of the product ontology model in M0
M2 instance level
– Physical ontology data managed by the system
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M0: Meta-class level
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M1: Class level
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M2: Implementation
Modeling goal is not only to design a conceptual product ontology model but also to implement it as an opera-tional ontology database model.
Through what?
OWL or RDFS?
– General purpose reasoning capability
– No robust OWL engine to practically handle a large knowledge-base
RDBMS?
– Restricted reasoning capability
– Shows high performance for low level semantic operations– Implement ontology subsystem to provide just enough reason-
ing capabilities along the core concepts
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class
Attr
class
class
value
UOM
AttrUOM
Synonym
Attr
UOM
valueAttr
class value
UOM
Reasoning Capabilities through Technical Dictionary
VocVoc
Search
Mapping
PropertyHierarchy
Instance
PropertyConstraint
Constraint
Conversion
Instance
Synonym
InstanceInstance
InferencesLv1 Inference
Attr
UOMvalue
class
Attr
class
value
classAttr UOM
value
UOM
class
class
class
value
class
AttrPropertyclass
Attr
class
class
value
UOM
AttrUOM
Synonym
Attr
UOM
valueAttr
class value
UOM
VocVoc
Search
Mapping
PropertyHierarchy
Instance
PropertyConstraint
Constraint
Conversion
Instance
Synonym
InstanceInstance
Attr
UOMvalue
class
Attr
class
value
classAttr UOM
value
UOM
class
class
class
value
class
AttrProperty
LCD
LCD PANEL
classAttr
TD1 Class & Relationships
TD2 Product Attributes
TD3 UOMs
TD4 Product Values
TD5 Vocabularies
TD6 Class-Product relations
TD7 Class-Attribute relations
TD8 Attribute-UOM relations
TD9 Vocabulary relations
eOTD, GDD, RNTD, EC-CMA, EAN/UCC, RosettaNet, …
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G2B classification TD
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Ontology Subsystems
WAS
Legacy System
Legacy DB
XML
온톨로지 애플리케이션 서버Construction Search
Maintenance
Synchronizer TD Manager Model Manger Log Manger
DB ManagerCategory Manager Miner
Loader
Analyzer
Distributer
Searcher
Parser
Infer Manager
Ranker
Catalog Builder
XML Publisher
XML/Excel ConverterCategory Mapper
Ontology Database
Attr Product Voc-Rel Class-Attr
Class-ProdVocClass UOM Attr-UOM
Ontology System
RMI Communication
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Probabilistic Similarity Computa-tion
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Probabilistic Similarity Computa-tion
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Visualization
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Conclusion
Developed a practical product ontology system.
Product ontology database
Ontology subsystems.
– Construction and maintenance
– Search
Based on Bayesian belief network
Meta-modeling
Concepts: Products, classification schemes, attributes, and UOMs
Relationships
Functions
Standard reference system for e-catalog construction
Supply tools and operations for managing catalog standards
Knowledge base
– Design and construction of product database
– Search and discovery of products and services
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Discussion
Uncovered semantics for handling inconsistencies
Constraints: domain, range, and cardinality
– foreign key constraints for ObjectTypeProperty
– data type constraints for DataTypeProperty
Triggers
OWL(RDF) export capability
Modeling based on OWL constructor
Generating schema and instances from rdbms
Querying performance comparison of RDF storages
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Model based on OWL
18
ec:G2BCategory
ec:G2B[XX]
rdfs:subClassOf
ec:PRO[XX]
rdf:type
owl:Class
rdf:type
ec:GUNGBCategory ec:UNSPSCCategory
ec:GUNGB[XX]
rdfs:subClassOf
rdf:typeec:belongsTo
ec:belongsTo
ec:UNSPSCCategory
ec:belongsTo
ec:belongsTo
ec:UOM
rdf:typerdf:type
rdf:type
ec:UG[XX]
rdfs:subClassOf
rdfs:subClassOf
ec:UOM[XX]
rdf:type
rdf:type
ec:Quantity
#unnamed
rdf:type
rdf:type
ec:hasUOM
xml:string
ec:hasName
ec:productProperty
ec:has[XX]
ec:hasAG[XX]
rdfs:subPropertyOf
rdfs:subPropertyOf
owl:ObjectPropertyrdf:type
owl:TransitiveProperty
rdf:type
ec:hasProductValue
rdf:typerdfs:subPropertyOf
ec:Product
rdf:type
rdf:type
ec:valueProperty
rdf:type
Complexity: OWL-DL
ec:ProductValue
owl:unionOf
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Querying Performance Compari-son
Simple queries
Complex queries that require inference
From 2007 MS thesis of Yucheon Lee.