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Building an Operational Product Ontology System

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  1. 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

  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

  3. 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

  4. Participants of KOCIS

  5. Building Product Ontology • Modeling • Ontology Subsystems • Construction and maintenance • Search

  6. 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

  7. M0: Meta-class level

  8. M1: Class level

  9. M2: Implementation • Modeling goal is not only to design a conceptual product ontology model but also to implement it as an operational ontology database model. • Through what? • OWL or RDFS? • General purpose reasoning capability • No robust OWL engine to practically handle a large knowledgebase • RDBMS? • Restricted reasoning capability • Shows high performance for low level semantic operations • Implement ontology subsystem to provide just enough reasoning capabilities along the core concepts

  10. class class class class class class class class Attr Attr UOM UOM UOM UOM UOM UOM value value Property Property Constraint Constraint value value Constraint Constraint value value Conversion Conversion UOM UOM Search Attr Attr value value class class Instance Instance Instance Instance class class Voc Voc Voc class class Synonym Synonym Property class class value value Attr Attr Synonym Synonym Attr Attr Mapping Mapping Instance Instance Property Property class class Instance Instance UOM UOM Hierarchy Hierarchy Attr Attr Attr Attr Attr Attr class class value value UOM UOM TD9 TD8 TD7 TD6 TD5 TD1 TD2 TD4 TD3 Vocabularies Class-Product relations Class & Relationships Class-Attribute relations UOMs Attribute-UOM relations Product Attributes Product Values Vocabulary relations class class value value Attr Attr UOM UOM Reasoning Capabilities through Technical Dictionary Inferences Lv1 Inference eOTD, GDD, RNTD, ECCMA, EAN/UCC, RosettaNet, … Search Voc LCD class Property LCD PANEL Attr

  11. G2B classification TD

  12. LegacyDB Ontology Subsystems Ontology System 온톨로지 애플리케이션 서버 Construction Search Loader Searcher Infer Manager Catalog Builder Analyzer Parser Ranker XML Publisher Legacy System Distributer Category Mapper XML/Excel Converter WAS Maintenance RMI Communication Category Manager DB Manager Miner Synchronizer TD Manager Model Manger Log Manger XML Ontology Database UOM Voc Attr-UOM Class-Prod Class Attr Product Voc-Rel Class-Attr

  13. Probabilistic Similarity Computation

  14. Probabilistic Similarity Computation

  15. Visualization

  16. 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

  17. 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

  18. Model based on OWL owl:Class Complexity: OWL-DL rdf:type rdf:type rdf:type rdf:type rdf:type ec:UOM ec:G2BCategory ec:GUNGBCategory ec:UNSPSCCategory ec:Quantity rdfs:subClassOf rdf:type rdf:type rdfs:subClassOf ec:belongsTo ec:belongsTo rdfs:subClassOf rdfs:subClassOf ec:GUNGB[XX] ec:UG[XX] ec:G2B[XX] ec:hasUOM ec:UNSPSCCategory ec:hasProductValue rdf:type ec:hasName owl:unionOf rdf:type ec:belongsTo ec:belongsTo xml:string ec:ProductValue ec:UOM[XX] rdf:type ec:Product rdf:type #unnamed rdf:type • ec:has[XX] ec:PRO[XX] rdfs:subPropertyOf rdfs:subPropertyOf rdf:type rdf:type • ec:hasAG[XX] • ec:valueProperty • owl:TransitiveProperty rdfs:subPropertyOf rdf:type • ec:productProperty rdf:type • owl:ObjectProperty

  19. Querying Performance Comparison Simple queries Complex queries that require inference From 2007 MS thesis of Yucheon Lee.