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 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 2

Copyright  2008 by CEBT 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 3

Copyright  2008 by CEBT Participants of KOCIS 4

Copyright  2008 by CEBT Building Product Ontology  Modeling  Ontology Subsystems Construction and maintenance Search 5

Copyright  2008 by CEBT 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 6

Copyright  2008 by CEBT M0: Meta-class level 7

Copyright  2008 by CEBT M1: Class level 8

Copyright  2008 by CEBT 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 9

Copyright  2008 by CEBT 10 class Attr class value UOM Attr UOM SynonymAttr UOM value Attr classvalue UOM Reasoning Capabilities through Technical Dictionary Voc Search Mapping Property Hierarchy Instance Property Constraint Conversion Instance Synonym Instance Inferences Lv1 Inference Attr UOM value class Attr class value class AttrUOM value UOM class value class Attr Property class Attr class value UOM Attr UOM Synonym Attr UOM valueAttr classvalue UOM Voc Search Mapping Property Hierarchy Instance Property Constraint Conversion Instance Synonym Instance Attr UOM value class Attr class value class AttrUOM value UOM class value class Attr Property LCD PANEL class Attr 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, ECCMA, EAN/UCC, RosettaNet, …

Copyright  2008 by CEBT G2B classification TD 11

Copyright  2008 by CEBT 12 Ontology Subsystems WAS Legacy System Legacy DB XML 온톨로지 애플리케이션 서버 Construction Search Maintenance Synchronizer TD Manager Model Manger Log Manger DB Manager Category Manager Miner Loader Analyzer Distributer Searcher Parser Infer Manager Ranker Catalog Builder XML Publisher XML/Excel Converter Category Mapper Ontology Database AttrProduct Voc-RelClass-Attr Class-ProdVoc Class UOM Attr-UOM Ontology System RMI Communication

Copyright  2008 by CEBT Probabilistic Similarity Computation 13

Copyright  2008 by CEBT Probabilistic Similarity Computation 14

Copyright  2008 by CEBT Visualization 15

Copyright  2008 by CEBT 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 16

Copyright  2008 by CEBT 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 17

Copyright  2008 by CEBT Model based on OWL 18 ec:G2BCategory ec:G2B[XX] rdfs:subClassOf ec:PRO[XX] rdf:type owl:Class rdf:type ec:GUNGBCategoryec:UNSPSCCategory ec:GUNGB[XX] rdfs:subClassOf rdf:type ec:belongsTo ec:UNSPSCCategory ec:belongsTo ec:UOM rdf:type ec:UG[XX] rdfs:subClassOf ec:UOM[XX] rdf:type ec:Quantity #unnamed rdf:type ec:hasUOM xml:string ec:hasName ec:productProperty ec:has[XX] ec:hasAG[XX] rdfs:subPropertyOf owl:ObjectProperty rdf:type owl:TransitiveProperty rdf:type ec:hasProductValue rdf:type rdfs:subPropertyOf ec:Product rdf:type ec:valueProperty rdf:type Complexity: OWL-DL ec:ProductValue owl:unionOf

Copyright  2008 by CEBT Querying Performance Comparison 19 Simple queries Complex queries that require inference From 2007 MS thesis of Yucheon Lee.