DIN CWA 16525-2013 Multilingual electronic cataloguing and classification in eBusiness - Classification Mapping for open and standardized product classification usage in eBusiness .pdf

1、Mai 2013 Bereich InnovationPreisgruppe 34DIN Deutsches Institut fr Normung e. V. Jede Art der Vervielfltigung, auch auszugsweise, nur mit Genehmigung des DIN Deutsches Institut fr Normung e. V., Berlin, gestattet.ICS 01.140.20; 35.240.60Zur Erstellung einer DIN SPEC knnen verschiedene Verfahrensweis

2、en herangezogen werden: Das vorliegende Dokument wurde nach den Verfahrensregeln eines CWAs erstellt.!$#“1990025www.din.deDDIN CWA 16525Mehrsprachige elektronische Katalogisierung und Klassifizierung imBereich eBusiness Klassifikations-Mapping im Hinblick auf offene und standardisierteProduktklassif

3、ikation im Bereich eBusiness;Englische Fassung CWA 16525:2012Multilingual electronic cataloguing and classification in eBusiness Classification Mapping for open and standardized product classification usage ineBusiness;English version CWA 16525:2012Catalogage et classification lectroniques multiling

4、ues dans le domaine eBusiness Mappage de classification en vue dune classification ouverte et standardise de produitsdans le domaine eBusiness;Version anglaise CWA 16525:2012Alleinverkauf der Spezifikationen durch Beuth Verlag GmbH, 10772 Berlin www.beuth.deGesamtumfang 151 SeitenDIN SPEC 91304DIN C

5、WA 16525 (DIN SPEC 91304):2013-05 2 Nationales Vorwort Dieses europisches CEN Workshop Agreement (CWA 16525:2012) wurde vom CEN Workshop Workshop Multilingual electronic cataloguing and classification in eBusiness“ bei CEN erarbeitet, dessen Sekretariat vom AFNOR (Frankreich) gehalten wurde. Es hand

6、elt sich um eine unvernderte bernahme der CWA 16525 Multilingual electronic cataloguing and classification in eBusiness Classification mapping for open and standardized product classification usage in eBusiness“ als DIN CWA 16525 (DIN SPEC 91304) Mehrsprachige elektronische Katalogisierung und Klass

7、ifizierung im Bereich eBusiness Klassifikations-Mapping im Hinblick auf offene und standardisierte Produktklassifikation im Bereich eBusiness“. Eine DIN SPEC nach dem CWA-Verfahren ist die nationale bernahme einer CEN/CENELEC-Vereinbarung, die innerhalb offener CEN/CENELEC Workshops entwickelt wird

8、und den Konsens zwischen den registrierten Personen und Organisationen widerspiegelt, die fr den Inhalt verantwortlich sind. Arbeiten eines CEN Workshop werden nicht durch ein nationales Gremium gespiegelt. Bei dem zu Grunde liegenden CWA wurde ein Entwurf durch CEN verffentlicht. EUROPEAN COMMITTEE

9、 FOR STANDARDIZATION COMIT EUROPEN DE NORMALISATION EUROPISCHES KOMITEE FR NORMUNG Management Centre: Avenue Marnix 17, B-1000 Brussels 2012 CEN All rights of exploitation in any form and by any means reserved worldwide for CEN national Members. Ref. No.:CWA 16525:2012 E CEN WORKSHOP AGREEMENT CWA 1

10、6525 December 2012 ICS 01.140.20; 35.240.60 English version Multilingual electronic cataloguing and classification in eBusiness Classification Mapping for open and standardized product classification usage in eBusiness This CEN Workshop Agreement has been drafted and approved by a Workshop of repres

11、entatives of interested parties, the constitution of which is indicated in the foreword of this Workshop Agreement. The formal process followed by the Workshop in the development of this Workshop Agreement has been endorsed by the National Members of CEN but neither the National Members of CEN nor t

12、he CEN-CENELEC Management Centre can be held accountable for the technical content of this CEN Workshop Agreement or possible conflicts with standards or legislation. This CEN Workshop Agreement can in no way be held as being an official standard developed by CEN and its Members. This CEN Workshop A

13、greement is publicly available as a reference document from the CEN Members National Standard Bodies. CEN members are the national standards bodies of Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Former Yugoslav Republic of Macedonia, France, Germany, Greec

14、e, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey and United Kingdom. CWA 16525:2012 (E) 2 Contents Page Foreword 3 Introduction .5 1 Scope 8 2 Normative References .8 3 Te

15、rms and definitions and abbreviations .8 4 Methodologies for product classification system mapping . 14 5 The cMap Overall Mapping Methodology . 47 6 Description of the classification systems 57 7 Definition of the architecture for an open standardized classification collaboration platform 83 8 Defi

16、nition of a synchronization process 118 9 Conclusion and recommendation . 135 Annex A (informative) The SKOS platform 139 Annex B (informative) The Protg Platform . 140 Annex C (informative) The Prompt Tool. 144 Annex D (informative) Mapping Tables 147 Bibliography . 148 DIN CWA 16525 (DIN SPEC 9130

17、4):2013-05 CWA 16525:2012 (E) 3 Foreword This CEN Workshop Agreement has been drafted and approved by a Workshop of representatives of interested parties on 2012-09-20, the constitution of which was supported by CEN following the public call for participation made on 2011-01-27. A list of the indivi

18、duals and organizations which supported the technical consensus represented by the CEN Workshop Agreement is available to purchasers from the CEN-CENELEC Management Centre. These organizations were drawn from the following economic sectors (ICT, eCommerce, Product Classification, Universities). The

19、formal process followed by the Workshop in the development of the CEN Workshop Agreement has been endorsed by the National Members of CEN but neither the National Members of CEN nor the CEN Management Centre can be held accountable for the technical content of the CEN Workshop Agreement or possible

20、conflict with standards or legislation. This CEN Workshop Agreement can in no way be held as being an official standard developed by CEN and its members. The final review/endorsement round for this CWA was started on 2012-04-26 and was successfully closed on 2012-06-26.The final text of this CWA was

21、 submitted to CEN for publication on 2012-10-22. The following companies/organizations endorsed the CWA: AFIM (France) Infoterm (Austria) Class.Ing (Germany) TANGER Computersystems (Czech Republic) AGATHIS (Belgium) CESI (China) CETIM (France) Colterm S.A. (Colombia) Consip S.p.A (Italy) DIN (German

22、y) Dr. Otto Mueller Consulting (Switzerland) eClss e.V. (Germany) ESG Elektroniksystem- und Logistik-GmbH (Germany) GS1 France (France) GS1 Germany GmbH (Germany) ICB - Institute for Computer in this document eCat DIN CWA 16525 (DIN SPEC 91304):2013-05 CWA 16525:2012 (E) 13 ePDC electronic Product D

23、escription and Classification ePPS electronic product property server GPC Global Product Classification (GS1 Classification System) GDSN Global Data Synchronisation Network by GS1 (see www.gs1.org/gdsn) GTIN Global Trade Item Number (GS1 Trade item unique identifier cf http:/www.gs1.org/barcodes/tec

24、hnical/id_keys) GUI Graphical User Interface IEC International Electrotechnical Commission ISO International Organization for Standardization OWL Ontology Web Language PCS Product Classification System PLIB Parts Library ISO 13584 RDF Resource Description Framework RDFS RDF schema SKOS Simple Knowle

25、dge Organization System UNSPSC United Nations Standard Products and Services Code URI Uniform Resource Identifier XML extensible markup language XSD XML schema definition DIN CWA 16525 (DIN SPEC 91304):2013-05 CWA 16525:2012 (E) 14 4 Methodologies for product classification system mapping 4.1 Ontolo

26、gies 4.1.1 General An ontology typically provides a vocabulary that describes a domain of interest and a specification of the meaning of terms used in the vocabulary as specified in “Ontology matching: state of the art and future challenges” 8. Depending on the precision of the specification, the no

27、tion of ontology encompasses several data and conceptual models, including, sets of terms, classifications, thesauri, database schemas, or fully axiomatised theories, see “Ontology matching” 7. When several competing ontologies are used in different applications, most often these applications cannot

28、 immediately interoperate. Ontologies are serving for structuring and exchanging of data or information. They typically consist of: Concepts or classes Types Instances Relations Inheritance and Axioms For further information on ontologies, see http:/semanticweb.org/wiki/Ontology. 4.1.2 Semantic hete

29、rogeneity Interoperability and integration of data sources are becoming ever more important issues as both, the amount of data and the number of data producers is growing. Interoperability not only has to resolve the differences in data structures, it also has to deal with semantic heterogeneity. Se

30、mantics refer to the meaning of data in contrast to syntax which only defines the structure of the schema items (e.g., classes and attributes). Resolving semantic heterogeneities must address more than 40 discrete categories of potential mismatches from units of measure, terminology, language, and m

31、any others. These sources may derive from structure, domain, data or language, refers “Sources and Classification of Semantic Heterogeneity“ 9. Even if the Ontology Web Language (OWL) or similar languages now provide the means to represent an ontology, there is the vexing challenge of how to resolve

32、 the differences between different views or perspectives, even within the same domain. An example of a perspective is the purchasing one as different to the sales perspective. When independent parties develop database schemas for the same domain, they will almost always be quite different from each

33、other. These differences are referred to as semantic heterogeneity, which also appears in the presence of multiple XML documents, Web services, and ontologies or more broadly, whenever there is more than one way to structure a body of data. The presence of semi-structured data exacerbates semantic h

34、eterogeneity, because semi-structured schemas are much more flexible to start with. For multiple data systems to cooperate with each other, they must understand each others schemas. Without such understanding, the multitude of data sources amounts to a digital version of the Tower of Babel. Refers t

35、o “Why Your Data Wont Mix” 10. DIN CWA 16525 (DIN SPEC 91304):2013-05 CWA 16525:2012 (E) 15 There are many potential circumstances where semantic heterogeneity may arise1: Enterprise information integration Querying and indexing the deep Web (which is a classic data federation problem in that there

36、are literally tens to hundreds of thousands of separate Web databases) Merchant catalogue mapping Schema versus data heterogeneity Schema heterogeneity and semi-structured data Naturally, there will always be differences in how differing authors or sponsors create their own particular “world view,”

37、which, if transmitted in XML or expressed through an ontology language such as OWL may also result in differences based on expression or syntax. Indeed, the ease of conveying these schemas as semi-structured XML, RDF or OWL is in and of itself a source of potential expression heterogeneities. There

38、are also other sources in simple schema use and versioning that can create mismatches, see “Why Your Data Wont Mix” 10. Thus, possible drivers in semantic mismatches can occur from world view, perspective, syntax, structure and versioning and timing: One schema may express a similar “world view” wit

39、h different syntax, grammar or structure One schema may be a new version of the other Two or more schemas may be evolutions of the same original schema There may be many sources modelling the same aspects of the underlying domain (”horizontal2resolution” such as for competing trade associations or s

40、tandards bodies), or There may be many sources that cover different domains but overlap at the seams (”vertical resolution” such as between pharmaceuticals and basic medicine) Heterogeneities can be classified into three broad classes, as indicated in “A Classification Scheme for Semantic and Schema

41、tic Heterogeneities in XML Data Sources” 11: Structural conflicts arise when the schema of the sources representing related or overlapping data exhibit discrepancies. Structural conflicts can be detected when comparing the underlying Document Type Definitions (DTDs). The class of structural conflict

42、s includes generalization conflicts, aggregation conflicts, internal path discrepancy, missing items, element ordering, constraint and type mismatch, and naming conflicts between the element types and attribute names. Domain conflicts arise when the semantic of the data sources that will be integrat

43、ed exhibit discrepancies. Domain conflicts can be detected by looking at the information contained in the DTDs and using knowledge about the underlying data domains. The class of domain conflicts includes schematic discrepancy, scale or unit, precision, and data representation conflicts. Data confli

44、cts refer to discrepancies among similar or related data values across multiple sources. Data conflicts can only be detected by comparing the underlying documents. The class of data conflicts includes ID-value, missing data, incorrect spelling, and naming conflicts between the element contents and t

45、he attribute values. 1http:/techwiki.openstructs.org/index.php/Classification_of_Semantic_Heterogeneity 2Horizontal in the context of this document means, that all products and services shall be taken into account. The opposite is vertical, where only products and services related to specific indust

46、ry sectors are considered. DIN CWA 16525 (DIN SPEC 91304):2013-05 CWA 16525:2012 (E) 16 Moreover, mismatches or conflicts can occur between set elements (a “population” mismatch) or attributes (a “description” mismatch). Figure 2 below shows about 40 distinct potential sources of semantic heterogene

47、ities: DIN CWA 16525 (DIN SPEC 91304):2013-05 CWA 16525:2012 (E) 17 Class Category Subcategory STRUCTURAL Naming Case sensitivity Synonyms Acronyms Homonyms Generalization / Specialization Aggregation Intra-aggregation Inter-aggregation Internal Path Discrepancy Missing Item Content Discrepancy Attr

48、ibute List Discrepancy Missing Attribute Missing Content Element Ordering Constraint Mismatch Type Mismatch DOMAIN Schematic Discrepancy Element-value to Element-label Mapping Attribute-value to Element-label Mapping Element-value to Attribute-label Mapping Attribute-value to Attribute-label Mapping

49、 Scale or Units Precision Data Representation Primitive Data Type Data Format DATA Naming Case sensitivity Synonyms Acronyms Homonyms ID Mismatch Missing Data Incorrect Spelling LANGUAGE Encoding Ingest Encoding Mismatch Ingest Encoding Lacking Query Encoding Mismatch Query Encoding Lacking Languages Script mismatches Passing / Morphological Analysis Errors (many) Syntactical Errors (many) Semantic Errors (many) Figure 2 - 40 sources of semantic heterogeneities