1. Organizing and Implementing on the Thesauri Mapping Project Dr. Chang Chun Associate Professor Agriculture Information Institute, Chinese Academy of Agricultural Sciences (AII/CAAS), Beijing China The Seventh Agricultural Ontology Service (AOS) Workshop AFITA 2006 November 9-11, Bangalore, India

2. Outline  Introduction  Organizing  AGROVOC and CAT  Conclusions Outline 7 th AOS  Objectives  Methods  Mapping rules  Discussions

3. Brief Introduction on the Mapping Project CAT CAAS AGROVOC FAO ExactMatch InexactMatch BroadMatch NarrowMatch AND,OR,NOT No mapping mapping Mapping RulesResourceTarget 7 th AOS Introduction

4. Objective 1: Enrich AOS Terminology Domain Knowledge  Key words have problems in search information;  Thesauri are still working in information management;  Research on conversion from thesaurus to ontology;  Mapping can add more new domain knowledge. 7 th AOSObjective

5. Objective 2: Develop Cross-Language Search System Chinese users Mapping Information ( e, b,n… ) Chinese data AGRIS data AGROVOC CAT English Users Search Search end 7 th AOSObjective

6. The Time and Tools of Mapping Project  The time of mapping project: From September 2005 to September 2006;  Mapping rules: a revision method of SKOS Mapping Vocabulary Specification;  Mapping direction: from CAT (resource) to AGROVOC (target)  Mapping tools: Prot é g é, Excel sheet, CAT and AGROVOC CD- ROM. 7 th AOS Organizing

7. Working Flow  From 2005-09-01 to 2005-11-05: make plans of mapping methods, prepare and test the mapping data;  From 2005-11-06 to 2006-05-30: the training and mapping with Excel sheet;  From 2006-06-01 to 2006-09-30: convert the Excel sheet information to OWL mapping data, Protégé can read this information. 7 th AOS Organizing

8. The specialists  we organized about 16 agricultural domain specialists in CAAS, many of them are PhD students, they were chosen based on the domain.  The main domain are biological science, agricultural environmental science, agricultural meteorology, fertilizer science, horticulture, forestry practice, plant protection, agronomy, agricultural products processing and storage and comprehensive utilization, veterinary medicine, biological control, Industrial technology and equipment, fishery science, and so on.  Some of them have knowledge of thesaurus. 7 th AOS Organizing

9. AGROVOC and CAT AGROVOC: 27736 English terms: 16769 descriptors, 10967 non descriptors 25060 Chinese terms: 16628 descriptors, 8432 non descriptors 1240 top terms organized in 130 categories (AGRIS/CARIS) includes biological taxonomy and geographical names CAT: 64638 Chinese terms: 51614 descriptors, 13024 non-descriptors 51400 descriptors has at least one translation 2332 top terms organized in 40 categories (e.g. crops, etc.) includes biological taxonomy and geographical names 7 th AOS Organizing

10. To Finish the Mapping Work in Two Steps  First, Excel sheet: We split CAT into 36 documents based on the domain, we use Excel sheet, try to find all mapping information and input it in the Excel sheet, all these sheets will be kept as original data;  Second,convert information to OWL document: After we finish the all Excel sheets, we convert and input these mapping information into OWL documents, they can be read in Protégé after import CAT and AGROVOC. 7 th AOS Organizing

11. Excel sheets ABCDEFGHIJ C-term code C- termRelation A-term code A- term combine relation C-revise suggestion C- comment A-revise suggestion A- comment 7 th AOS Organizing

13. Mapping Standards and Methods  Exact Match, Inexact Match ；  Broad Match,Narrow Match ；  AND ； OR ； NOT ； 7 th AOSMethods

14. Mapping relationships Exact match SKOS: exactMatch OWL: equivalentTo Broader/Narrower match SKOS: broadMatch, narrowMatch OWL: subClassOf OR, AND, NOT operators SKOS: OR, AND, NOT OWL unionOf, intersectionOf, complementOf Partial equivalences SKOS: minorMatch, majorMatch 7 th AOSMethods

15. Exact Match CAT AGROVOC Mapping Exact Match Such as ： ‘17147- 禾谷类作物 ’ Exact Match ‘25512-Cereal crops’ 7 th AOSMethods

18. equivalentClass: One of main mapping relation (13105) 7 th AOSMethods

19. Inexact Match CAT Mapping AGROVOC Inexact Such as ： ‘ 经济大国 ’ Inexact match ‘Developed countries’ 7 th AOSMethods

20. 55581_ 玉米芯 _Maizecob ie 16171 <rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >inexact mapping with 16171 Inexact Match : We seldom use this mapping relation 7 th AOSMethods

21. Broad Match CAT Mapping AGROVOCBroad Match Such as ： “35234- 普及教育 ” Broad Match ‘2488-Education’ 7 th AOSMethods

25. subClassOf: BroadMatch (another main mapping relation 11408) 7 th AOSMethods

26. Narrow Match CAT Mapping AGROVOC Narrow Match Such as ： “8341_ 岛屿 _Islands” Narrow Match “695_Atolls_ 环礁 ” 7 th AOSMethods

30. subClassOf: Narrow Match (173) 7 th AOSMethods

31. AND ； OR ； NOT AND “59683- 自动标引 ” Exact Match ‘11729-Indexing of information’ AND ‘15855 -Automation’ ORNOT “7536_ 大麦 _Barley” Exact Match ‘823_Barley_ 大麦 OR 3662_Hordeum vulgare_ 大麦植物 ’ ‘12114- 非传染性病害 ’ Exact match ‘5962-Plant diseases’ NOT ‘34024-Infectious diseases’ 7 th AOSMethods

32. AND “59683_ 自动标引 _Automaticindexing” Exact Match 11729_Indexingofinformation_ 信息编目 and 15855_Automation_ 自动化 7 th AOSMethods

36. AND: intersectionOf 7 th AOSMethods

37. OR 7536_ 大麦 _Barley” Exact Match ‘823_Barley_ 大麦 OR 3662_Hordeum vulgare_ 大麦植物 Methods7 th AOS

41. OR: unionOf 7 th AOSMethods

42. NOT ‘12114_ 非传染性病害 _Non-infectiousdiseases’ Exact match ‘5962_Plantdiseases_ 植物病害 ’ AND NOT ‘34024_Infectiousdiseases_ 侵染性病害 ’ 7 th AOSMethods

46. NOT: complementOf 7 th AOSMethods

47. No mapping: 13867_ 干扰 _Interference 7 th AOSMethods

50. <rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >AGROVOC hasn't this concept NoMapping: comment 7 th AOSMethods

51. How to get OWL documents  Convert the Excel sheet information to Protégé (machine convert and human input ), and get OWL mapping data;  Use the tools of ‘import ontology’, import one domain of CAT and whole AGROVOC, and input the mapping relations, after save the working, we can get different domain OWL documents; 7 th AOSMethods

52. Combine the OWL documents  Delete the top and the end of all OWL documents, then paste them together,we get the whole middle part of mapping project;  Create a new OWL document, import whole CAT and AGROVOC, and save the document;  Insert the whole middle part of mapping project into the upper document, then we get a whole mapping OWL document, it works with whole CAT and AGROVOC. Methods7 th AOS

55. 1 Candidate and the True mapping Conclusions 7 th AOS Classification Exact match bn e-b-n total Other relation Classification total Total13 10511 40817324 6861 74725 433 Num.Taxon.Geogr. TotalAction Match English and Chinese2 4701 5471434 160Exact match Match English but different Chinese624546151 187Match not ensured Match Chinese but different English3 2974051883 890Tentative exact match Automatic identification of candidate exact matches The statistics of true mapping matches relation

56. 2 The Series Mapping Knowledge Data Files Conclusions 7 th AOS The contribution include the following documents: (a) cat_agrovco_mapping.owl; (b) ag_20051101.owl; (c) cat_all_u.owl; (d) agrovoc-zh-revise.xls; (e) agrovoc-usefor-comment.xls; Users can use Protégé create a new ontology with the data of (a), the machine will ask to import (b) and (c), and then you can open the (a), the open time is a little slow, our computer need about 4 minutes, the computer CPU 3.4, RAM: 1 G. (d) notes the information which need to be revised about the terms of AGROVOC; (e) is the comments about AGROVOC terms

57. Discussions  No mapping ;  InexactMatch;  Begin from the top term;  Mapping document need work with CAT and AGROVOC;  There are many broadMatch relations;  The comment and the suggestion; 7 th AOS Discussions

58. The Heredity of Mapping Relation About 60% CAT concepts obtain mapping relation with AGROVOC by heredity. They normally follow the ExactMatch, BroadMatch (24 513) 7 th AOS Discussions C1A1 2122 31 32 33 ExactMatch BroadMatch CATAGROVOC

59. Different Thesauri with Different Classification A few concepts have different domain trees in two thesauri, means different thesauri have their own classification. 7 th AOS Discussions C1A1 2122 31 32 33 ExactMatch CATAGROVOC 2122 31 32

60. The Resource and Target  ExactMatch: same concepts;  BroadMatch: Chinese users get more broad concept, or get some useless information;English users get more specific concept, or can’t find all information.  NarrowMatch: the opposite.  CAT has more than 60,000 terms, AGROVOC has only about 30,000 terms, so take CAT as resource is better. 7 th AOS Discussions C1A1 2122 31 32 33 ExactMatch BroadMatch CATAGROVOC A4 NarrowMatch

61. Discussions 2  Different knowledge taxonomy ；  Difference on noun and verb ；  Different social ideas ；  Different cultures ；  Different translations. 7 th AOS Discussions

62. Chinese Academy of Agricultural Sciences (CAAS) and Food and Agriculture Organization (FAO) changc@mail.caas.net.cn margherita.sini@fao.org Thank you 7 th AOSThanks