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Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
Making topic maps from Subject Headings for linking and organizing
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Making topic maps from Subject Headings for linking and organizing

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  • 1. Making topic maps from Subject Headings for liking and organizing information 2010-4-15 Motomu Naito Center for Integrated Area Studies (CIAS) Kyoto University [email_address] Ψ http://psi.ontopedia.net/Motomu_Naito http://www.cias.kyoto-u.ac.jp/english/CIAS/
  • 2. Table of Contents
    • 1. Back ground
    • 2. Purpose
    • 3. Subject Headings
    • 3 .1  NDLSH
    • 3 .2  BSH
    • 3 .3  LCSH
    • 4. Practical use of Subject Headings
    • 5. Demo
    • 6. Conclusion
    • 7. Future work
  • 3. 1 . Background: Area Study and Area Informatics
    • This activity is a part of activities of Area Informatics in Center for Integrated Area Study (CIAS) in Kyoto university
    • Area Study is an Interdisciplinary Science
      • Understanding/comparing areas comprehensively
      • Diverse languages/subjects/disciplines/methodologies:
        • history, literature, religions, politics, economics, ethnology, folklore, agriculture, environment, etc.
    • Area I nformatics
      • Informatics paradigm in area studies
      • Focusing on quantitative analysis
        • Objective, comparative and reproducible approaches
        • Spatiotemporal attributes of events
      • Knowledge discovery supports
        • Integration of d isciplines
        • Creation of hypotheses
    Source: Shoichiro Hara, TMJP2010, http://www.knowledge-synergy.com/events/documents/TMJP2010-hara.pdf
  • 4. Model of Area Informatics Source: Shoichiro Hara, TMJP2010
  • 5. 2. Purpose
    • To make good system for linking and organizing Area Studies related information
    • ・ Making and maintaining well organized knowledge is very hard and time consuming work
    • ・ We have already had well organized knowledge and we can use those knowledge
    • ・ We are focusing attention on Subject Headings and thesauri
    •   - NDLSH, BSH, LCSH, JST thesaurus, etc.
    • ・ We are making topic maps and PSI from those knowledge, to make good use of those knowledge for linking and organizing Area Studies information
  • 6. 3. Subject Headings
    • What is Subject Headings:
    • Wikipedia redirects “Subject Headings” to “Index term” and define the term as
    • “ An index term, subject term, subject heading, or descriptor, in information retrieval, is a term that captures the essence of the topic of a document. Index terms make up a controlled vocabulary for use in bibliographic records.
    • (http://en.wikipedia.org/wiki/Index_term)
    • ・ We are working on the following SH at the moment
    • - NDLSH, BSH and LCSH
    • ・ Probably we can find much more SH in each country
    • - Norwegian SH, Finnish SH, German SH, Thai SH, etc.
  • 7. 3.1  NDLSH
    • ・ NDLSH: National Diet Library Subject Headings, in Japan
    • ・ We are making topic map from NDLSH 2008 Version
    •   - Subject Headings : 17,953
    •   - Subject Headings + Reference words : 47,816 (47,377)
    •   - BT-NT relation : 13,220     RT relation : 9,738
    • - USE-UF relation with LCSH: 11,663
    • ・ Conversion from SH to Topic Map
    • - Subject Headings -> Topics
    • - BT-NT, RT, USE-UF relation -> Associations
    • - USE-UF, SA relation, Scope note, reading, … -> Occurrences
    • ・ SHs have each own ID that can be used as PSI (e.g. 00574308)
    • e.g. http://www.ndl.go.jp/psi/ndlsh/heading-00560674
    • ・ If NDLSH shares PSI with LCSH, it can be merged with LCSH
    • ・ NDLSH will be exposed on the Web soon, I hope
  • 8. NDLSH Ontology
    • Ontology graph of NDLSH topic map
  • 9. NDLSH topic map application
    • Screen shots of the application
  • 10. 3.2  BSH
    • ・ BSH : Basic Subject Headings, Japan Library Association
    • ・ We are making topic map from BSH4  
    •   - Subject Headings : 7,847 (8,036), Reference words : 2,873 (2,892)
    •   - Descriptive reference words : 93, Particular : 169
    •   - BT-NT relation : 8,454, RT relation : 213
    •    USE-UF relation : 3,065, Top Term : 10,064
    • ・ Conversion from SH to Topic Map
    • - Subject Headings, Reference words, Top terms, etc. -> Topics
    • - BT-NT, RT, USE-UF relation -> Associations
    • - Scope note, reading, NDC8, NDC9, etc. -> Occurrences
    • ・ SHs have each own ID that can be used as PSI (e.g. BSH400894400)
    • e.g. http://www.jla.or.jp/psi/bsh/heading-BSH400894400
    • ・ If BSH shares PSI with LCSH, it can be merged with LCSH
    • ・ BSH has not exposed on the Web yet
  • 11. BSH Ontology
    • Ontology graph of BSH topic map
  • 12. BSH topic map application
    • Screen shots of the application
  • 13. 3.3  LCSH
    • ・ LCSH : Library of Congress Subject Headings in US
    • ・ We are making topic map from LCSH
    • - We downloaded it from “http://id.loc.gov/authorities/”
    • - Subject Headings : 372, 399
    • - BT-NT, RT, inScheme, closeMatch and sameAs relation: uncountable (because, we have not finished full conversion)
    • ・ RDF (SKOS) to Topic Maps using Omnigator
    • - SH (core:Concept, core:ConceptScheme) etc. -> Topics
    • - BT-NT, RT, inScheme, closeMatch, sameAs relation -> Associations
    • - scopeNote, created, modified, comment etc. -> Occurrences
    • ・ SHs have each own identifiers as URI that can be used as PSIs
    • (e.g. http://id.loc.gov/authorities/sh85000002#concept)
    • ・ LCSH has already exposed on the Web in consideration of Linked data
  • 14. LCSH Ontology
    • Ontology graph of LCSH topic map
  • 15. LCSH topic map application
    • Screen shots of the application
  • 16. 4. Practical use of Subject Headings Subject Headings can be used as Organized PSI to organize, control and link information ・ According to SH, organizing internal and external information ・ In order to enrich our own subjects and vocabularies, merging them with SH ・ Multilanguage mapping using LCSH as a core system ・ SH providing web service using TMRAP or SDShare protocol ・ To link SH with words within our tools such as Hu-Time ・ To use SH for thesaurus query
  • 17. Example 1: Organizing Wikipedia ・ Organizing Wikipedia according to SH ・ Available links to Wikipedia (NDLSH: 12051, BSH: 6086) Articles of Wikipedia NDLSH, BSH or LCSH
  • 18. Organizing Wikipedia Beer Hop Malt Wines and Spirits Liquor Amenities of life Wine Whiskey Fruit liquor Brandy Barley Beer Distilled liquor The world around “Beer” in NDLSH
  • 19. Organizing Wikipedia We can easily generate Wikipedia’s address “ http://ja.wikipedia.org/wiki/” + “ ビール” (SH)
  • 20. Example 2: Enrich our own subjects Sometimes SH doesn’t have enough subjects or vocabulary though it is very hard to gather enough subjects from scratch by ourselves By merging our own subjects with SH we can get enriched subjects SH Our own subjects merge Beer Ale Beer Lager Bock Pilsner IPA Barley Wine
  • 21. Example 3: Mapping between multi-language If each language is mapped to LCSH, multi-language mapping will be achieved NDLSH or BSH (Japanese) LCSH (English) merge merge merge merge Norwegian SH (Norwegian) e.g. Japanese Norwegian mapping via LSCH (English) ビール Beer Øl
  • 22. Mapping between multi-language Link from NDLSH to LCSH (USE-UF relation between NDLSH and LCSH) LCSH
  • 23. Example 4: Web service for providing Subject Headings Ontopia - Navigator Framework - Query engine Topic Maps Web Application - JSP Page Topic Map SH Topic Map Ontopia - Navigator Framework - Query engine Topic Maps Web Application - JSP Page Client SH providing Web service “ Term or Subject” “ Subject” topic Request SH Return SH related TM fragments SH related information Subject Headings providing web service using TMRAP or SDShare Information from client’s Web application
  • 24. 5. Demo I will do short demo if I have enough time
  • 25. 6. Conclusion ・ CIAS has already stored huge amount of information to organize ・ Many well organized knowledge has already existed and we are trying to use them to organize information ・ We are focusing attention on Subject Headings and thesauri such as NDLSH, BSH, LCSH, etc. ・ We are making topic maps and their web application from them ・ Topic maps can inherit Subject Headings and their relationships such as BT-NT, RT and USE-UF naturally ・ According to the relationships, information can be linked and organized ・ there are many practical way to use them ・ By providing Subject Headings as topic maps and PSI for use in the context of Linked Topic Maps, they will become powerful elements and they will be used in many way
  • 26. 7. Future work ・ To prompt NDL and JLA to expose their SH with IRI ・ Continue to try to achieve multi-language mapping using SH ・ Continue to try to realize the web service for providing SH ・ Continue to try to merge our domain subjects with SH ・ Continue to try to find out good ways to link SH with information resources
  • 27. ありがとう ございました。 Tusen Takk!

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