When developing topic maps and their applications, key challenges are how to pick up the main subjects in targeted domains and how to systematize those subjects. This paper introduces a topic map development about topic map case examples. It also introduces what kinds of subjects were extracted and how the identifiers of those subjects were given and how those subjects were classified in the first version. Then the difficulties which were emerged during the development are discussed. In order to promote sharing of the case examples and make good use of them, I provide some consideration and future works.

1. TMRA 2008 Topic map for Topic Maps case examples 2008-10-17, Leipzig, Germany Motomu Naito (motom@green.ocn.ne.jp) Knowledge Synergy Inc. http://www.knowledge-synergy.com/

3. 1. Purpose, Method Purpose - To investigate and analyze Topic Maps researches, case examples, etc. - To introduce the process of Topic Maps and its web application development - To report the results (the map of Topic Maps world) - To answer questions about Topic Maps and promote it Method - Collect and Analyze Topic Maps case examples - Make a topic map to organize the collected data - Make a topic map application to navigate and show them

4. 2. Target of investigation At the first step, presentations at three conferences held in 2007 was tergeted 1. Topic Maps 2007 ( 2007.3.20-21, Oslo, Norway) http://www.topicmaps.com/tmc/conference.jsp?conf=TM2007 The number of targeted presentation : 24 2. TMRA 2007 (2007.10.10-12, Leipzig, Germany) http://www.informatik.uni-leipzig.de/~tmra/2007/ The number of targeted presentation : 27 3. AToMS 2007 (2007.12.12, Kyoto, Japan) http://www.knowledge-synergy.com/news/atoms2007.html The number of targeted presentation : 16 Total number of presentations : 67

5. 3. Making process - Data collection and analysis - Ontology making - Topic map making - Application making

6. 3.1 Data collection and analysis - The following data and others were collected on EXCEL by hand - Selected main candidate subjects of the topic map

8. 3.2 Ontology making Ontology was made according to the selected subjects and relations between them Ontology diagram of the topic map - Squares represent Topic types - Lines represent Association types

10. The number of types and instances The number of types The number of instances 1 Occurrence 30 Association Role 15 Association 17 Topic The number of types Type 1843 Total 67 Occurrence 1094 Association 682 Topic The number of instances Instance

12. 4. Demo The application for Topic Maps case example topic map Screen shots of the application

13. 5. Results The application can navigate the topic map from various view points, and show various results, for example, the number of presentations Country basis ranking 4 UK 5 7 USA 4 10 Japan 3 15 Germany 2 16 Norway 1 The number of presentation Country Ranking

14. Person basis ranking Industrial domain basis ranking 3 Sam Gyun Oh 1 3 Michihiko Setogawa 1 3 Markus Ueberall 1 3 Lars Marius Garshol 1 The number of presentation Person Ranking 4 Manufacturing 4 7 Government 3 15 Education-Learning support 2 38 Information and communications 1 The number of presentation Industrial domain Ranking

15. Organization basis ranking 3 J.-W.-Goethe University 2 3 Networked Planet 2 3 National Institute of Informatics 2 3 Ontopedia 2 3 University Leipzig 2 3 Sungkyunkwan University 2 3 Hitachi System and Services 2 8 Bouvet 1 The number of presentation Organization Ranking

16. 6. Issues and discussion - Coding scheme of Subject Identifier - Classification scheme - Appropriate metadata for posting

17. (1) Coding scheme of Subject Identifier I used the following identifier for person http://www.knowledge-synergy.com/psi/tmcase/person# MaicherLutz This kind of identifier can’t solve the homonym, synonym and polysemy problem (How identify persons who have the same family and personal name) Alternative 1 . #MaicherLutz + birth date + birth place + … Does it need never ending expansion? Alternative 2 . #MaicherLutz + some digits (for example, MaicherLutz-001) Is it intuitive, correspondable? Other alternative?

18. I know at least two Micheal Jackson - famous singer - famous beer hunter Wikipedia does not solve this problem in reality Wikipedia uses the following URL: - for famous singer http://en.wikipedia.org/wiki/ Michael_Jackson - for famous beer hunter http://en.wikipedia.org/wiki/ Michael_Jackson_(writer) - for others http://en.wikipedia.org/wiki/ Michael_Jackson_(disambiguation)#Other

19. (2) Classification scheme For industrial domain, I luckily found Japan Industrial code This code system consist of 4 level categories The first level (L category) is the following: A: Agriculture B: Forestry C: Fisheries D: Mining E: Construction F: Manufacturing G: Electricity-Gas-Heat supply and Water H: Information and Communications I: Transport J: Wholesale and Retail trade K: Finance and Insurance : O: Education-Learning support : R: Government- N.E.C. S: Industries unable to classify

20. For purpose , target knowledge/information , providing services , etc., I unluckily have not found applicable classification scheme To make up new classification scheme is very difficult and time consuming work The processes to make classification scheme, for example, are the following: 1. Attach digested word to the targets 2. Enumerate the words on the big board 3. Categorize the words and give titles We need to invent a good way

21. The process to make classification scheme scope of Topic Maps Topic Maps constraint language Dublin Core in Topic Maps Dublin Core Abstract Model and the TMDM learning of introductory physics Course management Information architecture for e-learning e-learning environment Mountain knowledge Knowledge framework Knowledge management personal knowledge architect information Application development environment Collaboration environment Knowledge management environment Ruby Topic Maps environment Knowledge management Development environment Standard activity E-learning Categorization of activity purpose

22. (3) Appropriate meta data for posting If there are good classification system and shown to authors, they can select suitable categories and attach them as metadata Topic Maps community should construct common vocabulary and classification system

24. 8. Conclusion and Future work (1) Conclusion - I can navigate 67 presentations and their documents from various view points easily and efficiently - I can use the topic map for my Topic Maps activity and Topic Maps popularization activity Future work - Review and improve the ontology - Add more viewpoints - Review and improve Identifier coding system - Review and improve Classification system - Open the topic map and the application through website

25. Conclusion and Future work (2) Future work - Add more case example and related information

26. Conclusion and Future work (3) Future work With support of topic-mappers, for sales promotion I would like to improve, enhance the TM and find answers to “ Benefits and Promising Applications of Topic Maps ” - Key strengths - Cool things become possible with Topic Maps - Achievable goals - Principal applications - Key functions and services - What TM can do and traditional technology can’t do Ref. Annex A of ISO/IEC 13250-1 ( http://www.itscj.ipsj.or.jp/sc34/open/1045.htm )