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Application and Evaluation of Topic Maps for the Cultural Resource Data

Presentation, was published by Motomu Naito at 2004-09-03

The objective of this research is to explore and study Topic Maps for large data.

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The objective of this research is to explore and study the new paradigm for effectively utilizing and evaluating, from users’ perspective, not from creators’, diverse cultural resource data that are archived at libraries, museums, research institutes, universities, and corporations today. The research starts with applying the Topic Map paradigm to relatively small data. The data size is then gradually scaled up in order to examine the applicability of the Topic Map technology to large-scale database, where it is expected to be most effective. For the small data, we use 227 pieces of graphic data from “Genji Monogatari (the Tale of Genji)” and related hypertext data in Japanese and English. As extraction of topics, grouping and association to other graphic data and topics can be done with ease on graphic data, we expect that our experiment will serve as a model which users may follow when they attempt to build Topic Maps on the same graphic data later on. We are still in the early phase of the research and expect many things to be done in order to attain our ultimate goal. In this paper, we put forth our view of anticipated future developments in the research method as well as in the research itself and invite advice and/or criticism from readers. In the later phase of the research we plan to apply the Topic Map system to large-scale database, evaluate its usefulness and effectiveness and discuss issues found in the process. We hope that eventually the knowledge and theory developed through this research will help manage, analyze, and evaluate massive and diverse cultural resource data that have been accumulated in our society over hundreds of years and also help bring users’ point of view to the forefront in the world of computer.



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As a former information scientist, I am fascinated since 1999 by the capabilities for building Topic Maps-based knowledge systems having the potential to augment human mind. One can model arbitrary knowledge organization systems, deal with semantic heterogeneity, collocate all facts about one subject in one logical place, and with TMQL have semantic retrieval on federated semantic networks. Therefore I expect bright prospects for business concepts building on the exchange of such knowledge snippets via semantic knowledge services.

Alexander Sigel
Topic Maps Lab auf der Cebit 2011

Graduate from the Topic Maps Lab