Won-Kyun Joo

Tag Based Collaborative Knowledge Management System with Crowdsourcing

Many researchers have taken an interest in a collective intelligence the development of the user-generated web. Knowledge publishing and management with interactions between users are mostly a social revolution from the wisdom of crowds. This paper proposes a tag based collaborative knowledge management system. It can be realized both the collective intelligence with the crowdsourcing system and the dynamic categorization with the collaborative tagging approach. In addition, we establish the trustworthy knowledge through significant role allotment between a user group and a knowledge expert group.

Service-Centric Object Fragmentation for Efficient Retrieval and Management of Huge XML Documents

Vast amount of XML documents raise interests in how they will be used and how far their usage can be expanded, This paper has two central goals: 1) easy and fast retrieval of XML documents or relevant elements; and 2) efficient and stable management of large-size XML documents, The keys to develop such a practical system are how to segment a large XML document to smaller fragments and how to store them. In order to achieve these goals, we designed SCOF(Service-centric Object Fragmentation) model, which is a semi-decomposition method based on conversion rules provided by XML database managers. Keyword-based search using SCOF model then retrieves the specific elements or attributes of XML documents, just as typical XML query language does. Even though this approach needs the wisdom of managers in XML document collection, SCOF model makes it efficient both retrieval and management of massive XML documents.

Development of Platform-Based Knowledge Map Service to get Data Insights of R&D Institution on User-Interested Subjects

Generally, any database is developed for the purpose of providing certain services for retrieval or management of data. If rules or processes designed to extract and analyze data in consideration of data characteristics are simplified and services are provided to users by visualizing analysis results in an easy-to-understand fashion, users will be able to get diverse insights easily without much cost or effort. If this kind of database is constructed and provided in NTIS database as well (e.g., project data, research outcome data such as paper and patent, personnel information, etc.) users might be able to get varied insights on national R&D data. The approach proposed in this study is significant in that it provides platform-based analysis services which can create a knowledge map after extracting and analyzing user-interested data and subsequently visualizing the results. Furthermore, this study is intended to make users obtain insight on a research institute more easily by providing analyzable additional information, not only as a list of research institutes in user-preferred subjects after providing the results of the analysis on R&D expenses, but also in regional distribution and annual changes in core research institutes, focusing on those taking part in national R&D programs in certain fields.

Study of Methods for Element Data Service for Electronic Documents Related to National R&D Projects

Abstract Major countries are supporting new knowledge creation and innovative activities by opening data so that the research results carried out by government budgets can serve as public goods. The Republic of Korea has also made research and development (R&D) reports and papers available in electronic file format, which is the result of national R&D programs for the general public. However, the extraction of meaningful information among unstructured data in text format has not satisfied researchers’ expectations. In order to evolve into a customized service reflecting the opinions of researchers, we investigated the demand for necessary contents and services at the planning stage of R&D projects. This study attempts to propose a method to offer significant information which shows a bigger unit than objects based on trend information with extraction and processing demands focusing on R&D reports based on the results of questionnaire survey and interviews with researchers. This study aims to provide the integration service, tentatively named ‘element data service’, of key sentences and table/figure images with a high demand for the utilization of researchers. The main procedure of the proposed method consists of the subject classification of the R&D report, the extraction of table/figure image, and the extraction of main sentence. We used public reports of the same classification published from 2012 to 2016 for the experiment and utilized the extractive summarization method for the copyright protection of report original text. After realizing the simple prototype, we examined the service possibility through the researcher target reviews.

Automatic Text Categorization Using Passage-based Weight Function and Passage Type

Researches in text categorization have been confined to whole-document-level classification, probably due to lacks of full-text test collections. However, full-length documents available today in large quantities pose renewed interests in text classification. A document is usually written in an organized structure to present its main topic(s). This structure can be expressed as a sequence of sub-topic text blocks, or passages. In order to reflect the sub-topic structure of a document, we propose a new passage-level or passage-based text categorization model, which segments a test document into several passages, assigns categories to each passage, and merges passage categories to document categories. Compared with traditional document-level categorization, two additional steps, passage splitting and category merging, are required in this model. By using four subsets of Reuters text categorization test collection and a full-text test collection of which documents are varying from tens of kilobytes to hundreds, we evaluated the proposed model, especially the effectiveness of various passage types and the importance of passage location in category merging. Our results show simple windows are best for all test collections tested in these experiments. We also found that passages have different degrees of contribution to main topic(s), depending on their location in the test document.