• International Journal of Knowledge Content Development & Technology
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• 제5권1호
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• pp.49-68
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• 2015

Given a query (a health question), retrieval of relevant frequently asked questions (FAQs) is essential as the FAQs provide both reliable and readable information to healthcare consumers. The retrieval requires the estimation of the semantic similarity between the query and each FAQ. The similarity estimation is challenging as semantic structures of Chinese healthcare FAQs are quite different from those of the FAQs in other domains. In this paper, we propose a conceptual model for Chinese healthcare FAQs, and based on the conceptual model, present a technique ECA that estimates conceptual similarities between FAQs. Empirical evaluation shows that ECA can help various kinds of retrievers to rank relevant FAQs significantly higher. We also make ECA online to provide services for FAQ retrievers.

• Journal of Information Processing Systems
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• 제5권3호
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.552-555
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• 1998

In dealing with representing knowledge under uncertainty there is a sustain tendency to increase flexibility in order to avoid problems of inconsistency in the knowledge. Many knowledge systems(information retrieval systems, expert system) include hybrid representation models. Funny retrieval systems appear as a complement or as an enrichment of this models. In this paper, we describe dynamic rule modification through situation assessment for uncertainty management.

• Asia pacific journal of information systems
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• 제16권4호
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• pp.203-215
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• 2006

The advent of KM(knowledge management) concept have led many organizations to seek an effective way to make use of their knowledge. But the absence of right tools for systematic handling of unstructured information makes it difficult to automatically retrieve and share relevant information that exactly meet user's needs. we propose a systematic method to enable content-based information retrieval from corpus of unstructured documents. In our method, a document is represented by using several key terms which are automatically selected based on their quantitative relevancy to the document. Basically, the relevancy is calculated by using a traditional TFIDF measure that are widely accepted in the related research, but to improve effectiveness of the measure, we exploited 'concept network' that represents term-term relationships. In particular, in constructing the concept network, we have also considered relative position of terms occurring in a document. A prototype system for experiment has been implemented. The experiment result shows that our approach can have higher performance over the conventional TFIDF method.

• International Journal of Knowledge Content Development & Technology
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• 제10권3호
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• pp.51-68
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• 2020

The library provides its services to satisfy the user's approach. Naturally, the development of library services may determine by considering the satisfaction of users. It traces users' perceptions regarding subject access highlighting problems in the retrieval of Bengali documents by subject. This study has designed to assess users' attitudes towards the retrievals of Bengali documents in OPAC through subject headings. For a collection of data, a representative sample has drawn from a large and heterogeneous population consisting of users in university libraries of West Bengal using a stratified sampling technique. Subsequently, under each of the universities, users' community was stratified into students, research scholars, and faculty members. Under each stratum, the sample selected on a random basis. The users met personally to collect relevant data, while they came to the library and went on to search OPAC. A structured schedule, prepared for the purpose, was presented before library users and consequently, interviews and interpretations recorded systematically. In this manner, several factors have identified concerning subject searching and retrieval performance for Bengali documents. This study explores the access using subject headings in multilingual information retrieval systems. Moreover, the suitability of subject headings for retrieval of Bengali resources has ascertained from the users' point of view. The findings demand standard principles and rules for the construction of Bengali subject headings to maintain uniformity and consistency.

• International Journal of Knowledge Content Development & Technology
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• 제7권1호
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• pp.101-120
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• 2017

Healthcare professionals have edited many health questions (HQs) and their answers for healthcare consumers on the Internet. The HQs provide both readable and reliable health information, and hence retrieval of those HQs that are relevant to a given question is essential for health education and promotion through the Internet. However, retrieval of relevant HQs needs to be based on the recognition of the intention of each HQ, which is difficult to be done by predefining syntactic and semantic rules. We thus model the intention recognition problem as a text classification problem, and develop two techniques to improve a learning-based text classifier for the problem. The two techniques improve the classifier by location-based and area-based feature weightings, respectively. Experimental results show that, the two techniques can work together to significantly improve a Support Vector Machine classifier in both the recognition of HQ intentions and the retrieval of relevant HQs.

• International Journal of Knowledge Content Development & Technology
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• 제10권3호
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• pp.21-31
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• 2020

The study examined information retrieval tools as predictors for information resources utilization, four research questions, and four hypotheses were made to guide the study. A descriptive survey was adopted for the study. Random sampling technique was used to select sample of 393 from a population of 557 academic staff registered in the University of Port Harcourt library. The questionnaire was adopted as a data collection instrument titled Information retrieval as predictors for information resources utilization (IRPIRUQ). Data were analyzed using both simple and multiple regression while analysis of variance (ANOVA) associate with regression was used for testing the hypotheses at 0.05 alpha level. The study revealed that information resources are under utilized as the OPAC and Online Databases are not easily accessed. Further findings showed that the academic staff made use of internet search engines more often than the OPAC and online databases. It was recommended among others that a new library software be installed in place of KOHA for wider connectivity and adequate distribution of software that will aid usage of the online databases and OPAC.

• 정보관리학회지
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• 제26권4호
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• pp.7-34
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• 2009

이야기 쓰기를 돕는 본문 및 문장 검색시스템의 구축을 위해서 (1) 이야기와 단락 및 문장의 구조를 분석하고 (2) 색인작성과 탐색 질문에 적용되는 언어 추론을 연구하였다. 이야기 쓰기에 필요한 이야기, 단락, 그리고 문장으로 구성된 사항 데이터베이스와 필요한 추론규칙으로 이루어진 지식베이스와 온톨로 지가 고안되었다. 추론의 기초인 실례(實例) 파일들은 시맨틱 웹 환경에서 작동될 마크업 언어 형식으로 만들어졌다. 시맨틱 웹 환경에서 실용적인 시스템이 되려면 단락과 문장을 정확히 대변하는 색인 방법론과 이를 정밀하게 지식베이스화 할 수 있는 마크업 언어의 창조가 필수적이라 사료된다.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2006년도 학술대회 1부
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• pp.926-930
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• 2006

Current image retrieval techniques have shortcomings that make it difficult to search for images based on a semantic understanding of what the image is about. Since an image is normally associated with multiple contexts (e.g. when and where a picture was taken,) the knowledge of these contexts can enhance the quantity of semantic understanding of an image. In this paper, we present a context-aware image retrieval system, which uses the context information to infer a kind of metadata for the captured images as well as images in different collections and databases. Experimental results show that using these kinds of information can not only significantly increase the retrieval accuracy in conventional content-based image retrieval systems but decrease the problems arise by manual annotation in text-based image retrieval systems as well.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.450-453
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• 1999

본 논문에서는 사용자와 시스템의 DB, 이미지데이터 및 언어적인 지식표현을 통해 구축된 지식기반 데이터베이스(KDB)와의 인터페이스 역할을 위한 협조적인 검색시스템을 구축하였다. 기존의 데이터베이스 검색시스템은 사용자의 요구에 정확히 일치하는 데이터가 시스템 내에 존재할 경우에만 해당 데이터를 제공하지만 그렇지 않은 경우에는 아무런 데이터도 제공할 수 없다. 이러한 문제점을 해결하기 위해 사용자의 요구에 일치하는 데이터가 데이터베이스 내에 존재할 때 인터페이스를 통해 해당 데이터를 제공하고, 만일 사용자의 요구에 일치하는 데이터가 존재하지 않을 경우에는 퍼지 클러스터링과 언어 레이블의 할당을 통한 지식기반 데이터베이스를 구축하여 사용사의 요구에 가장 근접한 데이터 및 이미지정보를 제시하도록 하였다.