• The Journal of Information Systems
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• v.8 no.1
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• pp.27-44
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• 1999

Case-Based Reasoning(CBR) offers a new approach for developing knowledge based systems. CBR has several research issues which can be divided into two categories : (1) static issues and (2) dynamic issues. The static issues are related to case representation scheme and case data model, that is, focus on casebase which is a repository of cases. The dynamic issues, on the other hand, are related to case retrieval procedure and problem solving process, i.e. case adaptation phase. This research is forcused on retrieval procedure Traditional query processing accepts precisely specified queries and only provides exact answers, thus requiring users to fully understand the problem domain and the casebase schema, but returning limited or even null information if the exact answer is not available. To remedy such a restriction, extending the classical notion of query answering to approximate query answering(AQA) has been explored. AQA can be achieved by neighborhood query answering or associative query answering. In this paper, neighborhood query answering technique is used for AQA. To reinforce the CBR process, a new retrieval procedure(cooperative CBR) using neighborhood query answering is proposed. An neighborhood query answering relaxes a query scope to enlarge the search range, or relaxes an answer scope to include additional information. Computer Aided Process Planning(CAPP) is selected as cooperative CBR application domain for test. CAPP is an essential key for achieving CIM. It is the bridge between CAD and CAM and translates the design information into manufacturing instructions. As a result of the test, it is approved that the problem solving ability of cooperative CBR is improved by relaxation technique.

• Asia pacific journal of information systems
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• v.8 no.1
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• pp.43-64
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• 1998

Cooperative query answering is a research effort to develop a fault-tolerant and intelligent database system using the semantic knowledge base constructed from the underlying database. Such knowledge base has two aspects of usage. One is supporting the cooperative query answering process for providing both an exact answer and neighborhood information relevant to a query. The other is supporting ongoing maintenance of the knowledge base for accommodating the changes in the knowledge content and database usage purpose. Existing studies have mostly focused on the cooperative query answering process but paid little attention to the dynamic knowledge base maintenance. This paper proposes a multi-level knowledge representation framework called Knowledge Abstraction Hierarchy(KAH) that can not only support cooperative query answering but also permit dynamic knowledge maintenance, On the basis of the KAH, a knowledge abstraction database is constructed on the relational data model and accommodates diverse knowledge maintenance needs and flexibly facilitates cooperative query answering. In terms of the knowledge maintenance, database operations are discussed for the cases where either the internal contents for a given KAH change or the structures of the KAH itself change. In terms of cooperative query answering, four types of vague queries are discussed, including approximate selection, approximate join, conceptual selection, and conceptual join. A prototype system has been implemented at KAIST and is being tested with a personnel database system to demonstrate the usefulness and practicality of the knowledge abstraction database in ordinary database application systems.

• Journal of Korean Society of Archives and Records Management
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• v.6 no.1
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• pp.113-136
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• 2006

This study aims for the above mentioned. After all, through the analysis of Electronic Question/Answering Function to understand a user's demand under online circumstances, groping for the method to provide an appropriate Archival Information Service is the most important thing. For this, in this study, it researched the users interviews and the research related to users as a precedence study, and the studies having examined the state of demanding information by users through analyzing the e-mail actually. Additionally, by looking over the study of Library and Information Science that is activated in a field of Electronic Question/Answering Function rather than Archival Science, as a matter of fact, the study has come up with the standard for analyzing Electronic Question/Answering Function. And based on the precedence study, the instances for the National Archives from USA, England, Australia and Canada were analyzed, and the chance of activating Archival Information Service were tried to grope for in the study. This study might be one of methodologies in examining the users study that is not activated yet in Archival Science. Therefore, the users study can be carried out in various methods as well as Electronic Archives/Answering Service. This study might be the important information in providing far better Archival Information Services. It is desirable that based on this opportunity, the study related to the various users by examining not only Electronic Archives/Answering Function but also Question/Answering of the users and the Archivists in the filed to the larger extend will be activated for Archival Science.

• The Journal of Korean Association of Computer Education
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• v.7 no.4
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• pp.43-50
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• 2004

Multi-lingual query answering system is the system which answers on the queries with several languages. LASSO[l] is the system that aims to answer the multi-lingual query. In this paper, we design and implement a Korean analysis system for LASSO. The Korean analysis system for query answering needs processing techniques of dialogue style. And the system must be practical and general so as to use on various domains. This system uses not dialogue processing techniques with high cost and low utility but heuristic rules with low cost and high utility. It is designed and implemented as a Korean interface of multi-lingual query answering system. The techniques of this system highly contribute to information retrieval and Korean analysis researches.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.4
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• pp.401-417
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• 2015

Question taxonomy is one of main approaches to understand the questioner's information need so that we can assign relevant answerers to the question submitted by the user. The goal of this study is to investigate question taxonomy of question and answering services, which are available online and in libraries and understand the characteristics of question answering services by type. In order to achieve the goal, this study examines the types of questions appeared in literature, specifically focusing on social reference, question answering systems, and reference services, and then provides a summary of question taxonomy found in question answering services.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.33-38
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• 2000

Krishnamoorphy and Raghavarao(1993) invented exact binomial and asymptotically normal test procedures for truthful answering in the repeated randomized response models under the assumption that two repeated response measures are independent. Under the same assumption, Lakshmi and Raghavarao(1992) suggested asymptotic chi-square test for respondents' truthful answering in the same models. In this article we detect the factors and the conditions with which two response variables might be independent, and find the condition for independence in the repeated randomized response models with considering untruthful answer. But, the condition of independence make the randomized model no meaning. Under the assumption of conditional independence between two response variables, we can apply the same logical statements on deriving the tests for truthful answering in the repeated randomized response models as in Krishnamoorphy and Raghavarao(1993).

• ETRI Journal
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• v.42 no.6
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• pp.899-911
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• 2020

The data in tables are accurate and rich in information, which facilitates the performance of information extraction and question answering (QA) tasks. TableQA, which is based on tables, solves problems by understanding the table structure and searching for answers to questions. In this paper, we introduce both novice and intermediate Korean TableQA tasks that involve deducing the answer to a question from structured tabular data and using it to build a question answering pair. To solve Korean TableQA tasks, we use S3-NET, which has shown a good performance in machine reading comprehension (MRC), and propose a method of converting structured tabular data into a record format suitable for MRC. Our experimental results show that the proposed method outperforms a baseline in both the novice task (exact match (EM) 96.48% and F1 97.06%) and intermediate task (EM 99.30% and F1 99.55%).

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.2
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• pp.131-156
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• 1998

Cooperative query answering is a research effort to develop a fault-tolerant and intelligent database system using the semantic knowledge base constructed from the underlying database. Such knowledge base has two aspects of usage. One is supporting the cooperative query answering Process for providing both an exact answer and neighborhood information relevant to a query. The other is supporting ongoing maintenance of the knowledge base for accommodating the changes in the knowledge content and database usage purpose. Existing studies have mostly focused on the cooperative query answering process but paid little attention on the dynamic knowledge base maintenance. This paper proposes a multi-level knowledge representation framework called Knowledge Abstraction Hierarchy (KAH) that can not only support cooperative query answering but also permit dynamic knowledge maintenance. The KAH consists of two types of knowledge abstraction hierarchies. The value abstraction hierarchy is constructed by abstract values that are hierarchically derived from specific data values in the underlying database on the basis of generalization and specialization relationships. The domain abstraction hierarchy is built on the various domains of the data values and incorporates the classification relationship between super-domains and sub-domains. On the basis of the KAH, a knowledge abstraction database is constructed on the relational data model and accommodates diverse knowledge maintenance needs and flexibly facilitates cooperative query answering. In terms of the knowledge maintenance, database operations are discussed for the cases where either the internal contents for a given KAH change or the structures of the KAH itself change. In terms of cooperative query answering, database operations are discussed for both the generalization and specialization Processes, and the conceptual query handling. A prototype system has been implemented at KAIST that demonstrates the usefulness of KAH in ordinary database application systems.

• Journal of KIISE
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• v.43 no.8
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• pp.927-932
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• 2016

With the explosive increase in the number of online medical documents, the demand for question-answering systems is increasing. Recently, question-answering models based on machine learning have shown high performances in various domains. However, many question-answering models within the medical domain are still based on information retrieval techniques because of sparseness of training data. Based on various information retrieval techniques, we propose an answer snippet retrieval model for question-answering systems of medical documents. The proposed model first searches candidate answer sentences from medical documents using a cluster-based retrieval technique. Then, it generates reliable answer snippets using a re-ranking model of the candidate answer sentences based on various sentence retrieval techniques. In the experiments with BioASQ 4b, the proposed model showed better performances (MAP of 0.0604) than the previous models.

• Asia pacific journal of information systems
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• v.25 no.4
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• pp.686-711
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• 2015

The performance of a novel medical question-answering engine called CliniCluster and existing search engines, such as CQA-1.0, Google, and Google Scholar, was evaluated using known-item searching. Known-item searching is a document that has been critically appraised to be highly relevant to a therapy question. Results show that, using CliniCluster, known-items were retrieved on average at rank 2 ($MRR@10{\approx}0.50$), and most of the known-items could be identified from the top-10 document lists. In response to ill-defined questions, the known-items were ranked lower by CliniCluster and CQA-1.0, whereas for Google and Google Scholar, significant difference in ranking was not found between well- and ill-defined questions. Less than 40% of the known-items could be identified from the top-10 documents retrieved by CQA-1.0, Google, and Google Scholar. An analysis of the top-ranked documents by strength of evidence revealed that CliniCluster outperformed other search engines by providing a higher number of recent publications with the highest study design. In conclusion, the overall results support the use of CliniCluster in answering therapy questions by ranking highly relevant documents in the top positions of the search results.