• Journal of KIISE
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• v.43 no.5
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• pp.553-561
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• 2016

Several machine learning techniques are able to automatically populate ontology data from web sources. Also the interest for large scale ontology reasoning is increasing. However, there is a problem leading to the speculative result to imply uncertainties. Hence, there is a need to consider the reliability problems of various data obtained from the web. Currently, large scale ontology reasoning methods based on the trust value is required because the inference-based reliability of quantitative ontology is insufficient. In this study, we proposed a large scale OWL Horst reasoning method based on a confidence value using spark, a distributed in-memory framework. It describes a method for integrating the confidence value of duplicated data. In addition, it explains a distributed parallel heuristic algorithm to solve the problem of degrading the performance of the inference. In order to evaluate the performance of reasoning methods based on the confidence value, the experiment was conducted using LUBM3000. The experiment results showed that our approach could perform reasoning twice faster than existing reasoning systems like WebPIE.

• Journal of KIISE
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• v.43 no.1
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• pp.87-95
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• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.1-26
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• 2013

Estimating levels of user trust is important for maintaining continuous use of e-commerce systems because trust alleviates user concerns about the invisibility of service providers or their reputation. Conventional trust estimation approaches such as policy-and reputationbased reasoning have focused on the experience of e-commerce systems at an early stage. However, only a few trust reasoning methods have considered the mature stage, which is more related to continuance intention. We propose a trust reasoning method dedicated to the mature stage of using e-commerce systems. In particular, a new method of unobtrusively estimating the degree of user intimacy is developed, because intimacy has been highly associated with trust as well as reputation. Our experiments show that the proposed method is valid and can be used in conjunction with reputation-based trust reasoning.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.170-175
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• 2022

일반적으로 대규모 언어 모델들은 다량의 데이터를 오랜시간 사전학습하면서 레이블을 예측하기 위한 성능을 높여왔다. 최근 언어 모델의 레이블 예측에 대한 정확도가 높아지면서, 언어 모델이 왜 해당 결정을 내렸는지 이해하기 위한 신뢰도 높은 Natural Language Explanation(NLE) 을 생성하는 것이 시간이 지남에 따라 주요 요소로 자리잡고 있다. 본 논문에서는 높은 레이블 정확도를 유지하면서 동시에 언어 모델의 예측에 대한 신뢰도 높은 explanation 을 생성하는 참신한 자연어 추론 시스템을 제시한 Natural-language Inference over Label-specific Explanations(NILE)[1] 을 소개하고 한국어 데이터셋을 이용해 NILE 과 NLE 를 활용하지 않는 일반적인 자연어 추론 태스크의 성능을 비교한다.

• Communications of Mathematical Education
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• v.13 no.2
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• pp.425-455
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• 2002

본 연구는 수학교육에서 강조되고 있는 수학적 힘의 구성 요소 중의 하나인 수학적 추론 능력에 대한 교사들의 구체적인 이해를 돕고, 문제 해결 과정에서 학생들의 추론 능력을 분석하고 평가하는 데 도움을 주기 위해 문헌 연구 및 학생반응 분석결과에 기초하여 귀납적, 유비적, 연역적 추론능력에 대한 평가기준을 개발하였다. 또한, 개발된 평가기준을 구체적인 문제에 적용하였으며 이를 기초로 문제점을 수정 ${\cdot}$ 보완한 후, 전문가의 타당성 검증과 동일한 학생반응에 대한 채점결과의 일치도를 알아봄으로써 신뢰도 검증을 실시하였다.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.355-366
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• 2002

Nonparametric statistical inference for the parameter of logit model were examined. Usually nonparametric approach is milder than parametric approach based on normal theory assumption. We compared the two nonparametric methods for legit model, the bootstrap and random permutation in the sense of coverage probability. Monte Carlo simulation is conducted for small sample cases. Empirical power of hypothesis test and coverage probability for confidence interval estimation were presented for simple and multiple legit model respectively. An example were also introduced.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.147-158
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• 2008

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.2
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• pp.217-242
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• 2006

This Study reviewed 86 research articles using inferential statistics published in 2001-2004 in 4 korean core journals in the field of library and information science. Sampling methods, response rates and nonresponse bias, reliability test, and inferential statistic techniques used in the articles were critically reviewed and analyzed. Nonprobability sampling was mostly used. Average response rate was 74.47%. Parametric statistics were mostly used. Some misunderstandings in using each inferential statistics, especially Reliability Test, Multiple Regression, Factor Analysis, MDS, etc. were reported in this study.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.689-699
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• 2013

This paper studies the partial credibility application method by assuming the empirical prior or noninformative prior informations in auto insurnace business where intensive rating segmentation is expanded because of premium competition. Expanding of rating factor segmetation brings the increase of pricing cells, as a result, the number of cells for partial credibility application will increase correspondingly. This study is trying to suggest more accurate estimation method by considering the Bayesian framework. By using empirically well-known or noninformative information, inducing the proper posterior distribution and applying the Bayes estimate which is minimizing the error loss into the credibility method, we will show the advantage of Bayesian inference by comparison with current approaches. The comparison is implemented with square root rule which is a widely accepted method in insurance business. The convergence level towarding to the true risk will be compared among various approaches. This study introduces the alternative way of redcuing the error to the auto insurance business fields in need of various methods because of more segmentations.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.269-279
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• 2015

This paper considers inferences of random effects. We show that the proposed confidence distribution (CD) performs well in logistic regression for random intercepts with small samples. Real data analyses are also done to identify the subject effects clearly.