• International Journal of Knowledge Content Development & Technology
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• 제7권3호
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• pp.5-27
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• 2017

Retrieval of scholarly articles about a specific research issue is a routine job of researchers to cross-validate the evidence about the issue. Two articles that focus on a research issue should share similar terms in their core contents, including their goals, backgrounds, and conclusions. In this paper, we present a technique CCSE ($\underline{C}ore$ $\underline{C}ontent$ $\underline{S}imilarity$ $\underline{E}stimation$) that, given an article a, recommends those articles that share similar core content terms with a. CCSE works on titles and abstracts of articles, which are publicly available. It estimates and integrates three kinds of similarity: goal similarity, background similarity, and conclusion similarity. Empirical evaluation shows that CCSE performs significantly better than several state-of-the-art techniques in recommending those biomedical articles that are judged (by domain experts) to be the ones whose core contents focus on the same research issues. CCSE works for those articles that present research background followed by main results and discussion, and hence it may be used to support the identification of the closely related evidence already published in these articles, even when only titles and abstracts of the articles are available.

• Journal of Information Technology Applications and Management
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• 제21권3호
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• pp.35-52
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• 2014

This study aims to examine how the relationship with an information source affects people to perceive credibility of online information. We developed a conceptual framework that explained how people perceived information credibility when they were familiar with the information source and/or when the information source seemed credible. We then compared the models in two different contexts, namely, online review and social media sites, to examine differences. We surveyed 136 online social media users with their online shopping experiences. Among our eight hypotheses, three (H6: the personality similarity between an information provider and an information seeker enhances the perceived credibility of the former; H7: the credibility of an information provider produces a much stronger mediating effect in review sites than in social media sites; H8: the familiarity of an information seeker with an information provider produces a stronger mediating effect in social media sites than in review sites) are fully supported and four (H1: the credibility of an information provider has a positive influence on the perceived credibility of the online information; H2: the familiarity of an information seeker with an information provider has a positive influence on the perceived credibility of the online information; H3: the goal similarity between an information provider and an information seeker enhances the perceived familiarity of the latter with the former; H5: the personality similarity between an information provider and an information seeker enhances the perceived familiarity of the latter with the former) are partially supported. The hypothesis of H4: the goal similarity between an information provider and an information seeker enhances the perceived credibility of the former is rejected. The result confirms that credibility of information is strongly mediated by credibility of information source than familiarity with information source in online review sites and vice versa in social media sites.

• 산업기술연구
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• 제20권B호
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• pp.219-226
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• 2000

In this paper, we propose a new novel method for similarity search that supports time warping. Our primary goal is to innovate on search performance in large databases without false dismissal. To attain this goal, we devise a new distance function $D_{tw-lb}$ that consistently underestimates the time warping distance and also satisfies the triangular inequality. $D_{tw-lb}$ uses a 4-tuple feature vector extracted from each sequence and is invariant to time warping. For efficient processing, we employ a multidimensional index that uses the 4-tuple feature vector as indexing attributes and $D_{tw-lb}$ as a distance function. We prove that our method does not incur false dismissal. To verify the superiority of our method, we perform extensive experiments. The results reveal that our method achieves significant speedup up to 43 times with real-world S&P 500 stock data.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.75-88
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• 2023

In a specific domain, experts have different understanding of domain knowledge or different purpose of constructing ontology. These will lead to multiple different ontologies in the domain. This phenomenon is called the ontology heterogeneity. For research fields that require cross-ontology operations such as knowledge fusion and knowledge reasoning, the ontology heterogeneity has caused certain difficulties for research. In this paper, we propose a novel ontology matching model that combines word embedding and a concatenated continuous bag-of-words model. Our goal is to improve word vectors and distinguish the semantic similarity and descriptive associations. Moreover, we make the most of textual and structural information from the ontology and external resources. We represent the ontology as a graph and use the SimRank algorithm to calculate the structural similarity. Our approach employs a similarity queue to achieve one-to-many matching results which provide a wider range of insights for subsequent mining and analysis. This enhances and refines the methodology used in ontology matching.

• Asia pacific journal of information systems
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• 제9권2호
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• pp.19-37
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• 1999

Computer aided process planning(CAPP) is a key for implementing CIM. It is bridge between CAD and CAM and translates the design information into manufacturing instructions. Generally, manufacturing is an area where intelligent systems will not be able to rely on methods requiring formalized knowledge. Manufacturing lacks a body of knowledge that is specific, formalized, and rigorous, and which can be coded as rules or procedures. Thus expertise in manufacturing is developed over a period of many years. Case-based reasoning(CBR) offers a new approach for developing intelligent system. In the case-based approach the problem solving experience of the experts is encoded in the form of cases. CBR's retrieval process can be divided to two step. The first step is matching step, and the second step is selection step. For selecting base case, new preference heuristics were introduced using similarity concept. Similarity concept has three has three dimensions, i.e. entity similarity, structural similarity, and goal similarity. In this paper, bolt's process planning was selected an application domain. Following the test result, the new preference heuristics were approved as a useful procedure in CAPP.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 1부
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• pp.298-303
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• 2008

사용자의 과거 이동 경로 자료는 사용자의 현재 이동 위치를 예측하고 이외 관련된 서비스를 제공하는데 유용하게 사용될 수 있다. 본 논문에서는 사용자의 과거 이동 경로의 분석을 통하여 이동 중인 사용자의 시공간 위치예측 기술을 제안한다. 환경으로부터 발생한 사용자의 이동 경로를 수집하고 수집된 데이터에서 이동 경로 요약(Path Summarization)과 이동 경로 선택(Path Selection) 방법을 제안한다. 이동 경로 요약 방법은 환경으로부터 수집한 사용자의 이동 경로를 군집 분류하고, 이동 경로 선택 방법은 이동 중에 발생한 경로의 거리, 시간, 방향의 요소와 동적 정합법을 사용하여 유사성(Similarity)을 측정하며 유사성이 가장 높은 경로를 선택한다. 선택된 경로는 시간에 따른 공간 정보 빚 위치에 따른 시간 예측 서비스를 위하여 사용가능 하며, 실험을 통하여 유사성이 높은 이동 경로를 선택하는 모습을 확인하였다.

• 한국지능시스템학회논문지
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• 제16권5호
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• pp.568-574
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• 2006

사용자에게 적응된 서비스를 제공하기 위하여 환경으로부터 얻어지는 다양한 형태의 데이터를 이용한 다양한 방법이 연구되고 있다. 그 중 과거 이동 경로 자료는 사용자의 현재 이동 위치를 예측하고 이와 관련된 서비스를 제공하는데 유용하게 사용될 수 있다. 본 논문에서는 사용자의 과거 이동 경로의 분석을 통하여 이동중인 사용자의 시공간 위치예측 기술을 제안한다. 환경으로부터 발생한 사용자의 이동 경로를 수집하고, 수집된 데이터에서 이동 경로 선택(Path Selection) 방법을 이용한다. 이동 경로 선택 방법은 이동 중에 발생한 경로의 거리, 시간, 방향의 요소와 동적정합법을 사용하여 유사성(Similarity)을 측정하며 유사성이 가장 높은 경로를 선택한다. 선택된 경로는 시간에 따른 공간 정보 및 위치에 따른 시간 예측 서비스를 위하여 사용가능 하며, 실험을 통하여 유사성이 높은 이동 경로를 선택하는 모습을 확인하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.288-295
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• 1998

This paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of optimal similarity theshold value is very important for the accurate classification. So, we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements such that (1) some tolerant objects are required to be included in the same class as many as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and the data set is grounded into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method that all data are classified by using the lower approxi ation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. We apply the proposed classification method to the handwritten numeral character classification. problem and compare its classification performance and learning time with those of the feed forward neural network's back propagation algorithm.

• 한국언어정보학회지:언어와정보
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• 제14권1호
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• pp.113-144
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• 2010

The primary goal of this paper is to find a feasible way to answer the question: Does the similarity in meaning between verbs relate to the similarity in their subcategorization? In order to answer this question in a rather concrete way on the basis of a large set of English verbs, this study made use of various language resources, tools, and statistical methodologies. We first compiled a list of 678 verbs that were selected from the most and second most frequent word lists from the Colins Cobuild English Dictionary, which also appeared in WordNet 3.0. We calculated similarity measures between all the pairs of the words based on the 'jcn' algorithm (Jiang and Conrath, 1997) implemented in the WordNet::Similarity module (Pedersen, Patwardhan, and Michelizzi, 2004). The clustering process followed, first building similarity matrices out of the similarity measure values, next drawing dendrograms on the basis of the matricies, then finally getting 177 meaningful clusters (covering 437 verbs) that passed a certain level set by z-score. The subcategorization frames and their frequency values were taken from the ICE-GB. In order to calculate the Selectional Preference Strength (SPS) of the relationship between a verb and its subcategorizations, we relied on the Kullback-Leibler Divergence model (Resnik, 1996). The SPS values of the verbs in the same cluster were compared with each other, which served to give the statistical values that indicate how much the SPS values overlap between the subcategorization frames of the verbs. Our final analysis shows that the degree of overlap, or the relationship between semantic similarity and the subcategorization frames of the verbs in English, is equally spread out from the 'very strongly related' to the 'very weakly related'. Some semantically similar verbs share a lot in terms of their subcategorization frames, and some others indicate an average degree of strength in the relationship, while the others, though still semantically similar, tend to share little in their subcategorization frames.

• 유통과학연구
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• 제19권2호
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• pp.53-64
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• 2021

Purpose: Even though arranging images of products is a common practice in the online retail context, relatively little attention has been paid to the distance effect among alternatives, that is, how distance among displayed products can impact consumers' responses. Drawing on contagion theory, the primary goal of the current study is to investigate how spatial distance between two products in a product display can influence consumers' perceived similarity. Research design, data and methodology: This study used a 2(spatial distance: close vs. far) experimental design and collected data from undergraduate students in Korea through an online survey using Qualtrics. ANOVA was conducted to test the proposed effect, in which the dependent variables are the perceived similarity of usage occasion/purpose (Study 1) and the indexed differences of perceived brand statuses between two products (Study 2). Results: The results of both experiments indicated that the displayed products were perceived to be more similar to one another when products were presented close together (vs. far). Conclusions: The results help to fill a research gap and provide a better understanding of the role of physical distance in diverse marketing communications. This is especially useful when designing online shopping websites to form perceptions of brand images.