• Journal of the Korea Computer Industry Society
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• v.5 no.1
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• pp.1-8
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• 2004

As more and more information is processed and stored in the digital form, many techniques and systems have been developed for service multimedia informations in wireless internet. In this paper, we propose MSPOB(Multimedia Statistics Post Office Box) structure for service datum which are related with similarity to subject a set of documents through grouping. The proposed structure is determined by relationship of datum based on count index and inverted file and is determined it through the semantic similarity between objects

• Journal of Korean Library and Information Science Society
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• v.47 no.2
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• pp.51-70
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• 2016

The purpose of this study is to analyze the semantic relation network and valence-arousal dimension through the words that describe emotions in Korean language. The results of this analysis are summarized as follows. Firstly, each emotion word was semantically linked in the network. This particular feature hinders differentiating various types of "emotion words" in accordance with similarity in meaning. Instead, central emotion words playing a central role in a network was identified. Secondly, many words are classified as two categories at the valence and arousal level: (1) negative of valence and high of arousal, (2) negative of valence and middle of arousal. This aspects of Korean emotional words would be useful to analyze emotions in various text data of books and document information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.3021-3027
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• 2010

Example-based chatBot generates a response to user's utterance by searching the most similar utterance in a collection of dialogue examples. Though finding an appropriate example is very important as it is closely related to a response quality, few studies have reported regarding what features should be considered and how to use the features for similar utterance searching. In this paper, we propose a machine learning framework which uses various linguistic features. Experimental results show that simultaneously using both semantic features and lexical features significantly improves the performance, compared to conventional approaches, in terms of 1) the utilization of example database, 2) precision of example matching, and 3) the quality of responses.

• The Journal of the Korea Contents Association
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• v.16 no.4
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• pp.56-64
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• 2016

With the development of information technologies and the wide spread of smart devices, the number of users of social network services has increased exponentially. Studies that identify user preferences and recommend similar users in these social network services have been actively done. In this paper, we propose a new scheme to recommend social network friends with similar preferences through the moving pattern analysis of mobile users. The proposed scheme removes the meaningless trajectories via companions, short time trajectories, and repeated trajectories to determine the correct user preference. The proposed scheme calculates user similarity using the meaningful trajectories and recommends users with similar preferences as friends. It is shown through performance evaluation that the proposed scheme outperforms the existing schemes.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.501-503
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• 2015

Malicious(악의적인) + Code 즉, 악의적인코드를 포함한 소프트웨어라는 의미로 줄여 Malware(Malicious + Software) 라고 불리는 악성코드는 최근 네트워크와 컴퓨터의 급속한 발전에 따라 기하급수적으로 증가하고 있는 추세이다. 폭발적인 증가율 추세를 보이고 있는 악성코드의 위협을 대비하기 위해 악성코드에 대한 분석이 필요한데 그 분석의 종류로는 초기분석, 동적 분석, 정적분석으로 나누고 장, 단점을 정리하였다. 또한 악성코드 대량화에 따른 효율적인 분석과 빠른 의사결정을 위한 악성코드 유사도에 대한 연구를 소개하고 API Call Sequence와 분류된 API를 이용한 악성행위 유사도 판별법을 제시하고 실험하였다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3874-3884
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• 2000

An information retrieval system has to retrieve all and only documents which are relevant to a user query, even if index terms and query terms are not matched exactly. However, term mismatches between index terms and qucry terms have been a serious obstacle to the enhancement of retrieval performance. In this paper, we discuss automatic term normalization between words in text corpora and their application to a Korean information retrieval system. We perform two types of term normalizations to alleviate semantic term mismatches: equivalence class and co-occurrence cluster. First, transliterations, spelling errors, and synonyms are normalized into equivalence classes bv using contextual similarity. Second, context-based terms are normalized by using a combination of mutual information and word context to establish word similarities. Next, unsupervised clustering is done by using K-means algorithm and co-occurrence clusters are identified. In this paper, these normalized term products are used in the query expansion to alleviate semantic tem1 mismatches. In other words, we utilize two kinds of tcrm normalizations, equivalence class and co-occurrence cluster, to expand user's queries with new tcrms, in an attempt to make user's queries more comprehensive (adding transliterations) or more specific (adding spc'Cializationsl. For query expansion, we employ two complementary methods: term suggestion and term relevance feedback. The experimental results show that our proposed system can alleviatl' semantic term mismatches and can also provide the appropriate similarity measurements. As a result, we know that our system can improve the rctrieval efficiency of the information retrieval system.

• Journal of the Korea Computer Graphics Society
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• v.11 no.4
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• pp.11-20
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• 2005

본 논문에서는 모션데이터의 재사용성을 높이기 위해 '유추적 이식(analogic transplanting)'이라는 새로운 방법을 제시한다. 일반적으로 사람 또는 동물의 동작에서 각 관절은 상호 연관성을 가지고 움직인다. 그러므로 신체를 몇 부분으로 나누었을 때 나누어진 한 부분의 유사성을 바탕으로 다른 부분과의 결합 가능성을 유추할 수 있게 된다. 본 연구에서는 신체 부분의 의미적 유사성을 판단하기 위해 주성분분석법과 클러스터링 기법을 사용하였다. 유추적 이식 방법은 매우 자연스러운 동작을 합성할 수 있으며, 충돌을 피할 수 있는 단순하고 저비용의 방법을 제공하며, 물리적 요소를 고려하지 않아도 결과적으로 매우 안정된 동작을 풍부하게 생성해 낸다.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.625-632
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• 2006

Collaborative filtering is a technology that aims at teaming predictive models of user preferences. Collaborative filtering systems have succeeded in Ecommerce market but they have shortcomings of high dimensionality and sparsity. In this paper we propose the nearest neighbor collaborative filtering method using non-negative matrix factorization(NNMF). We replace the missing values in the user-item matrix by using the user variance coefficient method as preprocessing for matrix decomposition and apply non-negative factorization to the matrix. The positive decomposition method using the non-negative decomposition represents users as semantic vectors and classifies the users into groups based on semantic relations. We compute the similarity between users by using vector similarity and selects the nearest neighbors based on the similarity. We predict the missing values of items that didn't rate by a new user based on the values that the nearest neighbors rated items.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.579-586
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• 2018

With the beginning of the new era of bigdata, information extraction or prediction are an important research area. Here, we present the acquisition of semi-automatically curated large-scale biological database and the prediction of enzyme reaction annotation for analyzing the pharmacological activities of drugs. Because the xenobiotic metabolism of pharmaceutical drugs by cellular enzymes is an important aspect of pharmacology and medicine. In this study, we apply and analyze similarity models to predict bimolecular reactions between human enzymes and their corresponding substrates. Thirteen models select to reflect the characteristics of each cluster in the similarity model. These models compare based on sensitivity and AUC. Among the evaluation models, the Simpson coefficient model showed the best performance in predicting the reactivity between the enzymes. The whole similarity model implement as a web service. The proposed model can respond dynamically to the addition of reaction information, which will contribute to the shortening of new drug development time and cost reduction.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.1753-1756
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• 2003

소프트웨어 생산성과 품질을 개선하기 위한 방안으로 컴포넌트 기반의 소프트웨어 개발이 전개되고 있다. 소프트웨어 컴포넌트 라이브러리를 재사용하기 위해서는 재사용 가능한 컴포넌트를 효율적으로 수집하여 분류, 저장, 검색하여야한다. 기존의 요구사항 정형화 기법들은 요구사항들 간의 의미적 관계를 표현하는 데 초점을 맞추고 있어 컴포넌트 검색에는 적합하지 않으므로 본 연구에서는 개발하려는 유즈케이스 다이어그램을 구문분석을 거쳐 명세하여 질의 정보를 추출하였다. 기존의 자연어를 기반으로 하는 컴포넌트의 비정형적인 명세를 컴포넌트 검색과 조립에 필요한 정보를 효율적으로 얻을 수 있도록 구문분석과 추상화 단계를 거쳐 정형화된 중간형태의 명세로 전환하고 제안한 유사도를 사용하여 컴포넌트를 검색하고자 한다. 또한 개괄명세와 상세명세를 통해 컴포넌트 검색에 필요한 정보를 추출할 뿐만 아니라 컴포넌트의 aspect을 이용하여 컴포넌트 조림에 필요한 정보도 얻을 수 있다. 2차 질의를 통해 컴포넌트 검색의 정확도를 향상시키고 명세를 추상화시켜 검색의 재현율을 향상시킨다.