• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.427-434
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• 2004

In this paper, we present a overview of content-based image retrieval (CBIR) systems: its results and its problems. We propose our CBIR system currently based on color and texture. From the CBIR systems. we discuss the way to add semantic values in image retrieval systems. There are 3 ways for adding them: concept definition, machine learning and man-machine interaction. Along with this we introduce our preliminary results and discuss them in the goal of reaching semantic retrieval. Different result representation schemes are presented. At last, we present our work to build a complete annotated image database and our image annotaion program.

• 한국경영과학회지
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• 제32권1호
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• pp.125-136
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• 2007

The study on query relaxation which provides approximate answers has received attention. In recent years, some arguments have been made that semantic relationships are useful to present the relationships among data values and calculating the semantic distance between two data values can be used as a quantitative measure to express relative distance. The aim of this article is a hierarchical metricized knowledge abstraction (HiMKA) with an emphasis on combining data abstraction hierarchy and distance measure among data values. We propose the operations and the query relaxation algorithm appropriate to the HiMKA. With various experiments and comparison with other method, we show that the HiMKA is very useful for the quantified approximate query answering and our result is to offer a new methodological framework for query relaxation.

• Asia pacific journal of information systems
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• 제18권1호
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• pp.79-96
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• 2008

One of the roles of the Semantic Web services is to execute dynamic intra-organizational services including the integration and interoperation of business processes. Since different organizations design their processes differently, the retrieval of similar semantic business processes is necessary in order to support inter-organizational collaborations. Most approaches for finding services that have certain features and support certain business processes have relied on some type of logical reasoning and exact matching. This paper presents our approach of using imprecise matching for expanding results from an exact matching engine to query the OWL(Web Ontology Language) MIT Process Handbook. MIT Process Handbook is an electronic repository of best-practice business processes. The Handbook is intended to help people: (1) redesigning organizational processes, (2) inventing new processes, and (3) sharing ideas about organizational practices. In order to use the MIT Process Handbook for process retrieval experiments, we had to export it into an OWL-based format. We model the Process Handbook meta-model in OWL and export the processes in the Handbook as instances of the meta-model. Next, we need to find a sizable number of queries and their corresponding correct answers in the Process Handbook. Many previous studies devised artificial dataset composed of randomly generated numbers without real meaning and used subjective ratings for correct answers and similarity values between processes. To generate a semantic-preserving test data set, we create 20 variants for each target process that are syntactically different but semantically equivalent using mutation operators. These variants represent the correct answers of the target process. We devise diverse similarity algorithms based on values of process attributes and structures of business processes. We use simple similarity algorithms for text retrieval such as TF-IDF and Levenshtein edit distance to devise our approaches, and utilize tree edit distance measure because semantic processes are appeared to have a graph structure. Also, we design similarity algorithms considering similarity of process structure such as part process, goal, and exception. Since we can identify relationships between semantic process and its subcomponents, this information can be utilized for calculating similarities between processes. Dice's coefficient and Jaccard similarity measures are utilized to calculate portion of overlaps between processes in diverse ways. We perform retrieval experiments to compare the performance of the devised similarity algorithms. We measure the retrieval performance in terms of precision, recall and F measure? the harmonic mean of precision and recall. The tree edit distance shows the poorest performance in terms of all measures. TF-IDF and the method incorporating TF-IDF measure and Levenshtein edit distance show better performances than other devised methods. These two measures are focused on similarity between name and descriptions of process. In addition, we calculate rank correlation coefficient, Kendall's tau b, between the number of process mutations and ranking of similarity values among the mutation sets. In this experiment, similarity measures based on process structure, such as Dice's, Jaccard, and derivatives of these measures, show greater coefficient than measures based on values of process attributes. However, the Lev-TFIDF-JaccardAll measure considering process structure and attributes' values together shows reasonably better performances in these two experiments. For retrieving semantic process, we can think that it's better to consider diverse aspects of process similarity such as process structure and values of process attributes. We generate semantic process data and its dataset for retrieval experiment from MIT Process Handbook repository. We suggest imprecise query algorithms that expand retrieval results from exact matching engine such as SPARQL, and compare the retrieval performances of the similarity algorithms. For the limitations and future work, we need to perform experiments with other dataset from other domain. And, since there are many similarity values from diverse measures, we may find better ways to identify relevant processes by applying these values simultaneously.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.863-875
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• 2017

The traditional text similarity measurement methods based on word frequency vector ignore the semantic relationships between words, which has become the obstacle to text similarity calculation, together with the high-dimensionality and sparsity of document vector. To address the problems, the improved singular value decomposition is used to reduce dimensionality and remove noises of the text representation model. The optimal number of singular values is analyzed and the semantic relevance between words can be calculated in constructed semantic space. An inverted index construction algorithm and the similarity definitions between vectors are proposed to calculate the similarity between two documents on the semantic level. The experimental results on benchmark corpus demonstrate that the proposed method promotes the evaluation metrics of F-measure.

• 복식
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• 제55권3호
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• pp.1-18
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• 2005

In this study, the transition characteristics of purple series color names appearing in the clothing of the Joseon Dynasty were examined, and the associational meaning of each name were investigated through various methods. The results are as follows; First, Such characteristics as continuity, differentiation, substitution could be observed through the investigation of color names of purple-series appeared on the clothing in the Joseon Dynasty period. Secondly, the associational meaning could be subdivided into; social position symbolic meanings, usage meanings, economic meanings, and thought meanings. The social position symbolic meanings could be observed mainly in the single names which has been used since the ancient times, usage meanings could be observed in a wide variety according to the individual color names. The economic meanings could be observed by comparing the value of colored cloths and colored threads. The thought meanings were mainly related with the Confucianism. Thirdly, the associational semantic structure were established on the basis of associational meanings of purple-series color names. Individual color name on the social position symbolic semantic structure symbolizes [government official] and [servant]. Through usage semantic structure individual color names could be understood structurally according to the social position, sex distinction, wearing situation, items of clothing, and structure of clothing. Individual names on the economic semantic structure were segmented by the semantic components of the values in [high], [medium], [low] prices, kinds and quantity of dyes. The thought semantic structure could be subdivided [Confucianism] and [The Thought of Taeil] in its semantic structure.

• 영재교육연구
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• 제25권2호
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• pp.195-216
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• 2015

직업가치관은 학생들의 직업동기 형성에 핵심적인 역할을 한다. 특히 지속가능한 미래 과학기술인력 공급과 육성을 위해서 과학영재들의 이공계 직업에 대한 직업가치관 형성은 중요한 의미를 지닌다. 이 연구는 사회네트워크 분석법을 통하여 중학교 과학영재와 일반학생들의 자신의 희망 직업과 관련된 직업 가치관을 비교 분석하였다. 이를 위하여 학생들이 희망하는 직업과 그 직업을 선택한 이유에 해당하는 직업가치관으로 구성된 네트워크를 구조화한 후, 사회 네트워크 분석법을 통해 산출된 네트워크의 구조적 지표들을 바탕으로 두 집단의 인식 네트워크의 특성을 확인하였다. 과학영재 학생들과 일반학생들의 네트워크를 비교분석한 결과, 첫째, 과학영재학생들은 창의성을 가장 중요한 직업가치관 요소로 생각하고 있었다. 둘째, 과학영재들은 직업선택 과정에서 일반학생들보다 다양한 직업가치관 요소들을 고려하고 있었다. 셋째, 과학영재들은 이공계열과 의약계열 직업 모두에 대해서 능력발휘와 같은 자아실현 관점을 중심으로 직업가치관을 고려했으나, 일반학생들은 사회봉사를 가장 큰 직업가치관 요소로 보고 있었다. 이 연구는 과학영재학생들의 희망 직업에 대한 직업가치관 구조가 일반학생과 많은 차이가 있음을 확인하고, 과학영재학생들의 진로 직업을 지도하는데 있어 직업가치관과 관련된 네트워크적 기초자료를 제공했다는데 그 의의가 있다.

• Journal of information and communication convergence engineering
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• 제11권3호
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• pp.179-184
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• 2013

Traditional clustering methods are usually based on the bag-of-words (BOW) model. A disadvantage of the BOW model is that it ignores the semantic relationship among terms in the data set. To resolve this problem, ontology or matrix factorization approaches are usually used. However, a major problem of the ontology approach is that it is usually difficult to find a comprehensive ontology that can cover all the concepts mentioned in a collection. This paper proposes a new document clustering method using semantic features and fuzzy relations for solving the problems of ontology and matrix factorization approaches. The proposed method can improve the quality of document clustering because the clustered documents use fuzzy relation values between semantic features and terms to distinguish clearly among dissimilar documents in clusters. The selected cluster label terms can represent the inherent structure of a document set better by using semantic features based on non-negative matrix factorization, which is used in document clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2000년도 춘계 학술대회 및 국제 감성공학 심포지움 논문집 Proceeding of the 2000 Spring Conference of KOSES and International Sensibility Ergonomics Symposium
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• pp.138-143
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• 2000

In order to investigate the relationship between fabric sound parameters and subjective sensation, each sound from 60 fabrics was recorded and analyzed by Fast Fourier transform. Level pressure of total sound (LPT), three coefficients (ARC, ARF, ARE) of auto regressive models, loudness (Z), and sharpness (Z) by Zwickers model were estimated as sound parameters. For subjective evaluation, seven sensation (softness, loudness, sharpness, clearness, roughness, highness, and pleasantness) was rated by both semantic differential scale (SDS) and free modulus magnitude estimation (FMME). As the results, the ARC values were positively proportional to both LPT and loudness (Z) values. In both of SDS and FMME, softness, clearness, and pleasantness were negatively correlated with loudness, sharpness, roughness, and highness. In regression models, softness and clearness by FMME were negatively affected by LPT뭉 ARC, while loudness, sharpness, roughness, and highness were positively expected. Regression models for pleasantness showed low values for R2.

• 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.

• 디지털산업정보학회논문지
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• 제9권1호
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• pp.59-72
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• 2013

In this paper, we propose a keyword query processing technique based on semantic relationships for OWL data. The proposed keyword query processing technique can improve user's search satisfaction by performing two types of associative search. The first associative search uses information inferred by the relationships between classes or properties during keyword query processing. And it supports to search all information resources that are either directly or indirectly related with query keywords by semantic relationships between information resources. The second associative search returns not only information resources related with query keywords but also values of properties of them. We design a storage schema and index structures to support the proposed technique. And we propose evaluation functions to rank retrieved information resources according to three criteria. Finally, we evaluate the validity and accuracy of the proposed technique through experiments. The proposed technique can be utilized in a variety of fields, such as paper retrieval and multimedia retrieval.