• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.43-66
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• 2008

Currently be proceeded a lot of researchers for 'user information demand description' for interface of an information retrieval system or Web search engines, but user information demand description for a natural language form is a difficult situation. These reasons are as they cannot provide the semantic similarity that an information retrieval model can be completely satisfied with variety regarding an information demand expression and semantic relevance for user information description. Therefore, this study using the description logic that is a knowledge representation base of OWL and a vector model-based weight between concept, and to be able to satisfy variety regarding an information demand expression and semantic relevance proposes a decision way for perfect assistances of user information demand description. The experiment results by proposed method, semantic similarity of a polyseme and a synonym showed with excellent performance in decision.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.4
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• pp.181-190
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• 2013

In this study, a total of 51 cases of hydraulic model tests has been conducted for various wave conditions and slope angles of breakwater to develop a stability formula for Rakuna-IV armoring a rubble-mound breakwater. The stability number of the formula is expressed as a function of relative damage, number of waves, structural slope, and surf similarity parameter. The stability formula is derived separately for plunging and surging waves, the greater of which is used. The transitional surf similarity parameter from plunging waves to surging waves is also presented. Lastly, to explain the stability of Rakuna-IV to the engineers who are familiar with the stability coefficient in the Hudson formula, the required weight of Rakuna-IV is calculated for varying significant wave height for typical plunging and surging wave conditions, which is then compared with those of the Hudson formula using several different stability coefficients.

• Journal of KIISE
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• v.43 no.9
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• pp.1043-1051
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• 2016

Web based entity linking cannot be applied in tweet entity linking because twitter documents are shorter in comparison to web documents. Therefore, tweet entity linking uses the information of users or groups. However, data sparseness problem is occurred due to the users with the inadequate number of twitter experience data; in addition, a negative impact on the accuracy of the linking result for users is possible when using the information of unrelated groups. To solve the data sparseness problem, we consider three features including the meanings from single tweets, the users' own tweet set and the sets of other users' tweets. Furthermore, we improve the performance and the accuracy of the tweet entity linking by assigning a weight to the information of users with a high similarity. Through a comparative experiment using actual twitter data, we verify that the proposed tweet entity linking has higher performance and accuracy than existing methods, and has a correlation with solving the data sparseness problem and improved linking accuracy for use of information of high similarity users.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.53-60
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• 2021

LOD-based recommender systems usually leverage the data available within LOD datasets, such as DBpedia, in order to recommend items(movies, books, music) to the end users. These systems use a semantic similarity algorithm that calculates the degree of matching between pairs of Linked Data resources. In this paper, we proposed a new approach to measuring semantic distance in an LOD-based recommender system by assigning weights converted from user ratings to links in the LOD graph. The semantic distance measurement model proposed in this paper is based on a processing step in which a graph is personalized to a user through weight calculation and a method of applying these weights to LDSD. The Experimental results showed that the proposed method showed higher accuracy compared to other similar methods, and it contributed to the improvement of similarity by expanding the range of semantic distance measurement of the recommender system. As future work, we aim to analyze the impact on the model using different methods of LOD-based similarity measurement.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.93-100
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• 2022

In the era of information overload on the Internet, the recommendation system, which is an indispensable function, is a service that recommends products that a user may prefer, and has been successfully provided in various commercial sites. Recently, studies to reflect the rating time of items to improve the performance of collaborative filtering, a representative recommendation technique, are active. The core idea of these studies is to generate the recommendation list by giving an exponentially lower weight to the items rated in the past. However, this has a disadvantage in that a time function is uniformly applied to all items without considering changes in users' preferences according to the characteristics of the items. In this study, we propose a time-aware collaborative filtering technique from a completely different point of view by developing a new similarity measure that integrates the change in similarity values between items over time into a weighted sum. As a result of the experiment, the prediction performance and recommendation performance of the proposed method were significantly superior to the existing representative time aware methods and traditional methods.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• The Journal of the Acoustical Society of Korea
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• v.31 no.4
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• pp.266-272
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• 2012

Speaker segmentation is a process of automatically detecting the speaker boundary points in the audio data. Speaker segmentation methods are divided into two categories depending on whether they use a prior knowledge or not: One is the model-based segmentation and the other is the metric-based segmentation. In this paper, we introduce the eigenvoice-based speaker weight distance method and compare it with the representative metric-based methods. Also, we employ and compare the Euclidean and cosine similarity functions to calculate the distance between speaker weight vectors. And we verify that the speaker weight distance method is computationally very efficient compared with the method directly using the distance between the speaker adapted models constructed by the eigenvoice technique.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.849-854
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• 2004

This paper proposes an algorithm to enhance the execution time of kNN in the document classification. The proposed algorithm is to enhance the execution time by minimizing the computing cost of the similarity between two documents by using the list of pairs, while the conventional kNN uses the iist of pairs. The 1ist of pairs can be obtained by applying the matrix transposition to the list of pairs at the training phase of the document classification. This paper analyzed the proposed algorithm in the time complexity and compared it with the conventional kNN. And it compared the proposed algorithm with the conventional kNN by using routers-21578 data experimentally. The experimental results show that the proposed algorithm outperforms kNN about $90{\%}$ in terms of the ex-ecution time.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.1
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• pp.83-90
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• 2016

A subtidal marine benthic algal vegetation at Ulleungdo Island, the eastern coast of Korea was investigated to clarify the community structure and vertical distribution by quadrat method at seven stations in May and August 2014. The total number of marine algal species was 148 species composed of the green algae of 12 species, the brown algae of 40 species and the red algae of 96 species. Mean biomass in dry weight was $94.8g\;dry\;weight\;m^{-2}$ in study sites, $98.1g\;dry\;weight\;m^{-2}$ in upper tidal level, and $86.6g\;dry\;weight\;m^{-2}$ in middle level. The R/P and (R+C)/P value reflecting flora characteristic were 1.9 and 2.3, respectively. Three groups produced by cluster analysis, one including sites Neunggeol, Daepung, Jukdo, second including sites Gongam, Ssangjeongcho and the other including sites Gwaneum, Hangnam, showed meaningful difference in similarity (about 40%), each other. The number of marine algal species and biomass in Ulleungdo Island area were markedly reduced comparing that in the previous studies. This result may suggest probably change of algal vegetation in future, considering the physical and chemical pollutions loaded in the coastal marine environment of this area.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1439-1448
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• 2005

In this paper, after similarity of color information in above toro and geometry position analysis of important characteristic information in face and abstraction object that is inputted detects face area using comparison, describe about method to do user certification using ratio information and hair spring degree. Face abstraction algorithm that use color information has comparative advantages than face abstraction algorithm that use form information because have advantage that is not influenced facial degree or site etc. that tip. Because is based on color information, change of lighting or to keep correct performance because is sensitive about color such as background similar to complexion is difficult. Therefore, can be used more efficiently than method to use color information as that detect characteristic information of eye and lips etc. that is facial importance characteristic element except color information and similarity for each object achieves comparison. This paper proposes system that eye and mouth's similarity that calculate characteristic that is ratio red of each individual after divide face by each individual and is segmentalized giving weight in specification calculation recognize user confirming similarity through search. Could experiment method to propose and know that the awareness rate through analysis with the wave rises.