• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.157-162
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• 2022

Today, most data is stored in a database (DB). In the DB environment, the users requests the DB to find the data they wants. Similarity Query has predicate that explained by a similarity. However, in the process of processing the similarity query, it is difficult to use an index that can reduce the range of processed records, so the cost of calculating the similarity for all records in the table is high each time. To solve this problem, this paper defines a lightweight similarity function. The lightweight similarity function has lower data filtering accuracy than the similarity function, but consumes less cost than the similarity function. We present a method for reducing similarity query processing cost by using the lightweight similarity function features. Then, Chebyshev distance is presented as a lightweight similarity function to the Euclidean distance function, and the processing cost of a query using the existing similarity function and a query using the lightweight similarity function is compared. And through experiments, it is confirmed that the similarity query processing cost is reduced when Chebyshev distance is applied as a lightweight similarity function for Euclidean similarity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.366-369
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• 2007

In this paper we derived fuzzy entropy that is based on similarity measure. Similarity measure represents the degree of similarity between two informations, those informations characteristics are not important. First we construct similarity measure between two informations, and derived entropy functions with obtained similarity measure. Obtained entropy is verified with proof. With the help of one-to-one similarity is also obtained through distance measure, this similarity measure is also proved in our paper.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.175-186
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• 2022

This research article develops an Arabic books' recommendation system, which is based on the content similarity that assists users to search for the right book and predict the appropriate and suitable books pertaining to their literary style. In fact, the system directs its users toward books, which can meet their needs from a large dataset of Information. Further, this system makes its predictions based on a set of data that is gathered from different books and converts it to vectors by using the TF-IDF system. After that, the recommendation algorithms such as the cosine similarity, the sequence matcher similarity, and the semantic similarity aggregate data to produce an efficient and effective recommendation. This approach is advantageous in recommending previously unrated books to users with unique interests. It is found to be proven from the obtained results that the results of the cosine similarity of the full content of books, the results of the sequence matcher similarity of Arabic titles of the books, and the results of the semantic similarity of English titles of the books are the best obtained results, and extremely close to the average of the result related to the human assigned/annotated similarity. Flask web application is developed with a simple interface to show the recommended Arabic books by using cosine similarity, sequence matcher similarity, and semantic similarity algorithms with all experiments that are conducted.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.441-443
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• 2021

Sentence or text similarity is a measure of the degree of similarity between two sentences. Techniques for measuring text similarity include Jacquard similarity, cosine similarity, Euclidean similarity, and Manhattan similarity. Currently, the cosine similarity technique is most often used, but since this is an analysis according to the occurrence or frequency of a word in a sentence, the analysis on the semantic relationship is insufficient. Therefore, we try to improve the efficiency of analysis on the similarity of sentences by giving relations between words using ontology and including semantic similarity when extracting words that are commonly included in two sentences.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.111-118
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• 2022

This research article presents the work that is related to the application of different machine learning based similarity techniques on religious text for identifying similarities and differences among its various translations. The dataset includes 10 different English translations of verses (Arabic: Ayah) of two Surahs (chapters) namely, Al-Humazah and An-Nasr. The quantitative similarity values for different translations for the same verse were calculated by using the cosine similarity and semantic similarity. The corpus went through two series of experiments: before pre-processing and after pre-processing. In order to determine the performance of machine learning based similarities, human annotated similarities between translations of two Surahs (chapters) namely Al-Humazah and An-Nasr were recorded to construct the ground truth. The average difference between the human annotated similarity and the cosine similarity for Surah (chapter) Al-Humazah was found to be 1.38 per verse (ayah) per pair of translation. After pre-processing, the average difference increased to 2.24. Moreover, the average difference between human annotated similarity and semantic similarity for Surah (chapter) Al-Humazah was found to be 0.09 per verse (Ayah) per pair of translation. After pre-processing, it increased to 0.78. For the Surah (chapter) An-Nasr, before preprocessing, the average difference between human annotated similarity and cosine similarity was found to be 1.93 per verse (Ayah), per pair of translation. And. After pre-processing, the average difference further increased to 2.47. The average difference between the human annotated similarity and the semantic similarity for Surah An-Nasr before preprocessing was found to be 0.93 and after pre-processing, it was reduced to 0.87 per verse (ayah) per pair of translation. The results showed that as expected, the semantic similarity was proven to be better measurement indicator for calculation of the word meaning.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.270-274
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• 2001

Conventional similarity measures suggested so far can be classified into three categories: (i) geometric similarity measures, (ij) set-theoretic similarity measures, and (iii) matching function-based similarity measures. On the basis of the characteristics of the conventional similarity measures, in this paper, we propose a new similarity measure of fuzzy sets and investigate its properLies. Finally, numelical examples are provided for the comparison of characteristics of the proposed similarity measure and other previous similarity measures.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.100-104
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• 2006

Fuzzy techniques can be applied in many domains of computer vision community. The definition of an adequate similarity measure for measuring the similarity between fuzzy sets is of great importance in the field of image processing, image retrieval and pattern recognition. This paper proposes a new class of the similarity measures. The properties, sensitivity and effectiveness of the proposed measures are investigated and tested on real data. Experimental results show that these similarity measures can provide a useful way for measuring the similarity between fuzzy sets.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1151-1160
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• 2006

The similarity weight, the pearson's correlation coefficient, which is used in the recommender system has a weak point that it cannot predict all of the prediction value. The similarity weight, the vector similarity, has a weak point of the high MAE although the prediction coverage using the vector similarity is higher than that using the pearson's correlation coefficient. The purpose of this study is to suggest how to raise the prediction coverage. Also, the MAE using the suggested method in this study was compared both with the MAE using the pearson's correlation coefficient and with the MAE using the vector similarity, so was the prediction coverage. As a result, it was found that the low of the MAE in the case of using the suggested method was higher than that using the pearson's correlation coefficient. However, it was also shown that it was lower than that using the vector similarity. In terms of the prediction coverage, when the suggested method was compared with two similarity weights as I mentioned above, it was found that its prediction coverage was higher than that pearson's correlation coefficient as well as vector similarity.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.257-261
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• 2008

In this Paper we derived fuzzy entropy that is based on similarity measure. Similarity measure represents the degree of similarity between two informations, those informations characteristics are not important. First we construct similarity measure between two informations, and derived entropy functions with obtained similarity measure. Obtained entropy is verified with proof. With the help of one-to-one similarity is also obtained through distance measure, this similarity measure is also proved in our paper.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.205-208
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• 2007

Measuring the similarity between fuzzy sets plays a vital role in several fields. However, none of all well-known similarity measure methods is all-powerful, and all have the localization of its usage. This paper defines some operations on the similarity measures of fuzzy sets such as summation and multiplication of two similarity measures. Also, these operations will be generalized to any number of similarity measures. These operations will be very useful especially in the field of computer vision, and data retrieval because these fields need to combine and find some relations between similarity measures.