의미간의 유사도 연구의 패러다임 변화의 필요성-인지 의미론적 관점에서의 고찰

최영석(Youngseok Choi),박진수(Jinsoo Park) 한국지능정보시스템학회 2013 지능정보연구 Vol.19 No.1

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called ‘World Knowledge.’ World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human’s cultural knowledge may also change. Today’s society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the metho

Assessment of performance of machine learning based similarities calculated for different English translations of Holy Quran

Al Ghamdi, Norah Mohammad,Khan, Muhammad Badruddin International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4

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.

Semantic Conceptual Relational Similarity Based Web Document Clustering for Efficient Information Retrieval Using Semantic Ontology

( Selvalakshmi B ),( Subramaniam M ),( Sathiyasekar K ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.9

In the modern rapid growing web era, the scope of web publication is about accessing the web resources. Due to the increased size of web, the search engines face many challenges, in indexing the web pages as well as producing result to the user query. Methodologies discussed in literatures towards clustering web documents suffer in producing higher clustering accuracy. Problem is mitigated using, the proposed scheme, Semantic Conceptual Relational Similarity (SCRS) based clustering algorithm which, considers the relationship of any document in two ways, to measure the similarity. One is with the number of semantic relations of any document class covered by the input document and the second is the number of conceptual relation the input document covers towards any document class. With a given data set Ds, the method estimates the SCRS measure for each document Di towards available class of documents. As a result, a class with maximum SCRS is identified and the document is indexed on the selected class. The SCRS measure is measured according to the semantic relevancy of input document towards each document of any class. Similarly, the input query has been measured for Query Relational Semantic Score (QRSS) towards each class of documents. Based on the value of QRSS measure, the document class is identified, retrieved and ranked based on the QRSS measure to produce final population. In both the way, the semantic measures are estimated based on the concepts available in semantic ontology. The proposed method had risen efficient result in indexing as well as search efficiency also has been improved.

Development of the Recommender System of Arabic Books Based on the Content Similarity

Alotaibi, Shaykhah Hajed,Khan, Muhammad Badruddin International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.8

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.

KNN-based Image Annotation by Collectively Mining Visual and Semantic Similarities

( Qian Ji ),( Liyan Zhang ),( Zechao Li ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.9

The aim of image annotation is to determine labels that can accurately describe the semantic information of images. Many approaches have been proposed to automate the image annotation task while achieving good performance. However, in most cases, the semantic similarities of images are ignored. Towards this end, we propose a novel Visual-Semantic Nearest Neighbor (VS-KNN) method by collectively exploring visual and semantic similarities for image annotation. First, for each label, visual nearest neighbors of a given test image are constructed from training images associated with this label. Second, each neighboring subset is determined by mining the semantic similarity and the visual similarity. Finally, the relevance between the images and labels is determined based on maximum a posteriori estimation. Extensive experiments were conducted using three widely used image datasets. The experimental results show the effectiveness of the proposed method in comparison with state-of-the-arts methods.

A Multi-Agent Improved Semantic Similarity Matching Algorithm Based on Ontology Tree

Qian Gao,Young Im Cho(조영임) 제어로봇시스템학회 2012 제어·로봇·시스템학회 논문지 Vol.18 No.11

Semantic-based information retrieval techniques understand the meanings of the concepts that users specify in their queries, but the traditional semantic matching methods based on the ontology tree have three weaknesses which may lead to many false matches, causing the falling precision. In order to improve the matching precision and the recall of the information retrieval, this paper proposes a multi-agent improved semantic similarity matching algorithm based on the ontology tree, which can avoid the considerable computation redundancies and mismatching during the entire matching process. The results of the experiments performed on our algorithm show improvements in precision and recall compared with the information retrieval techniques based on the traditional semantic similarity matching methods.

사전학습 언어모델 기반 트랜스포머를 활용한 의미유사도기반 자연어이해 의도파악 방법

정상근,서혜인,김현지,황태욱 한국정보과학회 2020 정보과학회논문지 Vol.47 No.8

자연어이해는 로봇, 메신저, 자연어 인터페이스 등에 활용되는 근간 기술 중 하나이다. 본 연구에서는 자연어이해 문제 중 문장의 의도를 파악하는 의도파악기술에 있어, 전통적인 분류기술을 활용하는 것이 아닌, 문장의 의미를 벡터 형태로 가공할 수 있는 문장 및 의미틀 읽기장치를 학습시키고, 훈련문장과 질의문장의 벡터 공간상의 의미거리를 측정하여, 가장 가까운 훈련문장의 의도를 질의문장의 의도로 부착하는 방법을 제안한다. 이를 위해, 사전학습 언어모델 기반 트랜스포머를 활용하여 기호 형태의 문장 및 의미틀을 벡터 형태로 변환하는 방법을 소개한다. 한국어 기반 날씨 및 내비게이션 영역의 말뭉치와 영어 기반 항공교통 예약 영역, 음성 언어 이해 시스템 영역의 자연어 말뭉치등을 활용한 다양한 실험을 통하여 제안한 방법이 성공적으로 의미벡터를 배움을 보이고, 기존 의도파악 기술 대비 높은 성능을 가짐을 보인다. Natural language understanding (NLU) is a central technique applied to developing robot, smart messenger, and natural interface. In this study, we propose a novel similarity-based intent analysis method instead of the typical classification methods for intent analysis problems in the NLU. To accomplish this, the neural network-based text and semantic frame readers are introduced to learn semantic vectors using pairwise text-semantic frame instances. The text to vector and the semantic frame to vector projection methods using the pre-trained transformer are proposed. Then, we propose a method of attaching the intention tag of the nearest training sentence to the query sentence by measuring the semantic vector distances in the vector space. Four experiments on the natural language learning suggest that the proposed method demonstrates superior performance compared to the existing intention analysis techniques. These four experiments use natural language corpora in Korean and English. The two experiments in Korean are weather and navigation language corpora, and the two English-based experiments involve air travel information systems and voice platform language corpora.

Three-dimensional model retrieval in single category geometry using local ontology created by object part segmentation through deep neural network

손호준,이수홍 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.11

3D model retrieval is useful for reusing designs in manufacturing industry. Traditionally, 3D model retrieval has been implemented only in low-level information such as geometry, color, and texture. However high-level semantic information should be used for more accurate retrieval. In this study, a 3D geometry is divided into several parts using PointNet and then the local ontology is constructed by summarizing the characteristics of each part. Then part align similarity, lemma similarity, name similarity, part location similarity, and part size similarity are calculated. Using the values of these similarities, 3D models are retrieved from input query model. This comprehensive retrieval that includes all the similarities is more balanced and shows better performance in nameless models than considering only partial similarities. Through the method in this paper, high-level information and low-level information can be used simultaneously for 3D model retrieval.

Content-Based Image Retrieval Improved by Incorporating Semantic Annotation via Query Expansion

Guoqing Xu,Jian Li,Chunyu Xu,Qi Wang 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.12

Automatic image annotation (AIA) is expected to be a promising way to improve the performance of content-based image retrieval (CBIR). However, current image annotation results are always incomplete and noisy, and far from practical usage. In this paper, we incorporate semantic annotations into CBIR via query expansion scheme to improve retrieval accuracy. In the proposed method, semantic annotations of test images are obtained using a visual nearest-neighbor-based annotation model. And both visual features and annotation keywords are used to represent images. The similarity between two images is determined by their visual similarity and semantic similarity. The method is evaluated on the well-known Pascal VOC 2007 dataset using standard performance evaluation metric. The experimental results indicate that the performance of CBIR can be improved by incorporating semantic annotation via query expansion.

의미 변화의 양적 추정 - 말뭉치를 이용한 의미 변화 연구 -

이민우 한국어의미학회 2021 한국어 의미학 Vol.73 No.-

This study introduces methods that can objectively and scientifically capture and describe changes in meaning using corpus data through studies applying statistical methods that have been conducted recently. For statistical inference on the change in meaning, historically recorded data must be quantified and used through a specific calculation method. A representative method of numerically analyzing language data is to use the Semantic Vector Space(SVM), which is also commonly used in the language data analysis method that has been very active in recent years. Recently, a great progress has been made in capturing and explaining semantic changes using these methods. Quantitative estimation of semantic change using computational statistics is based on frequency-based, similarity-based, and network-based methods depending on the representation of meaning can be divided into. The difference between the use of meaning in the present and the use of meaning in the past can be confirmed by comparing the context similarity of the text. If there is a large change in usage compared to the present, the context similarity will be lower, and if there is no change, the context similarity will be high. In order to grasp a detailed and specific change in meaning, a method of comparing a list of key expressions or words that well reveals the meaning of the context can be used by calculating the similarity between expressions or words in the corpus by period. Meaning expansion and reduction, which are representative results of meaning change, can be grasped by measuring the change in the scope of use, and the rise and fall of meaning can be grasped through sentiment analysis. By statistically analyzing the historical corpus in this way, it is possible to statistically grasp the historical changes in the meaning of the Korean language, and suggest implications on how to identify and analyze the change in language meaning.