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Artykuły w czasopismach na temat „Ontology-based”

Autor: Grafiati

Data publikacji: 5 czerwca 2025

Data aktualizacji: 1 sierpnia 2025

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1

Abdullah, Noryusliza, and Rosziati Ibrahim. "Application-Based Ontology Engineering." International Journal of Future Computer and Communication 3, no. 3 (2014): 177–81. http://dx.doi.org/10.7763/ijfcc.2014.v3.291.

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Zhu, Linli, Yu Pan, Mohammad Reza Farahani, and Wei Gao. "Graph Laplacian Based Ontology Regularization Distance Framework for Ontology Similarity Measuring and Ontology Mapping." Journal of Computational Mathematica 1, no. 1 (2017): 88–98. http://dx.doi.org/10.26524/cm6.

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Ma, Yinglong, Shaohua Liu, Beihong Jin, and Gang Xu. "Inconsistent ontology revision based on ontology constructs." Expert Systems with Applications 37, no. 10 (2010): 7269–75. http://dx.doi.org/10.1016/j.eswa.2010.04.001.

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4

Schuurman, Nadine, and Agnieszka Leszczynski. "Ontology-Based Metadata." Transactions in GIS 10, no. 5 (2006): 709–26. http://dx.doi.org/10.1111/j.1467-9671.2006.01024.x.

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5

Mecca, Giansalvatore, Guillem Rull, Donatello Santoro, and Ernest Teniente. "Ontology-based mappings." Data & Knowledge Engineering 98 (July 2015): 8–29. http://dx.doi.org/10.1016/j.datak.2015.07.003.

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Djuric, Dragan. "MDA-based ontology infrastructure." Computer Science and Information Systems 1, no. 1 (2004): 91–116. http://dx.doi.org/10.2298/csis0401091d.

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The paper presents Ontology Definition Metamodel (ODM) and Ontology UML Profile that enables using Model Driven Architecture (MDA) standards in ontological engineering. Other similar metamodels are based on ontology representation languages, such as RDF(S), DAML+OIL, etc. However, none of these other solutions uses the recent W3C effort-The Web Ontology Language (OWL). In our approach, we firstly define the ODM and Ontology UML Profile place in the context of the MDA four-layer architecture and identify the main OWL concepts. Then, we define ODM using Meta-Object Facility (MOF). The relations

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7

Čerba, Otakar, Sarah Velten, Jan Macura, and Viktorie Sloupová. "Ontology-based Web Map." Abstracts of the ICA 5 (September 14, 2022): 1. http://dx.doi.org/10.5194/ica-abs-5-117-2022.

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Umar, Amjad, and Ramesh Subbaraman. "Ontology-Based Network Planning." International Journal of Business Data Communications and Networking 4, no. 3 (2008): 31–51. http://dx.doi.org/10.4018/jbdcn.2008070102.

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9

Batet, Montserrat. "Ontology-based semantic clustering." AI Communications 24, no. 3 (2011): 291–92. http://dx.doi.org/10.3233/aic-2011-0501.

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10

Carbonaro, Antonella. "Ontology-based video retrieval." International Journal of Digital Culture and Electronic Tourism 1, no. 4 (2009): 302. http://dx.doi.org/10.1504/ijdcet.2009.025357.

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11

Schreiber, A. T., B. Dubbeldam, J. Wielemaker, and B. Wielinga. "Ontology-based photo annotation." IEEE Intelligent Systems 16, no. 3 (2001): 66–74. http://dx.doi.org/10.1109/5254.940028.

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12

Raubal, Martin, and Werner Kuhn. "Ontology-Based Task Simulation." Spatial Cognition & Computation 4, no. 1 (2004): 15–37. http://dx.doi.org/10.1207/s15427633scc0401_3.

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13

Tempich, Christoph, Elena Simperl, Markus Luczak, Rudi Studer, and H. Sofia Pinto. "Argumentation-Based Ontology Engineering." IEEE Intelligent Systems 22, no. 6 (2007): 52–59. http://dx.doi.org/10.1109/mis.2007.103.

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14

Bienvenu, Meghyn, Balder Ten Cate, Carsten Lutz, and Frank Wolter. "Ontology-Based Data Access." ACM Transactions on Database Systems 39, no. 4 (2014): 1–44. http://dx.doi.org/10.1145/2661643.

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15

Fürst, Frédéric, and Francky Trichet. "Axiom-based ontology matching." Expert Systems 26, no. 2 (2009): 218–46. http://dx.doi.org/10.1111/j.1468-0394.2009.00482.x.

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16

Fensel, D. "Ontology-based knowledge management." Computer 35, no. 11 (2002): 56–59. http://dx.doi.org/10.1109/mc.2002.1046975.

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17

Kokar, Mieczyslaw M., Christopher J. Matheus, and Kenneth Baclawski. "Ontology-based situation awareness." Information Fusion 10, no. 1 (2009): 83–98. http://dx.doi.org/10.1016/j.inffus.2007.01.004.

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18

Amin, Muhammad Bilal, Wajahat Ali Khan, Sungyoung Lee, and Byeong Ho Kang. "Performance-based ontology matching." Applied Intelligence 43, no. 2 (2015): 356–85. http://dx.doi.org/10.1007/s10489-015-0648-z.

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19

Kantola, Jussi I. "Ontology-based resource management." Human Factors and Ergonomics in Manufacturing 19, no. 6 (2009): 515–27. http://dx.doi.org/10.1002/hfm.20181.

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20

CAN, Özgü, and Murat ÜNALIR. "Ontology Based Access Control: A Case Study through Ontology Based Data Access." Deu Muhendislik Fakultesi Fen ve Muhendislik 25, no. 74 (2023): 417–32. http://dx.doi.org/10.21205/deufmd.2023257413.

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Ontology Based Data Access (OBDA) is the provision of data access and data integration as a result of the mapping that is established between an ontology and a data source. Thus, storing large amounts of data becomes easier, more powerful queries can be written, and management of complex information systems can be performed quickly and effectively by using Semantic Web technologies. Ontology Based Access Control (OBAC) uses Semantic Web technologies to enable the enforcement of access control mechanism. Therefore, only authorized persons can access data to protect data privacy. In this study,

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21

Ma, Yinglong, Beihong Jin, and Yulin Feng. "Semantic oriented ontology cohesion metrics for ontology-based systems." Journal of Systems and Software 83, no. 1 (2010): 143–52. http://dx.doi.org/10.1016/j.jss.2009.07.047.

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22

Iyabo Omotosho, Oluyinka. "Ontology Based Learners Style Compliant E-Learning System." International Journal of Science and Research (IJSR) 11, no. 4 (2022): 1363–67. http://dx.doi.org/10.21275/sr23121103224.

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23

Kardan, Ahmad A., and Hamed Jafarpour. "N-Dimensional Matrix-Based Ontology." International Journal on Semantic Web and Information Systems 14, no. 2 (2018): 47–69. http://dx.doi.org/10.4018/ijswis.2018040103.

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This article describes how the assessment of semantic similarities between word pairs is an important component of understanding text which enables processing, classifying and structuring of textual resources. For this purpose, an ontology is a powerful technique when applied to compute similarity. In this article, the authors propose a novel model to represent an ontology in which an N-dimensional matrix is applied, called an N-dimensional matrix-based ontology. This matrix-based ontology attempts to decrease the time complexity of computation. Second, a new semantic similarity measure is int

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24

Wei, Moji, Jianliang Xu, Hongyan Yun, and Linlin Xu. "Ontology-based home service model." Computer Science and Information Systems 9, no. 2 (2012): 813–38. http://dx.doi.org/10.2298/csis101222001w.

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This paper researches Home Service retrieval and invocation for smart home. We represent ontology-based Home Service Model to retrieve and invoke services according to user?s needs automatically. Firstly by analyzing the context of home service, we differentiate key concepts in the field and analyze the relations among them, and as a result, an upper ontology as a fixed viewpoint for further more detailed conceptualization is achieved. Then by reifying the concepts of the upper ontology, we construct two domain ontologies which are Function Concept Ontology and Context Concept Ontology to anno

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25

Shi, Xin. "Construction of Design Process Ontology Database Based on Process Ontology." Applied Mechanics and Materials 263-266 (December 2012): 3352–56. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.3352.

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Based on product design process knowledge reuse, according to the characteristics of ontology knowledge representation, process ontology knowledge representation method is offered. The paper extracts the category of design process, defines and describes the attribute and relationship of these categories. Then, the design process ontology database based on process ontology is constructed. As a result, it supports to achieve the sharing and reusing of design process knowledge.

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26

Li, Shun Xin, and Lei Jun Shi. "Ontology Mapping Based on Domain Framework." Applied Mechanics and Materials 65 (June 2011): 21–24. http://dx.doi.org/10.4028/www.scientific.net/amm.65.21.

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For service-oriented software engineering, the basic problem faced is the sharing and interaction of information between software, and in essence, is to make the software to communicate from the semantic level. Ontology is as a carrier of information and expression methods and is used to describe software entities. We may want to take into account the reuse of ontology modules, meanwhile different ontology libraries may face ontology heterogeneity and other problems, and all the above problems can be resolved through ontology mapping. Based on the process of ontology construction, the ways of

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27

J, Naren, Raja Rajeswari D, Nikhith Sannidhi, and Vithya G. "An Investigation on Ontology Based Fuzzy Semantic Information Retrieval." International Journal of Psychosocial Rehabilitation 23, no. 1 (2019): 377–84. http://dx.doi.org/10.37200/ijpr/v23i1/pr190248.

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28

Subbiyan, Reka, and Sowmyadevi Kanagasabapathy. "An Ontology based Anatomy Approach to Temporal Topic Summarization." International Journal of Scientific Research 2, no. 1 (2012): 42–43. http://dx.doi.org/10.15373/22778179/jan2013/14.

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29

Park, Hasil, Jinho Park, and Joonki Paik. "Ontology-Based Semantic Gait Interpretation." TECHART: Journal of Arts and Imaging Science 2, no. 2 (2015): 57. http://dx.doi.org/10.15323/techart.2015.05.2.2.57.

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30

Moroz, Oleg V. "Universal Transcendental Logic-Based Ontology." Computer and Information Science 14, no. 2 (2021): 1. http://dx.doi.org/10.5539/cis.v14n2p1.

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In this paper, Kant&#39;s philosophical doctrine of the categories of the reason is used to substantiate the conceptual model of knowledge representation, based on the collective interaction of a lot of intellectual atomic elements of knowledge (knowledge quanta), which are combined into clusters like neurons in the brain; and also a phenomenological description of the corresponding universal ontology, proceeding from the philosophical premise of Husserl-Heidegger that the meaning of intelligence is not so much in knowing the absolute truth as in survival, is presented. In the process of c

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31

Xu, Cijun, Aiping Li, and Xuemei Liu. "Ontology-Based Knowledge Fusion Framework." Journal of Computer-Aided Design & Computer Graphics 22, no. 7 (2010): 1230–36. http://dx.doi.org/10.3724/sp.j.1089.2010.10897.

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32

Ahmed, Soraya Setti, Mimoun Malki, and Sidi Mohamed Benslimane. "Ontology Partitioning: Clustering Based Approach." International Journal of Information Technology and Computer Science 7, no. 6 (2015): 1–11. http://dx.doi.org/10.5815/ijitcs.2015.06.01.

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33

Thị Anh Lê, Phạm, and Nguyễn Phước Cường. "Ontology based semantic information search." Journal of Science, Educational Science 60, no. 7A (2015): 21–29. http://dx.doi.org/10.18173/2354-1075.2015-0049.

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34

Ali, Abdullah, Siti Mariyam Shamsuddin, and Fathy E. Eassa. "Ontology-based Cloud Services Representation." Research Journal of Applied Sciences, Engineering and Technology 8, no. 1 (2014): 83–94. http://dx.doi.org/10.19026/rjaset.8.944.

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35

Liu Tingting, Liu Guangli, and Liu Tong. "Ontology-based Grain Emergency System." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 5 (2011): 27–35. http://dx.doi.org/10.4156/aiss.vol3.issue5.4.

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36

Eine, Bastian, Matthias Jurisch, and Werner Quint. "Ontology-Based Big Data Management." Systems 5, no. 3 (2017): 45. http://dx.doi.org/10.3390/systems5030045.

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37

Kumar, Rajiv, and M. D Tiwari. "Ontology based KM: A Review." International Journal of Computer Applications 90, no. 4 (2014): 8–13. http://dx.doi.org/10.5120/15560-4109.

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38

Schorlemmer, Marco, and Yannis Kalfoglou. "Institutionalising ontology-based semantic integration." Applied Ontology 3, no. 3 (2008): 131–50. http://dx.doi.org/10.3233/ao-2008-0041.

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39

Lee, In-K., Suk-T. Seo, Hye-C. Jeong, Seo-H. Son, and Soon-H. Kwon. "Ontology-based Image Understanding Systems." Journal of Korean Institute of Intelligent Systems 17, no. 3 (2007): 328–35. http://dx.doi.org/10.5391/jkiis.2007.17.3.328.

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40

Lee, In-K., Do-Sam Hwang, Suk-T. Seo, and Soon-H. Kwon. "Ontology Integration based on MetaOntology." Journal of Korean Institute of Intelligent Systems 17, no. 5 (2007): 604–13. http://dx.doi.org/10.5391/jkiis.2007.17.5.604.

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41

., Anbarasi M. S. "ONTOLOGY ORIENTED CONCEPT BASED CLUSTERING." International Journal of Research in Engineering and Technology 03, no. 02 (2014): 600–604. http://dx.doi.org/10.15623/ijret.2014.0302106.

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42

El-Halees, Alaa, and Ahmed Al-Asmar. "Ontology Based Arabic Opinion Mining." Journal of Information & Knowledge Management 16, no. 03 (2017): 1750028. http://dx.doi.org/10.1142/s0219649217500289.

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In Arabic language, studies in the area of opinion mining are still limited compared to that being carried out in other languages. In this paper, we highlight the problem for Arabic opinion mining techniques when analysing reviews having different features with different opinion strengths. The traditional works of opinion mining consider all features extracted from the reviews to be equally important, so they fail to determine the correct opinion of the review and make the review's sentiment classification less accurate. This research presents a technique based on an ontology that uses feature

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43

Mckensy-Sambola, Dexon, Miguel Ángel Rodríguez-García, Francisco García-Sánchez, and Rafael Valencia-García. "Ontology-Based Nutritional Recommender System." Applied Sciences 12, no. 1 (2021): 143. http://dx.doi.org/10.3390/app12010143.

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Obesity is considered an epidemic that is continuously growing around the world. Heart diseases, diabetes, and bone and joint diseases are some of the diseases that people who are overweight or obese can develop. One of the vital causes of those disorders is poor nutrition education; there is no raising awareness about eating healthy food and practicing physical activities to burn off the excess energy. Therefore, it is necessary to use new technologies to build methods/tools that help people to overcome these avoidable nutrition disorders. For this reason, we implemented a recommendation engi

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44

Krishnamurthy, Saruladha, Arthi Janardanan, and B. Akoramurthy. "Rough Set Based Ontology Matching." International Journal of Rough Sets and Data Analysis 5, no. 2 (2018): 46–68. http://dx.doi.org/10.4018/ijrsda.2018040103.

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Ontologies enriches the knowledge and add meaning to the data residing in semantic web. Ontology matching identifies concepts for matching in source ontologies to the target ontologies to eliminate heterogeneities. Despite using similarity measures for identifying similar concepts, the ontology matching systems fails to handle uncertainty. This paper proposes a rough set based ontology matching system to handle uncertainty which aims (i)to optimize concepts considered for matching by using concept type classification(ii) to use rough set concepts using indiscernibility relations and reducts (i

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45

Sendhilkumar, S., G. S. Mahalakshmi, and S. N. Rajasekar. "Ontology-based automatic query refinement." International Journal of Artificial Intelligence and Soft Computing 1, no. 2/3/4 (2009): 316. http://dx.doi.org/10.1504/ijaisc.2009.027298.

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tyagi, Geetanjali. "Ontology Based Fuzzy Query Execution." American Journal of Networks and Communications 4, no. 3 (2015): 16. http://dx.doi.org/10.11648/j.ajnc.s.2015040301.14.

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Kogalovsky, M. R. "Ontology-based data access systems." Programming and Computer Software 38, no. 4 (2012): 167–82. http://dx.doi.org/10.1134/s0361768812040032.

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48

Huang, Ning, and ShiHan Diao. "Ontology-based enterprise knowledge integration." Robotics and Computer-Integrated Manufacturing 24, no. 4 (2008): 562–71. http://dx.doi.org/10.1016/j.rcim.2007.07.007.

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49

Shen, Li ping, and Rui min Shen. "Ontology-based learning content recommendation." International Journal of Continuing Engineering Education and Life-Long Learning 15, no. 3/4/5/6 (2005): 308. http://dx.doi.org/10.1504/ijceell.2005.007719.

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50

Gayathri, M., and R. Jagadeesh Kannan. "Ontology Based Indian Medical System." Materials Today: Proceedings 5, no. 1 (2018): 1974–79. http://dx.doi.org/10.1016/j.matpr.2017.11.301.

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