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Journal articles on the topic 'Ontology alignment evaluation initiative'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Dragisic, Zlatan, Valentina Ivanova, Huanyu Li, and Patrick Lambrix. "Experiences from the anatomy track in the ontology alignment evaluation initiative." Journal of Biomedical Semantics 8, no. 1 (2017): 56. https://doi.org/10.1186/s13326-017-0166-5.

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<strong>Background: </strong>One of the longest running tracks in the Ontology Alignment Evaluation Initiative is the Anatomy track which focuses on aligning two anatomy ontologies. The Anatomy track was started in 2005. In 2005 and 2006 the task in this track was to align the Foundational Model of Anatomy and the OpenGalen Anatomy Model. Since 2007 the ontologies used in the track are the Adult Mouse Anatomy and a part of the NCI Thesaurus. Since 2015 the data in the Anatomy track is also used in the Interactive track of the Ontology Alignment Evaluation Initiative.<strong>Results: </strong>I

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Vargas-Vera, Maria, and Miklos Nagy. "Experiences on the Evaluation of DSSim." International Journal of Knowledge Society Research 6, no. 2 (2015): 20–50. http://dx.doi.org/10.4018/ijksr.2015040102.

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This paper presents a comprehensive evaluation of DSSim (DSSim stands for Similarity based on Dempster-Shafer), our ontology alignment system. The authors participated several years in the annual evaluation defined by the Ontology Alignment Initiative (OAEI). Each year their DSSim was evolved and participated in more difficult tracks defined by the Ontology Alignment Initiative. In fact, DSSim obtained exceptional results in the OAEI-2008 Evaluation. In this evaluation (OAEI-2008), DSSim participated on all given tracks namely, benchmark, anatomy, fao, directory, mldirectory, library, very lar

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Harrow, Ian, Ernesto Jiménez-Ruiz, Andrea Splendiani, et al. "Matching disease and phenotype ontologies in the ontology alignment evaluation initiative." Journal of Biomedical Semantics 8, no. 1 (2017): 55. https://doi.org/10.1186/s13326-017-0162-9.

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<strong>Background: </strong>The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease.<strong>Results: </strong>Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the <i>Disease and Phenotype</i> track. AML, FCA-Map, LogM

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Antunes, Cauã Roca, Alexandre Rademaker, and Mara Abel. "A faster and less aggressive algorithm for correcting conservativity violations in ontology alignments." Applied Ontology 16, no. 3 (2021): 277–96. http://dx.doi.org/10.3233/ao-210243.

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Ontologies are computational artifacts that model consensual aspects of reality. In distributed contexts, applications often need to utilize information from several distinct ontologies. In order to integrate multiple ontologies, entities modeled in each ontology must be matched through an ontology alignment. However, imperfect alignments may introduce inconsistencies. One kind of inconsistency, which is often introduced, is the violation of the conservativity principle, that states that the alignment should not introduce new subsumption relations between entities from the same source ontology

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Zhou, Lu, Michelle Cheatham, Adila Krisnadhi, and Pascal Hitzler. "GeoLink Data Set: A Complex Alignment Benchmark from Real-world Ontology." Data Intelligence 2, no. 3 (2020): 353–78. http://dx.doi.org/10.1162/dint_a_00054.

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Ontology alignment has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships. In this paper, we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark. The data set consists of two ontologies, th

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Huang, Yikun, Xingsi Xue, and Chao Jiang. "Semantic Integration of Sensor Knowledge on Artificial Internet of Things." Wireless Communications and Mobile Computing 2020 (July 25, 2020): 1–8. http://dx.doi.org/10.1155/2020/8815001.

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Artificial Internet of Things (AIoT) integrates Artificial Intelligence (AI) with the Internet of Things (IoT) to create the sensor network that can communicate and process data. To implement the communications and co-operations among intelligent systems on AIoT, it is necessary to annotate sensor data with the semantic meanings to overcome heterogeneity problem among different sensors, which requires the utilization of sensor ontology. Sensor ontology formally models the knowledge on AIoT by defining the concepts, the properties describing a concept, and the relationships between two concepts

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Xue, Xingsi, Xiaojing Wu, Chao Jiang, Guojun Mao, and Hai Zhu. "Integrating Sensor Ontologies with Global and Local Alignment Extractions." Wireless Communications and Mobile Computing 2021 (February 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/6625184.

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In order to enhance the communication between sensor networks in the Internet of things (IoT), it is indispensable to establish the semantic connections between sensor ontologies in this field. For this purpose, this paper proposes an up-and-coming sensor ontology integrating technique, which uses debate mechanism (DM) to extract the sensor ontology alignment from various alignments determined by different matchers. In particular, we use the correctness factor of each matcher to determine a correspondence’s global factor, and utilize the support strength and disprove strength in the debating p

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Wu, Zhen Le, Ying Li, Yong Bin Wang, and Yan Jiao Zang. "Continual Word Embedding Based for Matching Lightweight Ontologies." Applied Mechanics and Materials 556-562 (May 2014): 6281–85. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6281.

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Ontology matching is the task of finding alignments between two different ontologies. It has become the key point of building knowledge base and integrating heterogeneous data. In this paper, a novel ontology matching approach that is based on continual word embedding is proposed. We describe in details how is skip-gram model adapted to capture the semantic of words to learn the word embedding. After computing the name similarity of concepts, similarity flooding algorithm is used to fix the initial similarity. Experiments on Ontology Alignment Evaluation Initiative (OAEI) benchmark without ins

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Souza, Jairo Francisco de, Sean Wolfgand Matsui Siqueira, and Bernardo Nunes. "A framework to aggregate multiple ontology matchers." International Journal of Web Information Systems 16, no. 2 (2019): 151–69. http://dx.doi.org/10.1108/ijwis-05-2019-0023.

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Purpose Although ontology matchers are annually proposed to address different aspects of the semantic heterogeneity problem, finding the most suitable alignment approach is still an issue. This study aims to propose a computational solution for ontology meta-matching (OMM) and a framework designed for developers to make use of alignment techniques in their applications. Design/methodology/approach The framework includes some similarity functions that can be chosen by developers and then, automatically, set weights for each function to obtain better alignments. To evaluate the framework, severa

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Zhu, Hai, Xingsi Xue, Chengcai Jiang, and He Ren. "Multiobjective Sensor Ontology Matching Technique with User Preference Metrics." Wireless Communications and Mobile Computing 2021 (March 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/5594553.

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Due to the problem of data heterogeneity in the semantic sensor networks, the communications among different sensor network applications are seriously hampered. Although sensor ontology is regarded as the state-of-the-art knowledge model for exchanging sensor information, there also exists the heterogeneity problem between different sensor ontologies. Ontology matching is an effective method to deal with the sensor ontology heterogeneity problem, whose kernel technique is the similarity measure. How to integrate different similarity measures to determine the alignment of high quality for the u

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Lian, Wenwu, Lingling Fu, Xishuan Niu, Junhong Feng, and Jian-Hong Wang. "Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm." Wireless Communications and Mobile Computing 2022 (March 14, 2022): 1–7. http://dx.doi.org/10.1155/2022/9662517.

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To implement co-operation among applications on the Internet of Things (IoT), we need to describe the meaning of diverse sensor data with the sensor ontology. However, there exists a heterogeneity issue among different sensor ontologies, which hampers their communications. Sensor ontology matching is a feasible solution to this problem, which is able to map the identical ontology entity pairs. This work investigates the sensor ontology meta-matching problem, which indirectly optimizes the sensor ontology alignment’s quality by tuning the weights to aggregate different ontology matchers. Due to

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Biniz, Mohamed, and Rachid El Ayachi. "Optimizing Ontology Alignments by Using Neural NSGA-II." Journal of Electronic Commerce in Organizations 16, no. 1 (2018): 29–42. http://dx.doi.org/10.4018/jeco.2018010103.

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In this article, the authors propose a new hybrid approach based on a continuous Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a neural network to refine the alignment results. This approach consists of three phases: (i) pre-alignment phase which allows to identify the formats of input ontologies, to adapt them and to transform them into Ontology Web Language (OWL) in order to solve the problem of heterogeneity of representation. (ii) alignment phase which combines syntactic and linguistic matching techniques and methods, based on the relevant attributes per different points of synt

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Huang, Yikun, Xingsi Xue, and Chao Jiang. "Optimizing Ontology Alignment through Improved NSGA-II." Discrete Dynamics in Nature and Society 2020 (June 19, 2020): 1–8. http://dx.doi.org/10.1155/2020/8586058.

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Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker (DM) without any user preferences. This work investigates the ontology matching problem, which is a challenge in the semantic web (SW) domain. Due to the complex heterogeneity between two different ontologies, it is arduous to get an excellent alignment that meets all DMs’ demands. To this end, a popular MOEA, i.e., nondominated sorting geneti

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Xue, Xingsi, Chaofan Yang, Chao Jiang, Pei-Wei Tsai, Guojun Mao, and Hai Zhu. "Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences." Complexity 2021 (February 5, 2021): 1–12. http://dx.doi.org/10.1155/2021/5574732.

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Data heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matching is able to identify the semantic correspondences of entities in different ontologies, which is an effective method to address the ontology heterogeneity problem. Due to huge memory consumption and long runtime, the performance of the existing ontology matching techniques requires further improveme

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Kiren, Tayybah, and Muhammad Shoaib. "A novel ontology matching approach using key concepts." Aslib Journal of Information Management 68, no. 1 (2015): 99–111. http://dx.doi.org/10.1108/ajim-04-2015-0054.

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Purpose – Ontologies are used to formally describe the concepts within a domain in a machine-understandable way. Matching of heterogeneous ontologies is often essential for many applications like semantic annotation, query answering or ontology integration. Some ontologies may include a large number of entities which make the ontology matching process very complex in terms of the search space and execution time requirements. The purpose of this paper is to present a technique for finding degree of similarity between ontologies that trims down the search space by eliminating the ontology concep

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Xue, Xingsi, and Miao Ye. "Interactive complex ontology matching with local and global similarity deviations." Electronic Research Archive 31, no. 9 (2023): 5732–48. http://dx.doi.org/10.3934/era.2023291.

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<abstract><p>Ontology serves as a central technique in the semantic web to elucidate domain knowledge. The challenge of dealing with the heterogeneity introduced by diverse domain ontologies necessitates ontology matching, a process designed to identify semantically interconnected entities within these ontologies. This task is inherently complex due to the broad, diverse entities and the rich semantics inherent in vocabularies. To tackle this challenge, we bring forth a new interactive ontology matching method with local and global similarity deviations (IOM-LGSD) for ontology matc

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Xiao, Lei, Junhong Feng, Xishuan Niu, and Jian-Hong Wang. "Using Competitive Binary Particle Swarm Optimization Algorithm for Matching Sensor Ontologies." Mobile Information Systems 2022 (February 9, 2022): 1–7. http://dx.doi.org/10.1155/2022/2207252.

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Developing sensor ontologies and using them to annotate the sensor data is a feasible way to address the data heterogeneity issue on Internet of Things (IoT). However, the heterogeneity issue exists between different sensor ontologies hampers their communications. Sensor ontology matching aims at finding all the heterogeneous entities in two ontologies, which is a feasible solution for aggregating heterogeneous sensor ontologies. This work investigates swarm intelligence (SI)-based sensor ontology matching techniques and further proposes a competitive binary particle swarm optimization algorit

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Xue, Xingsi, Qi Wu, Miao Ye, and Jianhui Lv. "Efficient Ontology Meta-Matching Based on Interpolation Model Assisted Evolutionary Algorithm." Mathematics 10, no. 17 (2022): 3212. http://dx.doi.org/10.3390/math10173212.

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Ontology is the kernel technique of the Semantic Web (SW), which models the domain knowledge in a formal and machine-understandable way. To ensure different ontologies’ communications, the cutting-edge technology is to determine the heterogeneous entity mappings through the ontology matching process. During this procedure, it is of utmost importance to integrate different similarity measures to distinguish heterogeneous entity correspondence. The way to find the most appropriate aggregating weights to enhance the ontology alignment’s quality is called ontology meta-matching problem, and recent

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Xue, Xingsi, Haolin Wang, and Wenyu Liu. "Matching sensor ontologies with unsupervised neural network with competitive learning." PeerJ Computer Science 7 (November 19, 2021): e763. http://dx.doi.org/10.7717/peerj-cs.763.

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Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural

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Xue, Xingsi, Jiawei Lu, Chengcai Jiang, and Yikun Huang. "Sensor Ontology Metamatching with Heterogeneity Measures." Wireless Communications and Mobile Computing 2020 (November 25, 2020): 1–10. http://dx.doi.org/10.1155/2020/6666228.

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The heterogeneity problem among different sensor ontologies hinders the interaction of information. Ontology matching is an effective method to address this problem by determining the heterogeneous concept pairs. In the matching process, the similarity measure serves as the kernel technique, which calculates the similarity value of two concepts. Since none of the similarity measures can ensure its effectiveness in any context, usually, several measures are combined together to enhance the result’s confidence. How to find suitable aggregating weights for various similarity measures, i.e., ontol

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Xue, Xingsi, Xiaojing Wu, and Junfeng Chen. "Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution." Discrete Dynamics in Nature and Society 2020 (May 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/4716286.

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Nowadays, most real-world decision problems consist of two or more incommensurable or conflicting objectives to be optimized simultaneously, so-called multiobjective optimization problems (MOPs). Usually, a decision maker (DM) prefers only a single optimum solution in the Pareto front (PF), and the PF’s knee solution is logically the one if there are no user-specific or problem-specific preferences. In this context, the biomedical ontology matching problem in the Semantic Web (SW) domain is investigated, which can be of help to integrate the biomedical knowledge and facilitate the translationa

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Xue, Xingsi, Pei-Wei Tsai, and Yucheng Zhuang. "Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm." Biology 10, no. 12 (2021): 1287. http://dx.doi.org/10.3390/biology10121287.

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To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment’s quality, which calculate the alignment’s statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the b

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Guimarães, João, Morgana Andrade, and Ana Baptista. "Domain Vocabulary Alignment using AML and LogMap." RDBCI: Revista Digital de Biblioteconomia e Ciência da Informação 20, no. 2022 (2022): 1–25. http://dx.doi.org/10.20396/rdbci.v20i00.8668437/29673.

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Introduction: In the context of the Semantic Web, interoperability among heterogeneous ontologies is a challenge due to several factors, among which semantic ambiguity and redundancy stand out. To overcome these challenges, systems and algorithms are adopted to align different ontologies. In this study, it is understood that controlled vocabularies are a particular form of ontology. Objective: to obtain a vocabulary resulting from the alignment and fusion of the Vocabularies Scientific Domains and Scientific Areas of the Foundation for Science and Technology, -FCT, European Science Vocabulary

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Qiang, Zhangcheng, Weiqing Wang, and Kerry Taylor. "Agent-OM: Leveraging LLM Agents for Ontology Matching." Proceedings of the VLDB Endowment 18, no. 3 (2024): 516–29. https://doi.org/10.14778/3712221.3712222.

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Ontology matching (OM) enables semantic interoperability between different ontologies and resolves their conceptual heterogeneity by aligning related entities. OM systems currently have two prevailing design paradigms: conventional knowledge-based expert systems and newer machine learning-based predictive systems. While large language models (LLMs) and LLM agents have revolutionised data engineering and have been applied creatively in many domains, their potential for OM remains underexplored. This study introduces a novel agent-powered LLM-based design paradigm for OM systems. With considerat

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Zhuang, Yucheng, Yikun Huang, and Wenyu Liu. "Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm." Sensors 23, no. 11 (2023): 5069. http://dx.doi.org/10.3390/s23115069.

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Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor devices is hindered. Sensor ontology matching achieves data integration and sharing between sensors by establishing semantic relationships between sensor devices. Therefore, a niching multi-objective particle swarm optimization algorithm (NMOPSO) is proposed to effectively solve the sensor ontology matching problem. As the sensor ontology meta

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Hnatkowska, Bogumila, Adrianna Kozierkiewicz, and Marcin Pietranik. "Formal transformation of OWL ontology to a FOKI generic meta-model." Computer Science and Information Systems, no. 00 (2025): 2. https://doi.org/10.2298/csis240227002h.

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Ontology integration is merging a set of ontologies to provide a single, unified ontology, which contains all of the knowledge from input ontologies. Most solutions described in the literature are based on the OWL format and in corporate its strengths and weaknesses. In our previous research, we developed the ontology integration framework FOKI, which does not use the OWL. Collected experimental data using prepared ontologies proved its usefulness. However, the lack of OWL support makes it challenging to use the FOKI framework in practical applications. This paper presents a meta-model and a s

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FREDDO, ADEMIR ROBERTO, and CESAR AUGUSTO TACLA. "POAM: PARTIAL ALIGNMENT OF ONTOLOGIES IN DIALOG OF AGENTS BASED ON CONCEPT SIMILARITY." International Journal of Semantic Computing 04, no. 03 (2010): 357–84. http://dx.doi.org/10.1142/s1793351x10001048.

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This paper describes a method to partially align ontologies in dialogs of agents which use different ontologies. The method aims at aligning in execution time only the concepts necessary to the agents fulfill the current dialog. Thus, reducing the number of concepts to be searched in the target ontology is a very important requirement for agents' mutual understanding. The proposed method (named POAM, acronym for Partial Ontology Alignment Method) uses syntactical and linguistic techniques to group concepts together. The underlying rationale of POAM is that a person perceives an object and imme

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Huang, Yikun, Yucheng Zhuang, and Xingsi Xue. "Solving Ontology Metamatching Problem through Improved Multiobjective Particle Swarm Optimization Algorithm." Wireless Communications and Mobile Computing 2022 (November 22, 2022): 1–15. http://dx.doi.org/10.1155/2022/1634432.

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In recent years, knowledge representation in the Artificial Intelligence (AI) domain is able to help people understand the semantics of data and improve the interoperability between diverse knowledge-based applications. Semantic Web (SW), as one of the methods of knowledge representation, is the new generation of World Wide Web (WWW), which integrates AI with web techniques and dedicates to implementing the automatic cooperations among different intelligent applications. Ontology, as an information exchange model that defines concepts and formally describes the relationships between two concep

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Rudwan, Mohammed Suleiman Mohammed, and Jean Vincent Fonou-Dombeu. "Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment." Future Internet 15, no. 7 (2023): 229. http://dx.doi.org/10.3390/fi15070229.

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Ontology alignment has become an important process for identifying similarities and differences between ontologies, to facilitate their integration and reuse. To this end, fuzzy string-matching algorithms have been developed for strings similarity detection and have been used in ontology alignment. However, a significant limitation of existing fuzzy string-matching algorithms is their reliance on lexical/syntactic contents of ontology only, which do not capture semantic features of ontologies. To address this limitation, this paper proposed a novel method that hybridizes fuzzy string-matching

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Xue, Xingsi, Xiaojing Wu, Jie Zhang, Lingyu Zhang, Hai Zhu, and Guojun Mao. "Aggregating Heterogeneous Sensor Ontologies with Fuzzy Debate Mechanism." Security and Communication Networks 2021 (May 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/2878684.

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Aiming at enhancing the communication and information security between the next generation of Industrial Internet of Things (Nx-IIoT) sensor networks, it is critical to aggregate heterogeneous sensor data in the sensor ontologies by establishing semantic connections in diverse sensor ontologies. Sensor ontology matching technology is devoted to determining heterogeneous sensor concept pairs in two distinct sensor ontologies, which is an effective method of addressing the heterogeneity problem. The existing matching techniques neglect the relationships among different entity mapping, which make

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Wang, Peng, Shiyi Zou, Jiajun Liu, and Wenjun Ke. "Matching biomedical ontologies with GCN-based feature propagation." Mathematical Biosciences and Engineering 19, no. 8 (2022): 8479–504. http://dx.doi.org/10.3934/mbe.2022394.

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<abstract> <p>With an increasing number of biomedical ontologies being evolved independently, matching these ontologies to solve the interoperability problem has become a critical issue in biomedical applications. Traditional biomedical ontology matching methods are mostly based on rules or similarities for concepts and properties. These approaches require manually designed rules that not only fail to address the heterogeneity of domain ontology terminology and the ambiguity of multiple meanings of words, but also make it difficult to capture structural information in ontologies th

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Al-Yadumi, Sohaib, Wei-Wei Goh, Ee-Xion Tan, Noor Zaman Jhanjhi, and Patrice Boursier. "Multimatcher Model to Enhance Ontology Matching Using Background Knowledge." Information 12, no. 11 (2021): 487. http://dx.doi.org/10.3390/info12110487.

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Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can o

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Xue, Xingsi, Jianhua Guo, Miao Ye, and Jianhui Lv. "Similarity Feature Construction for Matching Ontologies through Adaptively Aggregating Artificial Neural Networks." Mathematics 11, no. 2 (2023): 485. http://dx.doi.org/10.3390/math11020485.

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Ontology is the kernel technique of Semantic Web (SW), which enables the interaction and cooperation among different intelligent applications. However, with the rapid development of ontologies, their heterogeneity issue becomes more and more serious, which hampers communications among those intelligent systems built upon them. Finding the heterogeneous entities between two ontologies, i.e., ontology matching, is an effective method of solving ontology heterogeneity problems. When matching two ontologies, it is critical to construct the entity pair’s similarity feature by comprehensively taking

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Xue, Xingsi, Jie Chen, Junfeng Chen, and Dongxu Chen. "Using Compact Coevolutionary Algorithm for Matching Biomedical Ontologies." Computational Intelligence and Neuroscience 2018 (October 8, 2018): 1–8. http://dx.doi.org/10.1155/2018/2309587.

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Over the recent years, ontologies are widely used in various domains such as medical records annotation, medical knowledge representation and sharing, clinical guideline management, and medical decision-making. To implement the cooperation between intelligent applications based on biomedical ontologies, it is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Although Evolutionary algorithms (EAs) are one of the state-of-the-art methodologies to match the heterogeneous ontologies, huge mem

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Vargas-Vera, Maria, and Miklos Nagy. "State of the Art on Ontology Alignment." International Journal of Knowledge Society Research 6, no. 1 (2015): 17–42. http://dx.doi.org/10.4018/ijksr.2015010102.

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Ontology mapping as a semantic data integration approach has evolved from traditional data integration solutions. The core problems and open issues related to early data integration approaches are also applicable to ontology mapping on the Semantic Web community. Therefore, in this review the authors present the related literature, starting from the traditional data integration approaches, in order to highlight the evolution of data integration from the early approaches. Once the roots of semantic data integration have been presented, the authors proceed to introduce the state-of-the-art of th

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Khan, Nouman, Sadaqat Jan, and M. Sohail. "Evaluation of Structural Ontology Alignment Techniques." International Journal of Computer Applications 180, no. 5 (2017): 33–37. http://dx.doi.org/10.5120/ijca2017916035.

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Sohail, Muhammad, Muhammad Waqar, and Nouman Khan. "Evaluation of String based Ontology Alignment Technique." International Journal of Computer Applications 179, no. 14 (2018): 1–8. http://dx.doi.org/10.5120/ijca2018916196.

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Zerhouni, Mourad, and Sidi Mohamed Benslimane. "Large-Scale Ontology Alignment- An Extraction Based Method to Support Information System Interoperability." International Journal of Strategic Information Technology and Applications 10, no. 2 (2019): 59–84. http://dx.doi.org/10.4018/ijsita.2019040104.

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Ontology alignment is an important way of establishing interoperability between Semantic Web applications that use different but related ontologies. Ontology alignment is the process of identifying semantically equivalent entities from multiple ontologies. This is not always obvious because technical constraints such as data volume and execution time are determining factors in the choice of an alignment algorithm. Nowadays, partitioning and modularization are two main strategies for breaking down large ontologies into blocks or ontology modules respectively to align ontologies. This article pr

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Mohammadi, Majid. "Bayesian Evaluation and Comparison of Ontology Alignment Systems." IEEE Access 7 (2019): 55035–49. http://dx.doi.org/10.1109/access.2019.2903861.

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Zavitsanos, Elias, George Paliouras, and George A. Vouros. "Gold Standard Evaluation of Ontology Learning Methods through Ontology Transformation and Alignment." IEEE Transactions on Knowledge and Data Engineering 23, no. 11 (2011): 1635–48. http://dx.doi.org/10.1109/tkde.2010.195.

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Deborah, Lazarus Jegatha, Ramachandran Baskaran, and Arputharaj Kannan. "Deontic Logic Based Ontology Alignment Technique for E-Learning." International Journal of Intelligent Information Technologies 8, no. 3 (2012): 56–72. http://dx.doi.org/10.4018/jiit.2012070104.

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The recent explosion in web services usage and information technology has led to the challenging issue of Ontology Construction and Alignment in order to enhance the semantics of web documents in E-Learning scenarios. In such a circumstance, it is necessary to provide an effective solution for Ontology Alignment that can help in student performance evaluation. The authors propose a rule-based solution for evaluating the students using Ontology Alignment techniques. For this purpose, they make use of the rules based on Deontic Logic and hence make a positive attempt in analyzing the presence of

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Ardjani, Fatima, and Djelloul Bouchiha. "A New Approach Based on the Bee Optimization Algorithm for Ontology Alignment." International Journal of Information Retrieval Research 9, no. 4 (2019): 13–22. http://dx.doi.org/10.4018/ijirr.2019100102.

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The ontology alignment process aims at generating a set of correspondences between entities of two ontologies. It is an important task, notably in the semantic web research, because it allows the joint consideration of resources defined in different ontologies. In this article, the authors developed an ontology alignment system called ABCMap+. It uses an optimization method based on artificial bee colonies (ABC) to solve the problem of optimizing the aggregation of three similarity measures of different matchers (syntactic, linguistic and structural) to obtain a single similarity measure. To e

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Gulić, Marko, and Marin Vuković. "An Iterative Automatic Final Alignment Method in the Ontology Matching System." Journal of information and organizational sciences 42, no. 1 (2018): 39–61. http://dx.doi.org/10.31341/jios.42.1.3.

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Ontology matching plays an important role in the integration of heterogeneous data sources that are described by ontologies. In order to determine correspondences between ontologies, a set of matchers can be used. After the execution of these matchers and the aggregation of the results obtained by these matchers, a final alignment method is executed in order to select appropriate correspondences between entities of compared ontologies. The final alignment method is an important part of the ontology matching process because it directly determines the output result of this process. In this paper

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TODOROV, KONSTANTIN, CELINE HUDELOT, ADRIAN POPESCU, and PETER GEIBEL. "FUZZY ONTOLOGY ALIGNMENT USING BACKGROUND KNOWLEDGE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 01 (2014): 75–112. http://dx.doi.org/10.1142/s0218488514500044.

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We propose an ontology alignment framework with two core features: the use of background knowledge and the ability to handle vagueness in the matching process and the resulting concept alignments. The procedure is based on the use of a generic reference vocabulary, which is used for fuzzifying the ontologies to be matched. The choice of this vocabulary is problem-dependent in general, although Wikipedia represents a general-purpose source of knowledge that can be used in many cases, and even allows cross language matchings. In the first step of our approach, each domain concept is represented

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Xue, Xingsi, Xiaojing Wu, and Junfeng Chen. "Optimizing Ontology Alignment Through an Interactive Compact Genetic Algorithm." ACM Transactions on Management Information Systems 12, no. 2 (2021): 1–17. http://dx.doi.org/10.1145/3439772.

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Ontology provides a shared vocabulary of a domain by formally representing the meaning of its concepts, the properties they possess, and the relations among them, which is the state-of-the-art knowledge modeling technique. However, the ontologies in the same domain could differ in conceptual modeling and granularity level, which yields the ontology heterogeneity problem. To enable data and knowledge transfer, share, and reuse between two intelligent systems, it is important to bridge the semantic gap between the ontologies through the ontology matching technique. To optimize the ontology align

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He, Yuan, Jiaoyan Chen, Denvar Antonyrajah, and Ian Horrocks. "BERTMap: A BERT-Based Ontology Alignment System." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5684–91. http://dx.doi.org/10.1609/aaai.v36i5.20510.

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Ontology alignment (a.k.a ontology matching (OM)) plays a critical role in knowledge integration. Owing to the success of machine learning in many domains, it has been applied in OM. However, the existing methods, which often adopt ad-hoc feature engineering or non-contextual word embeddings, have not yet outperformed rule-based systems especially in an unsupervised setting. In this paper, we propose a novel OM system named BERTMap which can support both unsupervised and semi-supervised settings. It first predicts mappings using a classifier based on fine-tuning the contextual embedding model

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Sun, Yufei. "A Comparative Evaluation of String Similarity Metrics for Ontology Alignment." Journal of Information and Computational Science 12, no. 3 (2015): 957–64. http://dx.doi.org/10.12733/jics20105420.

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Khiat, Abderrahmane, and Moussa Benaissa. "A New Instance-Based Approach for Ontology Alignment." International Journal on Semantic Web and Information Systems 11, no. 3 (2015): 25–43. http://dx.doi.org/10.4018/ijswis.2015070102.

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Due to the increasing number of information sources available on the web and their distribution and heterogeneity, ontology alignment became a very important and inevitable problem to resolve in order to ensure semantic interoperability between these sources. Instance-based ontology alignment represents a very promising technique to find semantic correspondences between entities of different ontologies. In practice, two situations may arise: ontologies that share common instances and those share few or do not share common instances. In this paper, the authors describe a new approach to manage

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Zhu, Hai, Xingsi Xue, Aifeng Geng, and He Ren. "Matching Sensor Ontologies with Simulated Annealing Particle Swarm Optimization." Mobile Information Systems 2021 (March 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/5510055.

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In recent years, innovative positioning and mobile communication techniques have been developing to achieve Location-Based Services (LBSs). With the help of sensors, LBS is able to detect and sense the information from the outside world to provide location-related services. To implement the intelligent LBS, it is necessary to develop the Semantic Sensor Web (SSW), which makes use of the sensor ontologies to implement the sensor data interoperability, information sharing, and knowledge fusion among intelligence systems. Due to the subjectivity of sensor ontology engineers, the heterogeneity pro

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Fakhar, Fatemeh. "Semantic Constraints Satisfaction Based Improved Quality of Ontology Alignment." Bulletin of Electrical Engineering and Informatics 2, no. 3 (2013): 182–89. http://dx.doi.org/10.11591/eei.v2i3.202.

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Development of informative and telecommunication technologies have caused to create much dissimilar information. As well with growing different information resources in ontology designs, the importance of management these dissimilar resources has increased. In spite of most matchers use diverse measures for discovery the mappings, some semantic inconsistencies in final alignment are unavoidable. So it is essential to enhance a post-processing phase to training error patterns in the final alignment. The impartial of this research was refining the ontology semantic constraints over defining sema

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