Journal articles: 'Ontology alignment'

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Sampson, Jennifer, John Krogstie, and Csaba Veres. "Ontology Alignment Quality." International Journal of Information System Modeling and Design 2, no. 3 (July 2011): 1–23. http://dx.doi.org/10.4018/jismd.2011070101.

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Recently semantic web technologies, such as ontologies, have been proposed as key enablers for integrating heterogeneous data schemas in business and governmental systems. Algorithms designed to align different but related ontologies have become necessary as differing ontologies proliferate. The process of ontology alignment seeks to find corresponding entities in a second ontology with the same or the closest meaning for each entity in a single ontology. This research is motivated by the need to provide tools and techniques to support the task of validating ontology alignment statements, since it cannot be guaranteed that the results from automated tools are accurate. The authors present a framework for understanding ontology alignment quality and describe how AlViz, a tool for visual ontology alignment, may be used to improve the quality of alignment results. An experiment was undertaken to test the claim that AlViz supports the task of validating ontology alignments. A promising result found that the tool has potential for identifying missing alignments and for rejecting false alignments.

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Ivanova, Tatyana. "Ontology Alignment." International Journal of Knowledge and Systems Science 1, no. 4 (October 2010): 22–40. http://dx.doi.org/10.4018/jkss.2010100102.

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A grand number of ontologies have been developed and are publicly accessible on the Web making techniques for mapping between various ontologies more significant. Research has been made in the area of ontology alignment, a grand number of approaches, algorithms, and tools have been developed in recent years, but are still not “perfect” and excellent knowledge. In this article, the author makes an overall view of the state of ontology alignment, including the latest research, comparing many approaches, and analyzing their strengths and drawbacks. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which can be used for ontology mapping in all cases, making knowledge about developed ontology mapping methods and their clear classification needed.

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Idoudi, Rihab, Karim Saheb Ettabaa, Basel Solaiman, and Kamel Hamrouni. "Ontology Knowledge Mining for Ontology Alignment." International Journal of Computational Intelligence Systems 9, no. 5 (September 2, 2016): 876–87. http://dx.doi.org/10.1080/18756891.2016.1237187.

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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 (July 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, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions. The alignment includes 1:1, 1:n, and m:n equivalence and subsumption correspondences, and is available in both Expressive and Declarative Ontology Alignment Language (EDOAL) and rule syntax. The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies. This allows it to be used by extensional alignment systems or those that require training data. This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) complex track to help researchers test their automated alignment systems and algorithms. This paper also analyzes the challenges inherent in effectively generating, detecting, and evaluating complex ontology alignments and provides a road map for future work on this topic.

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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. Due to human’s subjectivity, a concept in different sensor ontologies could be defined with different terminologies and contexts, yielding the ontology heterogeneity problem. Thus, before using these ontologies, it is necessary to integrate their knowledge by finding the correspondences between their concepts, i.e., the so-called ontology matching. In this work, a novel sensor ontology matching framework is proposed, which aggregates three kinds of Concept Similarity Measures (CSMs) and an alignment extraction approach to determine the sensor ontology alignment. To ensure the quality of the alignments, we further propose a compact Particle Swarm Optimization algorithm (cPSO) to optimize the aggregating weights for the CSMs and a threshold for filtering the alignment. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s conference track and two pairs of real sensor ontologies to test cPSO’s performance. The experimental results show that the quality of the alignments obtained by cPSO statistically outperforms other state-of-the-art sensor ontology matching techniques.

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Xue, Xingsi, and Jianhua Liu. "Optimizing Ontology Alignment Through Compact MOEA/D." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (February 2, 2017): 1759004. http://dx.doi.org/10.1142/s0218001417590042.

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In order to support semantic inter-operability in many domains through disparate ontologies, we need to identify correspondences between the entities across different ontologies, which is commonly known as ontology matching. One of the challenges in ontology matching domain is how to select weights and thresholds in the ontology aligning process to aggregate the various similarity measures to obtain a satisfactory alignment, so called ontology meta-matching problem. Nowadays, the most suitable methodology to address the ontology meta-matching problem is through Evolutionary Algorithm (EA), and the Multi-Objective Evolutionary Algorithms (MOEA) based approaches are emerging as a new efficient methodology to face the meta-matching problem. Moreover, for dynamic applications, it is necessary to perform the system self-tuning process at runtime, and thus, efficiency of the configuration search strategies becomes critical. To this end, in this paper, we propose a problem-specific compact Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), in the whole ontology matching process of ontology meta-matching system, to optimize the ontology alignment. The experimental results show that our proposal is able to highly reduce the execution time and main memory consumption of determining the optimal alignments through MOEA/D based approach by 58.96% and 67.60% on average, respectively, and the quality of the alignments obtained is better than the state of the art ontology matching systems.

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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 (July 21, 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. We propose a two-step quadratic-time algorithm for automatically correcting such violations, and evaluate it against datasets from the Ontology Alignment Evaluation Initiative 2019, comparing the results to a state-of-the-art approach. The proposed algorithm was significantly faster and less aggressive; that is, it performed fewer modifications over the original alignment when compared to the state-of-the-art algorithm.

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Haddad, AbdulHameed, and Akram Selah. "Ontology Alignment with FOAM++." International Journal of Computer Applications 18, no. 8 (March 31, 2011): 14–20. http://dx.doi.org/10.5120/2305-2435.

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Scharffe, François, Ondřej Zamazal, and Dieter Fensel. "Ontology alignment design patterns." Knowledge and Information Systems 40, no. 1 (April 26, 2013): 1–28. http://dx.doi.org/10.1007/s10115-013-0633-y.

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Lin, Weiwei, and Reiko Haga. "Matching Cyber Security Ontologies through Genetic Algorithm-Based Ontology Alignment Technique." Security and Communication Networks 2021 (November 30, 2021): 1–7. http://dx.doi.org/10.1155/2021/4856265.

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Security ontology can be used to build a shared knowledge model for an application domain to overcome the data heterogeneity issue, but it suffers from its own heterogeneity issue. Finding identical entities in two ontologies, i.e., ontology alignment, is a solution. It is important to select an effective similarity measure (SM) to distinguish heterogeneous entities. However, due to the complex semantic relationships among concepts, no SM is ensured to be effective in all alignment tasks. The aggregation of SMs so that their advantages and disadvantages complement each other directly affects the quality of alignments. In this work, we formally define this problem, discuss its challenges, and present a problem-specific genetic algorithm (GA) to effectively address it. We experimentally test our approach on bibliographic tracks provided by OAEI and five pairs of security ontologies. The results show that GA can effectively address different heterogeneous ontology-alignment tasks and determine high-quality security ontology alignments.

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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 (June 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 alignment’s quality, this article proposes an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique, which consists of an automatic ontology matching process based on a Compact Genetic Algorithm (CGA) and a collaborative user validating process based on an argumentation framework. First, CGA is used to automatically match the ontologies, and when it gets stuck in the local optima, the collaborative validation based on the multi-relationship argumentation framework is activated to help CGA jump out of the local optima. In addition, we construct a discrete optimization model to define the ontology matching problem and propose a hybrid similarity measure to calculate two concepts’ similarity value. In the experiment, we test the performance of ICGA with the Ontology Alignment Evaluation Initiative’s interactive track, and the experimental results show that ICGA can effectively determine the ontology alignments with high quality.

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Seyed H. Haeri (Hossein), Hassan Abolhassani, Vahed Qazvinian, and Babak Bagheri Hariri. "Coincidence-Based Scoring of Mappings in Ontology Alignment." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 7 (September 20, 2007): 803–16. http://dx.doi.org/10.20965/jaciii.2007.p0803.

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Ontology Matching (OM) which targets finding a set of alignments across two ontologies, is a key enabler for the success of Semantic Web. In this paper, we introduce a new perspective on this problem. By interpreting ontologies as Typed Graphs embedded in a Metric Space,coincidenceof the structures of the two ontologies is formulated. Having such a formulation, we define a mechanism to score mappings. This scoring can then be used to extract a good alignment among a number of candidates. To do this, this paper introduces three approaches: The first one, straightforward and capable of finding the optimum alignment, investigates all possible alignments, but its runtime complexity limits its use to small ontologies only. To overcome this shortcoming, we introduce a second solution as well which employs a Genetic Algorithm (GA) and shows a good effectiveness for some certain test collections. Based on approximative approaches, a third solution is also provided which, for the same purpose, measures random walks in each ontology versus the other.

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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 process to calculate its local factor. Through comprehensively considering these two factors, the judgment factor of an entity mapping can be obtained, which is further applied in extracting the final sensor ontology alignment. This work makes use of the bibliographic track provided by the Ontology Alignment Evaluation Initiative (OAEI) and five real sensor ontologies in the experiment to assess the performance of our method. The comparing results with the most advanced ontology matching techniques show the robustness and effectiveness of our approach.

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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 (February 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 as a fuzzy set of reference concepts. In the next step, the fuzzified domain concepts are matched to one another, resulting in fuzzy descriptions of the matches of the original concepts. Based on these concept matches, we propose an algorithm that produces a merged fuzzy ontology that captures what is common to the source ontologies. The paper describes experiments in the domain of multimedia by using ontologies containing tagged images, as well as an evaluation of the approach in an information retrieval setting. The undertaken fuzzy approach has been compared to a classical crisp alignment by the help of a ground truth that was created based on human judgment.

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Ouali, Imene, Faiza Ghozzi, Raouia Taktak, and Mohamed Saifeddine Hadj Sassi. "Ontology Alignment using Stable Matching." Procedia Computer Science 159 (2019): 746–55. http://dx.doi.org/10.1016/j.procs.2019.09.230.

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Brown, J. W., A. Birmingham, P. E. Griffiths, F. Jossinet, R. Kachouri-Lafond, R. Knight, B. F. Lang, et al. "The RNA structure alignment ontology." RNA 15, no. 9 (July 21, 2009): 1623–31. http://dx.doi.org/10.1261/rna.1601409.

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Zahaf, Ahmed, and Mimoun Malki. "Alignment Evolution under Ontology Change." International Journal of Information Technology and Web Engineering 11, no. 2 (April 2016): 14–38. http://dx.doi.org/10.4018/ijitwe.2016040102.

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The alignment of ontologies is the backbone of semantic interoperability. It facilitates the import of data from an ontology to another, translating queries between them or merging ontologies in a global one. However, these services cannot be guaranteed throughout the life cycle of the ontology. The problem is that the evolution of mapped ontologies may be affected and make obsolete the relationship of the mapping. Inspired by belief revision theory, the authors identify and formalize the constraints and requirements of the alignment evolution problem. Then they give an orchestration of designed operations to resolve the problem. The satisfaction of these constraints and requirements is discussed for each operation showing its strengths and weaknesses. Finally, the authors conduct an experimental process with the objective to show the limits of alignment evolution methods and ontology matching tools when dealing with alignment evolution problem highlighting the emergency to invest in dedicated methods.

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Thayasivam, U., and P. Doshi. "Speeding Up Iterative Ontology Alignment using Block-Coordinate Descent." Journal of Artificial Intelligence Research 50 (August 25, 2014): 805–45. http://dx.doi.org/10.1613/jair.4366.

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In domains such as biomedicine, ontologies are prominently utilized for annotating data. Consequently, aligning ontologies facilitates integrating data. Several algorithms exist for automatically aligning ontologies with diverse levels of performance. As alignment applications evolve and exhibit online run time constraints, performing the alignment in a reasonable amount of time without compromising the quality of the alignment is a crucial challenge. A large class of alignment algorithms is iterative and often consumes more time than others in delivering solutions of high quality. We present a novel and general approach for speeding up the multivariable optimization process utilized by these algorithms. Specifically, we use the technique of block-coordinate descent (BCD), which exploits the subdimensions of the alignment problem identified using a partitioning scheme. We integrate this approach into multiple well-known alignment algorithms and show that the enhanced algorithms generate similar or improved alignments in significantly less time on a comprehensive testbed of ontology pairs. Because BCD does not overly constrain how we partition or order the parts, we vary the partitioning and ordering schemes in order to empirically determine the best schemes for each of the selected algorithms. As biomedicine represents a key application domain for ontologies, we introduce a comprehensive biomedical ontology testbed for the community in order to evaluate alignment algorithms. Because biomedical ontologies tend to be large, default iterative techniques find it difficult to produce a good quality alignment within a reasonable amount of time. We align a significant number of ontology pairs from this testbed using BCD-enhanced algorithms. Our contributions represent an important step toward making a significant class of alignment techniques computationally feasible.

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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 (October 16, 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, several simulations were performed with a data set from the Ontology Alignment Evaluation Initiative. Simple similarity functions were used, rather than aligners known in the literature, to demonstrate that the results would be more influenced by the proposed meta-alignment approach than the functions used. Findings The results showed that the framework is able to adapt to different test cases. The approach achieved better results when compared with existing ontology meta-matchers. Originality/value Although approaches for OMM have been proposed, it is not easy to use them during software development. On the other hand, this work presents a framework that can be used by developers to align ontologies. New ontology matchers can be added and the framework is extensible to new methods. Moreover, this work presents a novel OMM approach modeled as a linear equation system which can be easily computed.

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Xue, Xingsi, Chao Jiang, Jie Zhang, Hai Zhu, and Chaofan Yang. "Matching sensor ontologies through siamese neural networks without using reference alignment." PeerJ Computer Science 7 (June 18, 2021): e602. http://dx.doi.org/10.7717/peerj-cs.602.

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Sensors have been growingly used in a variety of applications. The lack of semantic information of obtained sensor data will bring about the heterogeneity problem of sensor data in semantic, schema, and syntax levels. To solve the heterogeneity problem of sensor data, it is necessary to carry out the sensor ontology matching process to determine correspondences among heterogeneous sensor concepts. In this paper, we propose a Siamese Neural Network based Ontology Matching technique (SNN-OM) to align the sensor ontologies, which does not require the utilization of reference alignment to train the network model. In particular, a representative concepts extraction method is presented to enhance the model’s performance and reduce the time of the training process, and an alignment refining method is proposed to enhance the alignments’ quality by removing the logically conflict correspondences. The experimental results show that SNN-OM is capable of efficiently determining high-quality sensor ontology alignments.

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Laukaitis, Algirdas, and Neda Laukaitytė. "SEMI-AUTOMATIC ONTOLOGICAL ALIGNMENT OF DIGITIZED BOOKS PARALLEL CORPORA." Mokslas - Lietuvos ateitis 13 (July 2, 2021): 1–8. http://dx.doi.org/10.3846/mla.2021.15034.

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In this paper, we present a method for general ontology management integration with an alignment of digitized books paraphrase corpus, which have been compiled from bilingual parallel corpus. We show that our method can improve ontology development and consistency checking when we add semantic parsing and machine translation to the process of general knowledge management. Additionally, we argue that the focus on one’s favorite books gives a factor of gamification for knowledge management process. A new formalism of semantic parsing ontological alignments is introduced and its use for ontology development and consistency checking is discussed. It is shown that existing general ontologies requires much more axioms than it is currently available in order to explain unaligned content of books. Proactive learning approach is suggested as part of the solution to improve development of ontology predicates and axioms. WordNet, FrameNet and SUMO ontologies are used as a starting knowledge base of paraphrase corpus semantic alignment method.

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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 users with different preferences is a challenging problem. To face this challenge, in our work, a Multiobjective Evolutionary Algorithm (MOEA) is used in determining different nondominated solutions. In particular, the evaluating metric on sensor ontology alignment’s quality is proposed, which takes into consideration user’s preferences and do not need to use the Reference Alignment (RA) beforehand; an optimization model is constructed to define the sensor ontology matching problem formally, and a selection operator is presented, which can make MOEA uniformly improve the solution’s objectives. In the experiment, the benchmark from the Ontology Alignment Evaluation Initiative (OAEI) and the real ontologies of the sensor domain is used to test the performance of our approach, and the experimental results show the validity of our approach.

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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 genetic algorithm (NSGA-II), is investigated to address the ontology matching problem, which outputs the knee solutions in the PF to meet diverse DMs’ requirements. In this study, for further enhancing the performance of NSGA-II, we propose to incorporate into NSGA-II’s evolutionary process the monkey king evolution algorithm (MKE) as the local search algorithm. The improved NSGA-II (iNSGA-II) is able to better converge to the real Pareto optimum region and ameliorate the quality of the solution. The experiment uses the famous benchmark given by the ontology alignment evaluation initiative (OAEI) to assess the performance of iNSGA-II, and the experiment results present that iNSGA-II is able to seek out preferable alignments than OAEI’s participators and NSGA-II-based ontology matching technique.

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Azzam, Said Rabah, and Shikun Zhou. "Comparative Analysis of Ontology Alignment Methodology." International Journal of Multimedia and Image Processing 3, no. 3/4 (September 1, 2013): 172–79. http://dx.doi.org/10.20533/ijmip.2042.4647.2013.0022.

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Nejhadi, Azadeh Haratian, Bita Shadgar, and Alireza Osareh. "Ontology Alignment Using Machine Learning Techniques." International Journal of Computer Science and Information Technology 3, no. 2 (April 30, 2011): 139–50. http://dx.doi.org/10.5121/ijcsit.2011.3210.

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Ardjani, Fatima, Djelloul Bouchiha, and Mimoun Malki. "Ontology-Alignment Techniques: Survey and Analysis." International Journal of Modern Education and Computer Science 7, no. 11 (November 8, 2015): 67–78. http://dx.doi.org/10.5815/ijmecs.2015.11.08.

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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 (December 15, 2017): 33–37. http://dx.doi.org/10.5120/ijca2017916035.

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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 the largescale entity and their complex semantic relationships, swarm intelligence (SI) based techniques are emerging as a popular approach to optimize the sensor ontology alignment. Inspired by the success of the flower pollination algorithm (FPA) in the IoT domain, this work further proposes a compact FPA (CFPA), which introduces the compact encoding mechanism to improve the algorithm’s efficiency, and on this basis, the compact exploration and exploitation operators are proposed, and an adaptive switching probability is presented to trade-off these two searching strategies. The experiment uses the ontology alignment evaluation initiative (OAEI)’s benchmark and the real sensor ontologies to test CFPA’s performance. The statistical comparisons show that CFPA significantly outperforms other state-of-the-art sensor ontology matching techniques.

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Usmani, A. U., M. Jadidi, and G. Sohn. "TOWARDS THE AUTOMATIC ONTOLOGY GENERATION AND ALIGNMENT OF BIM AND GIS DATA FORMATS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences VIII-4/W2-2021 (October 7, 2021): 183–88. http://dx.doi.org/10.5194/isprs-annals-viii-4-w2-2021-183-2021.

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Abstract. Establishing semantic interoperability between BIM and GIS is vital for geospatial information exchange. Semantic web have a natural ability to provide seamless semantic representation and integration among the heterogeneous domains like BIM and GIS through employing ontology. Ontology models can be defined (or generated) using domain-data representations and further aligned across other ontologies by the semantic similarity of their entities - introducing cross-domain ontologies to achieve interoperability of heterogeneous information. However, due to extensive semantic features and complex alignment (mapping) relations between BIM and GIS data formats, many approaches are far from generating semantically-rich ontologies and perform effective alignment to address geospatial interoperability. This study highlights the fundamental perspectives to be addressed for BIM and GIS interoperability and proposes a comprehensive conceptual framework for automatic ontology generation followed by ontology alignment of open-standards for BIM and GIS data formats. It presents an approach based on transformation patterns to automatically generate ontology models, and semantic-based and structure-based alignment techniques to form cross-domain ontology. Proposed two-phase framework provides ontology model generation for input XML schemas (i.e. of IFC and CityGML formats), and illustrates alignment technique to potentially develop a cross-domain ontology. The study concludes anticipated results of cross-domain ontology can provides future perspectives in knowledge-discovery applications and seamless information exchange for BIM and GIS.

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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 (April 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 proposes ONTEM as an effective alignment method for large-scale ontology based on the ontology entities extraction. This article conducts a comprehensive evaluation using the datasets of the OAEI 2018 campaign. The obtained results are promising, and they revealed that ONTEM is one of the most effective systems.

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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 (January 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 the ontology mappings systems including the early approaches and the systems that can be compared through the Ontology Alignment Initiative (OAEI).

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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 improvement. In this work, an extended compact genetic algorithm-based ontology entity matching technique (ECGA-OEM) is proposed, which uses both the compact encoding mechanism and linkage learning approach to match the ontologies efficiently. Compact encoding mechanism does not need to store and maintain the whole population in the memory during the evolving process, and the utilization of linkage learning protects the chromosome’s building blocks, which is able to reduce the algorithm’s running time and ensure the alignment’s quality. In the experiment, ECGA-OEM is compared with the participants of ontology alignment evaluation initiative (OAEI) and the state-of-the-art ontology matching techniques, and the experimental results show that ECGA-OEM is both effective and efficient.

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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 instances show that the proposed method significantly improves the quality of mappings.

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Xue, Xingsi, Yuping Wang, and Aihong Ren. "Optimizing ontology alignment through Memetic Algorithm based on Partial Reference Alignment." Expert Systems with Applications 41, no. 7 (June 2014): 3213–22. http://dx.doi.org/10.1016/j.eswa.2013.11.021.

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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 (December 31, 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 concepts that have less likelihood of being matched. Design/methodology/approach – Algorithms are written for finding key concepts, concept matching and relationship matching. WordNet is used for solving synonym problems during the matching process. The technique is evaluated using the reference alignments between ontologies from ontology alignment evaluation initiative benchmark in terms of degree of similarity, Pearson’s correlation coefficient and IR measures precision, recall and F-measure. Findings – Positive correlation between the degree of similarity and degree of similarity (reference alignment) and computed values of precision, recall and F-measure showed that if only key concepts of ontologies are compared, a time and search space efficient ontology matching system can be developed. Originality/value – On the basis of the present novel approach for ontology matching, it is concluded that using key concepts for ontology matching gives comparable results in reduced time and space.

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Wimmer, Hayden, Victoria Yoon, and Roy Rada. "Applying Semantic Web Technologies to Ontology Alignment." International Journal of Intelligent Information Technologies 8, no. 1 (January 2012): 1–9. http://dx.doi.org/10.4018/jiit.2012010101.

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Ontologies are the backbone of intelligent computing on the World Wide Web but also crucial in many decision support situations. Many sophisticated tools have been developed to support working with ontologies, including prominently exploiting the vast array of existing ontologies. A system called ALIGN is developed that demonstrates how to use freely available tools to facilitate ontology alignment. First two ontologies are built with the ontology editor Protégé and represented in OWL. ALIGN then accesses these ontologies via Java’s JENA framework and SPARQL queries. The efficacy of the ALIGN prototype is demonstrated on a drug-drug interaction problem. The prototype could readily be applied to other domains or be incorporated into decision support tools.

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Jauro, Fatsuma, S. B. Junaidu, and S. E. Abdullahi. "Falcon-AO++: An Improved Ontology Alignment System." International Journal of Computer Applications 94, no. 2 (May 16, 2014): 1–7. http://dx.doi.org/10.5120/16312-5541.

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Han, Jun, Hyunjun Jung, and Doo-Kwon Baik. "Ontology Alignment by Using Discrete Cuckoo Search." KIPS Transactions on Software and Data Engineering 3, no. 12 (December 31, 2014): 523–30. http://dx.doi.org/10.3745/ktsde.2014.3.12.523.

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Azzam, Said Rabah, and Shikun Zhou. "Assumption/Commitment Algorithmic Approach for Ontology Alignment." International Journal of Intelligent Computing Research 3, no. 4 (December 1, 2012): 278–83. http://dx.doi.org/10.20533/ijicr.2042.4655.2012.0035.

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Kachroudi, Marouen, and Sadok Ben Yahia. "Dealing with Direct and Indirect Ontology Alignment." Journal on Data Semantics 7, no. 4 (November 19, 2018): 237–52. http://dx.doi.org/10.1007/s13740-018-0098-y.

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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 (January 17, 2018): 1–8. http://dx.doi.org/10.5120/ijca2018916196.

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Kachroudi, Marouen, Sami Zghal, and Sadok Ben Yahia. "Bridging the multilingualism gap in ontology alignment." International Journal of Metadata, Semantics and Ontologies 9, no. 3 (2014): 252. http://dx.doi.org/10.1504/ijmso.2014.063139.

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Juanzi Li, Jie Tang, Yi Li, and Qiong Luo. "RiMOM: A Dynamic Multistrategy Ontology Alignment Framework." IEEE Transactions on Knowledge and Data Engineering 21, no. 8 (August 2009): 1218–32. http://dx.doi.org/10.1109/tkde.2008.202.

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Hao, Wenning, Junyue Chen, Gang Chen, Ruizhi Kang, Zixuan Zhang, and Ao Zou. "Ontology Alignment Repair Through 0-1 Programming." IEEE Access 7 (2019): 155424–36. http://dx.doi.org/10.1109/access.2019.2938967.

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Bock, Jürgen, and Jan Hettenhausen. "Discrete particle swarm optimisation for ontology alignment." Information Sciences 192 (June 2012): 152–73. http://dx.doi.org/10.1016/j.ins.2010.08.013.

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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 (April 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 large crosslingual resources and conference. The challenges presented by each track were addressed by the DSSim team.

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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 (January 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 syntactic and structural technic. (iii) The post-alignment phase which optimizes the matching by a hybrid technique of continuous NSGA-II and networks of neurons. This approach is compared with the greatest systems per the Ontology Alignment Evaluation Initiative (OAEI) standard. The experimental results appear that the proposed approach is effective.

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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 (October 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 evaluate the ABCMap+ ontology alignment system, authors considered the OAEI 2012 alignment system evaluation campaign. Experiments have been carried out to get the best ABCMap+'s alignment. Then, a comparative study showed that the ABCMap+ system is better than participants in the OAEI 2012 in terms of Recall and Precision.

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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 (November 2011): 1635–48. http://dx.doi.org/10.1109/tkde.2010.195.

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Khan, Wajahat Ali, Muhammad Bilal Amin, Asad Masood Khattak, Maqbool Hussain, Muhammad Afzal, Sungyoung Lee, and Eun Soo Kim. "Object-oriented and ontology-alignment patterns-based expressive Mediation Bridge Ontology (MBO)." Journal of Information Science 41, no. 3 (March 6, 2015): 296–314. http://dx.doi.org/10.1177/0165551514560952.

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Journal articles on the topic 'Ontology alignment'

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