Matching large-scale biomedical ontologies with central concept based partitioning algorithm and Adaptive Compact Evolutionary Algorithm

Xue, Xingsi; Zhang, Jie

doi:10.1016/j.asoc.2021.107343

Cited by 81 publications

(46 citation statements)

References 29 publications

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“…Furthermore, all correspondences constitute an alignment, and reference alignment is the golden alignment provided by the domain experts, which is employed to evaluate OMTs' performance. Definition 3 The process of ontology matching is a function (Xue, Chen & Yao, 2018;Xue & Zhang, 2021):…”

Section: Preliminary Sensor Ontology and Sensor Ontology Matchingmentioning

confidence: 99%

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Matching sensor ontologies through siamese neural networks without using reference alignment

Xue

Jiang

Zhang

et al. 2021

PeerJ Computer Science

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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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Section: Preliminary Sensor Ontology and Sensor Ontology Matchingmentioning

confidence: 99%

“…The process of ontology matching is a function ( Xue, Chen & Yao, 2018 ; Xue & Zhang, 2021 ): where A is the final alignment; O 1 and O 2 are two to-be-matched ontologies; RA is the reference alignment (optional); R is the utilized resources; and P is the utilized parameters.…”

Section: Preliminarymentioning

confidence: 99%

Matching sensor ontologies through siamese neural networks without using reference alignment

Xue

Jiang

Zhang

et al. 2021

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, in the existing ontology matching methods based on CI, various ontology matchers are regarded as tools with the same effect and try to aggregate their outputs by determining the optimal weights [28]. e matching quality will decrease if ignoring the influence of different entity mappings on the matching results of different matching devices.…”

Section: Related Workmentioning

confidence: 99%

“…To be specific, sensor ontology alignment extraction technique works by extracting entity correspondences from different matching suggestions generated from different similarity measures for the same sensor ontology matching task to form the final alignment [28].…”

Section: Ontology Matching and Sensor Ontology Alignmentmentioning

confidence: 99%

“…where x is an element of a similarity matrix which stands for the similarity n. According to previous practical experience, we believe that matching pairs with similarity less than 0.7 are mostly mismatched, and 0.5 is a medium similarity in similarity interval [0, 1] [28]. erefore, μ Low is used to measure the degree of low similarity.…”

Section: Fuzzy Similaritymentioning

confidence: 99%

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Aggregating Heterogeneous Sensor Ontologies with Fuzzy Debate Mechanism

Xue

Zhang

et al. 2021

Security and Communication Networks

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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 makes them unable to make sure of the alignment’s high quality. To get rid of this shortcoming, in this work, a sensor ontology extraction method technology using Fuzzy Debate Mechanism (FDM) is proposed to aggregate the heterogeneous sensor data, which determines the final sensor concept correspondences by carrying out a debating process among different matchers. More than ever, a fuzzy similarity metric is presented to effectively measure two entities’ similarity values by membership function. It first uses the fuzzy membership function to model two entities’ similarity in vector space and then calculate their semantic distance with the cosine function. The testing cases from Bibliographic data which is furnished by the Ontology Alignment Evaluation Initiative (OAEI) and six sensor ontology matching tasks are used to evaluate the performance of our scheme in the experiment. The robustness and effectiveness of the proposed method are proved by comparing it with the advanced ontology matching techniques.

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An optimized system for sensor ontology meta‐matching using swarm intelligent algorithm

Lateef Haroon P S,

Patil,

Bidare Divakarachari

et al. 2024

Internet Technology Letters

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It is beneficial to annotate sensor data with distinct sensor ontologies in order to facilitate interoperability among different sensor systems. However, for this interoperability to be possible, comparable sensor ontologies are required since it is essential to make meaningful links between relevant sensor data. Swarm Intelligent Algorithms (SIAs), namely the Beetle Swarm Optimisation Algorithm (BSO), present a possible answer to ontology matching problems. This research focuses on a method for optimizing ontology alignment that employs BSO. A novel method for effectively controlling memory use and striking a balance between algorithm exploration and exploitation is proposed: the Simulated Annealing‐based Beetle Swarm Optimisation Algorithm (SA‐BSO). Utilizing Gray code for solution encoding, two compact operators for exploitation and exploration, and Probability Vectors (PVs) for swarming choosing exploitation and exploration, SA‐BSO combines simulated annealing with the beetle search process. Through inter‐swarm communication in every generation, SA‐BSO improves search efficiency in addressing sensor ontology matching. Three pairs of real sensor ontologies and the Conference track were used in the study to assess SA‐BSO's efficacy. Statistics show that SA‐BSO‐based ontology matching successfully aligns sensor ontologies and other general ontologies, particularly in conference planning scenarios.

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Matching large-scale biomedical ontologies with central concept based partitioning algorithm and Adaptive Compact Evolutionary Algorithm

Cited by 81 publications

References 29 publications

Matching sensor ontologies through siamese neural networks without using reference alignment

Matching sensor ontologies through siamese neural networks without using reference alignment

Aggregating Heterogeneous Sensor Ontologies with Fuzzy Debate Mechanism

An optimized system for sensor ontology meta‐matching using swarm intelligent algorithm

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