Sensor Ontology Research Articles

Sensor Ontology Matching With Compact Firework Algorithm

Sensor Ontology Matching With Compact Firework Algorithm

Semantic sensor data integration for talent development via hybrid multi‐objective evolutionary algorithm

AbstractIn this work, we propose a new hybrid Multi‐Objective Evolutionary Algorithm (hMOEA) specifically designed for semantic sensor data integration, targeting talent development within the burgeoning field of the Semantic Internet of Things (SIoT). Our approach synergizes the capabilities of Multi‐Objective Particle Swarm Optimization and Genetic Algorithms to tackle the sophisticated challenges inherent in Sensor Ontology Matching (SOM). This innovative hMOEA framework is adapt at discerning precise semantic correlations among diverse ontologies, thereby facilitating seamless interoperability and enhancing the functionality of IoT applications. Central to our contributions are the development of an advanced multi‐objective optimization model that underpins the SOM process, the implementation of the hMOEA framework which sets a new benchmark for accurate semantic sensor data integration, and the rigorous validation of hMOEA's superiority through extensive testing in varied real‐world SOM scenarios. This research not only marks a significant advancement in SOM but also highlights the critical role of cutting‐edge SOM methodologies in educational curricula, for example, the new business subject education proposed by China in recent years, aimed at equipping future professionals with the necessary skills to innovate and lead in the SIoT and SW domains.

A lightweight sensor ontology for supporting sensor selection, deployment, and data processing in forming processes

In the era of smart manufacturing, modern manufacturing systems face high demands for enhancing process performance and reducing machine downtime. Sensors and process data are essential for successfully implementing data-driven approaches to guarantee robust and reliable process monitoring, tool conditioning, or quality assurance. However, the accuracy and performance of such approaches are highly dependent on the quality of the gathered sensor data and influenced by the implemented data acquisition and processing methods. For this purpose, this work proposes a lightweight sensor ontology to provide a comprehensive overview to characterize underlying relationships between the physical environment and the quality of the data sets. The extended sensor ontology, in combination with domain knowledge, aims to support engineers in fully exploiting the potential of sensor data to obtain trustworthy data sets in forming technologies. As a result, this approach can improve the implementation of automated and data-driven process monitoring of forming systems and tools.

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

AbstractIt 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.

Retracted: Semisupervised Learning-Based Sensor Ontology Matching

Retracted: Semisupervised Learning-Based Sensor Ontology Matching

Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm.

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-matching problem is essentially a multi-modal optimization problem (MMOP), a niching strategy is introduced into MOPSO to enable the algorithm to find more global optimal solutions that meet the needs of different decision makers. In addition, a diversity-enhancing strategy and an opposition-based learning (OBL) strategy are introduced into the evolution process of NMOPSO to improve the quality of sensor ontology matching and ensure the solutions converge to the real Pareto fronts (PFs). The experimental results demonstrate the effectiveness of NMOPSO in comparison to MOPSO-based matching techniques and participants of the Ontology Alignment Evaluation Initiative (OAEI).

MATHEMATICAL MODELS ONTOLOGY OF TECHNOLOGICAL OBJECTS FOR INDUSTRIAL ENTERPRISES. PART 2. ONTOLOGY IMPLEMENTATION AND VERIFICATION

The article proposes the implementation of an applied ontology of mathematical models of technological objects for the design of a subsystem of decision-making support, which issues recommendations for the mathematical apparatus in relation to the development goals for the automated management of a food enterprise. It consists of 46 entities with corresponding relationships, attributes, and axioms, and is also implemented in the OWL language by the Protege open platform tools, taking into account existing standards and recommendations. In the structure of the ontology, the mathematical model is represented as branches of subclasses with the corresponding sets of attributes, characterized by their relationship to the higher-level model. Within this ontology, 17 types of relationships are presented. Applied ontology passed two stages of verification: structural - based on generally accepted estimates; logical - by testing queries and manually checking the correctness of the results. In particular, examples of model selection using an ontology for virtual sensors are given. The use of the proposed ontology in the structure of the management decision support subsystem increases the efficiency of these decisions, the validity of management actions and the efficiency of the technological component of the enterprise. Also, the ontology can be integrated into the ontology of industrial enterprises tasks or other domain ontologies.

Interactive complex ontology matching with local and global similarity deviations

<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 matching, which consists of three novel components. First, a local and global similarity deviation (LGSD) metrics are presented to measure the consistency of similarity measures (SMs) and single out the less consistent SMs for user validation. Second, we present a genetic algorithm (GA) based SM selector to evolve the SM subsets. Lastly, a problem-specific induced ordered weighting aggregating (IOWA) operator based SM aggregator is proposed to assess the quality of selected SMs. The experiment evaluates IOM-LGSD with the ontology alignment evaluation initiative (OAEI) Benchmark and three real-world sensor ontologies. The evaluation underscores the effectiveness of IOM-LGSD in efficiently identifying high-quality ontology alignments, which consistently outperforms comparative methods in terms of effectiveness and efficiency.</p></abstract>

Semantic-based anomaly detection in laboratory environments using SOSA/SSN sensor ontology frameworks

Semantic-based anomaly detection in laboratory environments using SOSA/SSN sensor ontology frameworks

A multi-objective particle swarm optimization with density and distribution-based competitive mechanism for sensor ontology meta-matching

Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them. The problem of heterogeneity between sensor ontologies is introduced because different sensor ontology engineers have different ways of describing sensor devices and different structures for the construction of sensor ontologies. Addressing the heterogeneity of sensor ontologies contributes to facilitate the semantic fusion of two sensor ontologies, enabling the sharing and reuse of sensor information. To solve the above problem, an ontology meta-matching method is proposed by this paper to find out the correspondence between entities in distinct sensor ontologies. How to measure the degree of similarity between entities with a set of suitable similarity measures and how to better integrate multiple measures to determine the equivalent entities are the challenges of the ontology meta-matching problem. In this paper, two approximate measurement methods of the quality for ontology matching results are designed, and a multi-objective optimization model for the ontology meta-matching problem is constructed based on these methods. Eventually, a multi-objective particle swarm optimization (MOPSO) algorithm is propounded to dispose of the problem and optimize the quality of ontology meta-matching results, which is named density and distribution-based competitive mechanism multi-objective particle swarm algorithm (D^{2}CMOPSO). The sophistication of the D^{2}CMOPSO based sensor ontology meta-matching method is verified through experiments. Comparing with other matching systems and advanced systems of Ontology Alignment Evaluation Initiative (OAEI), the proposed method can improve the quality of matching results more effectively.

Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm

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.

Matching sensor ontologies with unsupervised neural network with competitive learning

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 networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal’s performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.

Matching Sensor Ontologies With Multi-Context Similarity Measure and Parallel Compact Differential Evolution Algorithm

Sensor ontology is able to resolve the sensor data heterogeneity issue among the Cybertwin-driven 6G based Internet of Everything (IoE) systems. However, due to human subjectivity, the sensor ontologies also suffer from the heterogeneity problem. To address this problem, it is necessary to execute the sensor Ontology Matching (OM) process, i.e., finding the identical entity pairs between two ontologies. To this end, we first propose a Multi-Context based ESM (MC-ESM) to measure the similarity of two entities by taking into consideration their semantic contexts. After that, a parallel compact Differential Evolution with Adaptive Step Length (pcDE-ASL) is proposed to find all entity mappings, which uses the probability presentation on the population to save the memory consumption, and the parallel processing mechanism and ASL to help the algorithm efficiently converge on the global optima. The experimental results show that pcDE-ASL is both effective and efficient.

Semisupervised Learning-Based Sensor Ontology Matching

Sensor ontology models the sensor information and knowledge in a machine-understandable way, which aims at addressing the data heterogeneity problem on the Internet of Things (IoT). However, the existing sensor ontologies are maintained independently for different requirements, which might define the same concept with different terms or context, yielding the heterogeneity issue. Since the complex semantic relationship between the sensor concepts and the large-scale entities is to be dealt with, finding the identical entity correspondences is an error-prone task. To effectively determine the sensor entity correspondences, this work proposes a semisupervised learning-based sensor ontology matching technique. First, we borrow the idea of “centrality” from the social network to construct the training examples; then, we present an evolutionary algorithm- (EA-) based metamatching technique to train the model of aggregating different similarity measures; finally, we use the trained model to match the rest entities. The experiment uses the benchmark as well as three real sensor ontologies to test our proposal’s performance. The experimental results show that our approach is able to determine high-quality sensor entity correspondences in all matching tasks.

Matching sensor ontologies through siamese neural networks without using reference alignment.

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.

An improved multi-objective evolutionary optimization algorithm with inverse model for matching sensor ontologies

An improved multi-objective evolutionary optimization algorithm with inverse model for matching sensor ontologies

Aggregating Heterogeneous Sensor Ontologies with Fuzzy Debate Mechanism

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.

CoAP-Based Lightweight Interoperability Semantic Sensor and Actuator Ontology for IoT Ecosystem

CoAP-Based Lightweight Interoperability Semantic Sensor and Actuator Ontology for IoT Ecosystem

Matching Sensor Ontologies with Simulated Annealing Particle Swarm Optimization

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 problem is introduced, which hampers the communications among these sensor ontologies. To address this problem, sensor ontology matching is introduced to establish the corresponding relationship between different sensor terms. Among all ontology matching technologies, Particle Swarm Optimization (PSO) can represent a contributing method to deal with the low-quality ontology alignment problem. For the purpose of further enhancing the quality of matching results, in our work, sensor ontology matching is modeled as the meta-matching problem firstly, and then based on this model, aiming at various similarity measures, a Simulated Annealing PSO (SAPSO) is proposed to optimize their aggregation weights and the threshold. In particular, the approximate evaluation metrics for evaluating quality of alignment without reference are proposed, and a Simulated Annealing (SA) strategy is applied to PSO’s evolving process, which is able to help the algorithm avoid the local optima and enhance the quality of solution. The well-known Ontology Alignment Evaluation Initiative’s benchmark (OAEI’s benchmark) and three real sensor ontologies are used to verify the effectiveness of SAPSO. The experimental results show that SAPSO is able to effectively match the sensor ontologies.

Integrating Sensor Ontologies with Global and Local Alignment Extractions

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.