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Biomedical ontology is a unified model for describing biomedical knowledge, which can be of help to solve the issues of heterogeneity in different biomedical databases. However, the existing biomedical ontologies could define the same biomedical concept in different ways, which yields the biomedical ontology heterogeneous problem. To implement the inter-operability among the biomedical ontologies, it is critical to establish the semantic links between heterogenous biomedical concepts, so-called biomedical ontology matching. Evolution Algorithm (EA) is a state-of-the-art methodology for matching ontologies, but two main shortcomings, i.e. the huge memory consumption and long runtime, make it incapable of effectively matching biomedical ontologies. In this work, a novel Adaptive Compact Differential Evolution algorithm (ACDE) is proposed to solve the biomedical ontology matching problem, which utilizes a compact encoding mechanism to save the memory consumption and introduces the compact adaption schemes on control parameters to improve the algorithm’s converging speed. The experiment exploits four biomedical ontology matching tracks, which are provided by the famous Ontology Alignment Evaluation Initiative (OAEI), to test ACDE’s performance. The experimental results show that ACDE can effectively reduce EA-based ontology matcher’s memory consumption and runtime, and its results significantly outperform other EA-based matchers and OAEI’s participants.

Biomedical ontology formally defines the biomedical entities and their relationships. However, the same biomedical entity in different biomedical ontologies might be defined in diverse contexts, resulting in the problem of biomedicine semantic heterogeneity. It is necessary to determine the mappings between heterogeneous biomedical entities to bridge the semantic gap, which is the so-called biomedical ontology matching. Due to the plentiful semantic meaning and flexible representation of biomedical entities, the biomedical ontology matching problem is still an open challenge in terms of the alignment's quality. To face this challenge, in this work, the biomedical ontology matching problem is deemed as a binary classification problem, and an attention-based bidirectional long short-term memory network (At-BLSTM)-based ontology matching technique is presented to address it, which is able to capture the semantic and contextual feature of biomedical entities. In the experiment, the comparisons with state-of-the-art approaches show the effectiveness of the proposal.

The widespread use of Internet of Things (IoT) technology has promoted location-based service (LBS) applications. Users can enjoy various conveniences brought by LBS by providing location information to LBS. However, it also brings potential privacy threats to location information. Location data that contains private information is often transmitted among IoT networks in LBS, and such privacy information should be protected. In order to solve the problem of location privacy leakage in LBS, a location privacy protection scheme based on k -anonymity is proposed in this paper, in which the Geohash coding model and Voronoi graph are used as grid division principles. We adopt the client-server-to-user (CS2U) model to protect the user’s location data on the client side and the server side, respectively. On the client side, the Geohash algorithm is proposed, which converts the user’s location coordinates into a Geohash code of the corresponding length. On the server side, the Geohash code generated by the user is inserted into the prefix tree, the prefix tree is used to find the nearest neighbors according to the characteristics of the coded similar prefixes, and the Voronoi diagram is used to divide the area units to complete the pruning. Then, using the Geohash coding model and the Voronoi diagram grid division principle, the G-V anonymity algorithm is proposed to find k neighbors in an anonymous area so that the user’s location data meets the k -anonymity requirement in the area unit, thereby achieving anonymity protection of location privacy. Theoretical analysis and experimental results show that our method is effective in terms of privacy and data quality while reducing the time of data anonymity.

Cognitive green computing (CGC) dedicates to study the designing, manufacturing, using and disposing of computers, servers and associated subsystems with minimal environmental damage. These solutions should provide efficient mechanisms for maximizing the efficiency of use of computing resources. Evolutionary algorithm (EA) is a well-known global search algorithm, which has been successfully used to solve various complex optimization problems. However, a run of population-based EA often requires huge memory consumption, which limited their applications in the memory-limited hardware. To overcome this drawback, in this work, we propose a compact EA (CEA) for the sake of CGC, whose compact encoding and evolving mechanism is able to significantly reduce the memory consumption. After that, we use it to address the ternary compound ontology matching problem. Six testing cases that consist of nine ontologies are used to test CEA's performance, and the experimental results show its effectiveness.

To address the heterogeneity problem of sensor data, it is necessary to conduct the Sensor Ontology Matching (SOM) process to find the mappings among diverse sensor data with the same semantics connotation. Currently, many Multi-Objective Evolutionary Algorithms (MOEAs) have been used to match the ontologies, which aim at finding a set of solutions called Pareto Set (PS) in the Pareto Front (PF) to represent a set of trade-off proposals for different Decision Makers (DMs). Being inspired by the success of MOEA with Inverse Model (IM-MOEA) in solving complicated optimization problems, in this work, an Improved IM-MOEA (I-IM-MOEA)-based matching technique is further proposed to enhance the algorithm’s matching efficiency as well as the alignment’s quality. To overcome the drawback of IM-MOEA that has poor performance on irregular PF, an adjusted selection mechanism is employed to avert the massive reduction in non-domination solutions on irregular PF, a dynamic Reference Vectors (RVs) is used to decrease the computational resources and boost the efficiency of the algorithm, and a local search strategy is introduced to promote the results’ quality. The experiment employs the benchmark provided by Ontology Alignment Evaluation Initiative (OAEI) and three sensor ontologies to assess the performance of I-IM-MOEA, and the experimental results show that I-IM-MOEA is both effective and efficient.

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.

Semiautomatic ontology matching poses a new challenge of how to implement an efficient user interactions. To address this challenge, we answer three questions in this paper: (1) when should we activate the interacting process; (2) which correspondences should be presented for user validation; and (3) how to make use of the validating results. In particular, we present an interactive compact memetic algorithm (ICMA) based semiautomatic ontology matching technique to: (1) determine the timing of getting a user involved; (2) determine the problematic correspondences; and (3) propagate the user validating results. The experimental results show that three proposed strategies can effectively reduce the user's workload and the algorithm's runtime, increase the alignment's quality, and the performance of ICMA outperforms the state-of-the-art semiautomatic ontology matching techniques.

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.

Digital Library (DL) is a source of inspiration for the standards and technologies on Semantic Web (SW), which is usually implemented by using bibliographic data. To address DL’s data heterogeneity problem, it is necessary to annotate the bibliographic data with semantic information, which requires the utilization of the bibliographic ontologies. In particular, a bibliographic ontology provides the domain knowledge by specifying the domain concepts and their relationships. However, due to human subjectivity, a concept in different bibliographic ontologies might be defined in different names, causing the data heterogeneity problem. To address this issue, it is necessary to find the mappings between bibliographic ontologies’ concepts, which is the so-called bibliographic ontology matching. In this paper, a Uniform Compact Genetic Algorithm (UCGA) is proposed to match the bibliographic ontologies, which employs the real-valued compact encoding mechanism to improve the algorithm’s performance, the Linearly Decreasing Virtual Population (LDVP) to trade-off the exploration and exploitation of the algorithm, and the local perturbation to enhance the convergence speed and avoid the algorithm’s premature convergence. In addition, by using the Uniform Probability Density Function (UPDF) and Uniform Cumulative Distribution Function (UCDF), the UCGA can greatly reduce the evolutionary time and memory consumption. The experiment uses the Biblio testing cases provided by the Ontology Alignment Evaluation Initiative (OAEI) to evaluate UCGA’s performance and the experimental results show that UCGA is both effective and efficient.

Wireless signal-transmitting process is a complex procedure, to improve the indoor positioning accuracy, and this work proposes a novel indoor positioning technique based on receiving signal’s strength. First, the indoor environment of the building is regionalized in the training phase of indoor positioning. Then, the adjacent points of the indoor space with the same wireless signal transmission characteristics are gathered into the same area, and the corresponding parameter sets and decision domains of each area are constructed. After that, during the positioning stage, the regional confidence and receiving signal’s strength are used to predict the indoor area where the mobile station is located. Finally, the ranging and solution results of the traditional three-sided positioning process are constrained to obtain the optimal solution. Comparing with the traditional positioning techniques that regard the entire complex indoor environment as an entirety, the proposed indoor space regionalization preprocessing method can effectively reduce the ranging error. Compared with the indiscriminate data fusion of the centroid method, the data filtering method based on regional confidence is more targeted. In the experiment, a practical office area is used to test our proposal’s performance, and the experimental results show that our approach can effectively improve the accuracy of indoor positioning results.

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 translational discoveries. Since biomedical ontologies often own large-scale concepts with rich semantic meanings, it is difficult to find a perfect alignment that could meet all DM’s requirements, and usually, the matching process needs to trade-off two conflict objectives, i.e., the alignment’s recall and precision. To this end, in this work, the biomedical ontology matching problem is first defined as a MOP, and then a compact multiobjective particle swarm optimization algorithm driven by knee solution (CMPSO-K) is proposed to address it. In particular, a compact evolutionary mechanism is proposed to efficiently optimize the alignment’s quality, and a max-min approach is used to determine the PF’s knee solution. In the experiment, three biomedical tracks provided by Ontology Alignment Evaluation Initiative (OAEI) are used to test CMPSO-K’s performance. The comparisons with OAEI’s participants and PSO-based matching technique show that CMPSO-K is both effective and efficient.

Biomedical ontologies have gained particular relevance in the life science domain due to its prominent role in representing knowledge in this domain. However, the existing biomedical ontologies could define the same biomedical concept in different ways, which yields the biomedical ontology heterogeneous problem. To implement the inter-operability among the biomedical ontologies, it is critical to establish the semantic links between heterogenous biomedical concepts, so-called biomedical ontology matching. Since modeling the ontology matching problem is a complex and time-consuming task, swarm intelligent algorithm (SIA) becomes a state-of-the-art methodology for solving this problem. However, when addressing the biomedical ontology matching problem, the existing SIA-based matchers tend to be inefficient due to biomedical ontology’s large-scale concepts and complex semantic relationships. In this work, we propose a compact firefly algorithm (CFA), where the explicit representation of the population is replaced by a probability distribution and two compact movement operators are presented to save the memory consumption and runtime of the population-based SIAs. We exploit the anatomy track, disease and phenotype track and biodiversity and ecology track from the ontology alignment evaluation initiative (OAEI) to test CFA-based matcher’s performance. The experimental results show that CFA can improve the FA-based matcher’s memory consumption and runtime by, respectively, 68.92% and 38.97% on average, and its results significantly outperform other SIA-based matchers and OAEI’s participants.

An ontology can formally present the domain knowledge by specifying the domain concepts and their relationships, which is a kernel technique for addressing the data heterogeneity issue in the semantic web. However, since existing ontologies are developed and maintained independently by different communities, a concept and its relationship with the others could be described in different ways, yielding the ontology heterogeneity problem. To solve this problem, in this work, we formally construct an optimal model for it, and propose a similarity measure for distinguishing identical ontology entities. Since determining the high-quality ontology alignment is a complex process, we propose to utilize a Brain Storm Optimization algorithm (BSO) to optimize the alignment. BSO is a recently developed Swarm Intelligence algorithm (SI), which can effectively solve the complex optimization problem by imitating the human's idea generating process. However, classic BSO needs to cluster various ideas in each generation and carry out the evolving operators on all ideas, which increases the computational complexity. To improve the efficiency of BSO-based ontology matcher, a Compact BSO (CBSO) is further proposed, which can reduce the memory consumption by utilizing the probabilistic representation on the idea cluster, and improve the algorithm's speed through the compact crossover operator and perturbation operator. The experiment uses the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our approach's performance. The comparisons among the state-of-the-art ontology matchers and our proposal show that CBSO-based ontology matcher can efficiently determine high-quality ontology alignments.

Although biomedical ontologies have been widely used in the life science domain, the heterogeneous problem among biomedical ontologies hampers their inter-operability. Thus, the establishment of meaningful links between heterogeneous biomedical ontologies, so-called biomedical ontology matching, is critical to the success of biomedical ontology engineering. To determine the biomedical ontology alignment with high quality, in this work, a Hybrid Compact Differential Evolution (HCDE) algorithm-based biomedical ontology matching technique is proposed. In particular, we propose a similarity metric on biomedical concepts, construct an optimal model for the biomedical ontology matching problem, and introduce a binomial crossover into CDE's evolving the process to enhance its performance. The experiments are carried out on the Disease and Phenotype track and Biodiversity and Ecology track from the Ontology Alignment Evaluation Initiative (OAEI 2018). The experimental results show that HCDE can significantly improve the CDE in terms of the alignment's quality, and the alignments obtained by HCDE are also better than OAEI 2018's participants.

To implement the semantic interoperability among intelligent sensor applications, it is necessary to match the identical entities across the sensor ontologies. Since sensor ontology matching problem requires matching thousands of sensor concepts and has many local optimal solutions, Evolutionary Algorithm (EA) becomes the state-of-the-art methodology for solving it. However, the premature convergence and long runtime are two drawbacks which make EA-based sensor ontology matchers incapable of effectively searching the optimal solution for sensor ontology matching problem. To improve the efficiency of EA-based sensor ontology matching technique, in this paper, a new optimal model of sensor ontology matching problem is first constructed, a novel sensor concept similarity measure is then presented to determine the identical sensor concepts, and finally, a problem-specific Compact Evolutionary Tabu Search algorithm (CETS) is presented to efficiently determine the sensor ontology alignment. In particular, CETS combines Compact Evolutionary Algorithm (global search) and Tabu Search algorithm (local search), and this marriage between global search and local search allows keeping high solution diversity via PV (reducing the possibility of the premature convergence) and increasing the convergence speed via the local search (reducing the runtime). The experimental results show that comparing with the state-of-the-art sensor ontology matching techniques, CETS can more efficiently determine the high-quality alignments.

To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping's preference on the similarity measures significantly reduces the alignment's quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of the authors' approach.

Web of Things (WoT) is capable of promoting the knowledge discovery and address interoperability problems of various Internet of Things (IoT) applications. To integrate the semantic information on WoT, Semantic Sensor Networks (SSN) based knowledge engineering is utilized for the uniform representation of identical knowledge by using the sensor ontology. However, it is arduous to link numerous heterogeneous sensor entities, and the Sensor Ontology Matching (SOM) is a newly emerging technique for solving the ontology heterogeneity problem, which aims at finding the semantically identical sensor entities in two ontologies. In this paper, the problem of SOM is deemed as regression problem that to integrate a number of Entity Similarity Measure (EMS) to estimate the real similarity score between two sensor entities. To address it, we propose a Artificial Neural Network (ANN)-based sensor ontology matching technique (ANN-OM), which employs the representative entities for enhancing the quality of alignment and the matching efficiency. The experimental results illustrate that ANN-OM is capable of determining superior alignment which is better than the state-of-the-art ontology matchers.

Ontology, the kernel technique of Semantic Web (SW), formally names the domain concepts and their relationships. However, as the ontologies are created and developed by different domain experts and communities, a concept may be named in various ways, bringing about the concept heterogeneity problem. To solve the problem, in this paper, a Siamese Neural Network (SNN)-based Ontology Matching Technique (OMT) is proposed, which is able to improve the matching efficiency by using a part of Reference Alignment (RA) to decrease the training time and improve the quality of matching results by using a logic reasoning approach to remove the conflict correspondences. The experimental results demonstrate that SNN-based OMT can determine high-quality alignment which outperforms the state-of-the-art OMTs.

Ontology matching can solve the heterogeneity problem between two ontologies, and EA represents a state-of-the-art technique for matching ontologies. However, there are two defects concerning the EA-based ontology matching technique: (1) a reference alignment between two ontologies to be matched is required in advance; (2) the confidence of entity similarity measure is low computational complexity of measuring the similarity value is high. To overcome these drawbacks, in this paper, an Evolutionary Algorithm with Context-based Reasoning method (EA-CR) is proposed, where: (1) an approximate metric without the reference alignment is utilized for evaluating the alignment’s quality; (2) a Context-based Reasoning method is presented to distinguish the heterogeneous entities. The experimental results show that the proposed approach is effective.

Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automatic ontology matching techniques suffers from the limitation of similarity measure, whose alignment’s quality can not be ensured. To overcome this drawback, in this work, an Evolutionary Ontology Matching technique with User Involvement (EOM-UI) is proposed, which utilizes both the Compact Evolutionary Algorithm and user knowledge to improve the algorithm’s performance and the alignment’s quality. In addition, an optimization model is established to formally define the ontology entity matching problem, and an efficient interacting strategy is proposed to reduce the user’s workload and maximize his working value. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test our proposal’s performance. The experimental results show that our approach is able to make use of the user knowledge to improve the alignment’s quality, and it also outperforms OAEI’s participants.

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.

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.

The development of intelligent transportation systems (ITSs) faces the challenge of integrating data from multiple unrelated sources. As one of the core technologies of knowledge integration in ITS, an ontology typically provides a normative definition of transportation domain that can be used as a reference for information integration. However, due to the subjectivity of domain experts, a concept may be expressed in multiple ways, yielding the ontology heterogeneity problem. Ontology matching (OM) is an effective method of addressing it, which is of help to further realize the mutual communication between the ontology-based ITSs. In this work, we first propose to use Word2Vec to model the entities in vector space and calculate their similarity values. Then, a stable marriage-based alignment extraction algorithm is presented to determine high-quality alignment. In the experiment, the performance of the proposal is tested by using the benchmark track of OAEI and real transportation ontologies. The experimental results show that our approach is able to obtain higher quality alignment results than OAEI’s participants and other state-of-the-art ontology matching techniques.

For years, the study on human-computer interaction in the mixed reality has been exploring in more natural and efficient interacting ways. As a common interacting component, the traditional menu on tangible user interface is not well suited to virtual interactive scenes in mixed reality and virtual menu represents a new category of efficient interacting methods in mixed reality. Recently, the combination of gesture recognition with the virtual menu has attracted little attention from researchers. To implement a natural, intuitive, and efficient interaction, in this work, a virtual menu using gesture recognition is proposed for the 3D object manipulation in mixed reality. In particular, several gesture states with respect to the different stages of menu interaction are first defined; then, the gestures on the screen are mapped to 3D virtual objects through coordinate transformation; and finally, the style and interaction logic of the menu is introduced to. In the experiment, the user’s interaction results using different menus are evaluated by comparing the time needed and the result’s accuracy with the same tasks. The experimental results show that our proposal can effectively reduce the number of interacting operations, and improve the efficiency of interacting process.

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.

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.

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.

The ontology matching technique aims at establishing the correspondence between two ontologies’ entities to solve their heterogeneity problem. In the matching process, the quality of the single matcher’s alignment can not be ensured. Usually, multiple matches are required to work together to increase the evidence of potential matches or mismatches. Selecting, combining, and adjusting these ontology matches to obtain high-quality ontology comparisons is one of the main challenges in ontology matching. This paper proposes an Argumentation Framework (A.F.) based on the ontology extracting technique to address this challenge. First, the proposed approach obtains the alignments obtained by different ontology matches. It extracts the final alignment by debating the disputed entity correspondences with A.F. The experiment is conducted on the Bibliographic track provided by Ontology Alignment Evaluation Initiative (OAEI), and the statistical comparison with the state-of-the-art ontology matching techniques shows the effectiveness of the proposed approach.

Brain Storm Optimization (BSO) is a relatively new swarm intelligence algorithm. It employs clustering, creating, and selecting operators, all connected and significantly impacting the optimization performance. However, with the increase of the problem dimension and the offset of the optimal value, the performance of the basic BSO algorithm becomes worse. This paper designs the double grouping operator, which employs k-means clustering in the horizontal and differential grouping in the vertical. A new creating operator is designed based on maximum likelihood estimation for the mean and variance of the Gaussian distribution. The numerical experiments are carried out to amplify and highlight the performance of the proposed algorithm artificially. Experimental results show that the proposed algorithm achieves satisfactory results on shifted and rotated benchmark functions.

In order to strengthen the accuracy of the recognition of abnormal data in the local area network common channel and improve the recognition efficiency, the convolutional network method is applied to data recognition, and the recognition method of the abnormal data in the local area network common channel based on the convolutional neural network is studied. First, the convolutional neural network structure is analyzed and the three main parts of the convolutional neural network are outlined. Second, the optimization algorithm flow of the abnormal data recognition of the common channel of the local area network is designed, and the specific calculation steps of the data recognition are obtained. Finally, a comparative experiment is designed to compare the traditional recognition method of abnormal data based on data stream and the recognition method of abnormal data based on log analysis with the recognition method of abnormal data in local area network common channel based on convolutional neural network designed in this paper. In this way, the loss value and recognition accuracy data are obtained, and the optimal abnormal data recognition method is judged through the data.

Semantic Web (SW) has attracted the increasing attention of researchers, which facilitates people to link and handle various data. Ontology is the kernel technique of SW, and biomedical ontology is a state-of-art biomedical knowledge modeling technique, which formally defines the biomedical concepts and their relationships. However, the same biomedical concepts in different biomedical ontologies could be defined in various contexts or with different terms, which yields the biomedical ontology heterogeneity problem. It is crucial to find mapping among heterogeneity concepts of different biomedical ontologies for bridging the semantic gaps, which is the so-called biomedical ontology matching. Biomedical ontology matching problem is an open challenge due to the rich semantic meaning and the flexible representation on a biomedical concept. To address this challenging problem, in this work, it is regarded as a binary classification problem, and a Long Short-Term Memory Networks (LSTM)-based ontology matching technique is proposed to solve it. Our proposal improves the quality of the alignment by introducing the char-embedding technique, which takes into account the semantic and context information of concepts. The comparing results with OAEI’s participants show the effectiveness of our proposal.

A biomedical ontology provides a formal definition on the concepts and their relationships in the biomedical domain, which supports applications such as biomedical data annotation, knowledge integration, search and analysis. Different biomedical ontologies are mostly developed independently, and thus, establishing meaningful links between their entities, so-called ontology matching, is critical to implement their inter-operation. Since biomedical research usually spans multiple domains and topics, which motivates a new type of complex ontology matching, i.e. compound ontology matching, which involves more than two ontologies. Due to the complexity of the ontology matching problem, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. However, there exist different aspects of a solution that are partially or wholly in conflict, and the single-objective EA may lead to unwanted bias to one of them and reduce the solution's quality. To improve the ternary compound alignment's quality when matching three biomedical ontologies, in this work, a compact Multi-Objective Evolutionary Algorithm Based On Adaptive Objective Space Decomposition (cMOEA-AOSD) based matching technique is proposed. In particular, a Ternary Compound Concept Similarity Measure (TCCSM) is proposed to calculate the similarity value of three biomedical concepts, a mathematical model for ternary compound matching problem is constructed, and a cMOEA-AOSD is presented to address it, which is able to adaptively decompose the objective space to ensure the diversity of the solutions in Pareto Front (PF) and the quality of the final solution. The experiment uses six testing cases that consists of nine biomedical ontologies to test our proposal's performance, and the experimental results show that cMOEA-AOSD significantly out performs other MOEA-based matching technique and the state-of-the-art ternary compound matching techniques.

Recently, with the growing number of ontologies defined in different languages, to bridge the semantic gaps between them, it is necessary to identify the correspondences between their heterogeneous entities, so-called cross-lingual ontology matching. Due to the complexity and the intricacy of the cross-lingual ontology matching, it is essential to get an expert involved in the matching process to guarantee the alignment's quality. In this paper, we propose an interactive cross-lingual ontology matching technique that makes the user and automatic matcher work together to create high-quality alignments in a reasonable amount of time. In particular, we present a cross-lingual similarity metric to calculate the similarity value of two cross-lingual entities, construct an optimal model for the cross-lingual ontology matching problem, and propose an interactive compact differential evolution (ICDE) algorithm to effectively match the cross-lingual ontologies. The experiment exploits the ontology alignment evaluation initiative (OAEI) multifarm track to test our proposal's performance. The experimental results show that the ICDE significantly outperforms other EA-based matchers and OAEI's participants, and the interacting mechanism can significantly improve the alignment's quality.

Process models can be created for different purposes or in different contexts, and the need for the comparisons of process models foster research on process model matching, which refers to the process of determining the correspondences between semantically identical activities of different process models. Despite the growing number of process model matchers, the process model matching contests reveal that the effectiveness of state-of-the-art process model matchers is still low, i.e., the obtained process model alignment contains many irrelevant correspondences. Thus, how to improve the quality of process model alignment becomes one of the main challenges in the process model matching domain. Being inspired by the success of memetic algorithm (MA) in the alongside related domain such as schema matching and ontology matching, in this work, a compact memetic algorithm-based process model matching technique (CMA-PMM) is proposed to efficiently determine the process model alignment. In particular, a new activity similarity measure is used to determine the similar activities, a optimal model for process model matching problem is constructed, and a CMA is presented to efficiently solve the process model matching problem. CMA can simulate the behavior of population-based MA by employing the probabilistic representation of the population, thus comparing with MA, CMA is able to significantly save the memory consumption without sacrificing the solution’s quality. In the experiment, three tracks provided by process model matching contest (PMMC), i.e., university admission processes (UA), birth registration processes and asset management, are utilized to evaluate the performance of CMA-PMM. Comparisons among evolutionary computation-based matchers, PMMC’s participants and CMA-PMM show the effectiveness of our proposal.

The technique that enables the user and the automatic ontology matching tool to cooperate with each other to generate high-quality alignments in a reasonable amount of time is referred to as the interactive ontology matching. Interactive ontology matching poses a new challenge in a way of how to efficiently leverage user validation to improve the ontology alignment. To address this challenge, this paper presents an innovative interactive ontology matching technique based on Partial Reference Alignment (PRA) to better balance between the large workload posed on users and the demand of improving the quality of ontology alignment. In particular, a PRA-based Interactive Compact Hybrid Evolutionary Algorithm (ICHEA) is proposed to reduce user workload, by adaptively determining the timing of involving users, showing them the most problematic mappings, and helping them to deal with multiple conflicting mappings simultaneously. Meanwhile, it increases the value of user involvement by propagating the confidences of validated mappings, as well as reducing the negative effects brought by the erroneous user validations. The well-known OAEI 2016's benchmark track and interactive track are utilized to test the performance of this approach. The experimental results on benchmark track show that both the f-measure and the f-measure per second of this approach outperform those of the OAEI participants and three state-of-the-art Evolutionary Algorithm (EA) based ontology matching techniques. In addition, the experimental results of three interactive testing cases further show that ICHEA can efficiently determine high-quality ontology alignments under different cases of user error rates, and the performance of the approach is generally better than that of state-of-the-art interactive ontology matching systems.

The canonical brain storm optimization (BSO) employs clustering, creating and selecting operators, which are all connected and have great impacts on the optimization performance. In this paper, the state-of-the-art search strategies are introduced and the potential strengths and weaknesses of these strategies in the BSO algorithm are analyzed and compared from the perspective of the analytical model rather than any metaphors. The numerical experiments are carried out to artificially amplify and highlight the performance of various strategies. Finally, the progressive directions of the BSO algorithm are discussed for further research.

This article describes how with the advent of sensors for collecting environmental data, many sensor ontologies have been developed. However, the heterogeneity of sensor ontologies blocks semantic interoperability between them and limits their applications. Ontology matching is an effective technique to solve the problem of sensor ontology heterogeneity. To improve the quality of sensor ontology alignment, the authors propose a semiautomatic ontology matching technique based on a preference-based multi-objective evolutionary algorithm (PMOEA), which can utilize the user's knowledge of the solution's quality to direct MOEA to effectively match the heterogeneous sensor ontologies. The authors specifically construct a new multi-objective optimal model for the sensor ontology matching problem, propose a user preference-based t-dominance rule, and design a PMOEA to solve the sensor ontology matching problem. The experimental results show that their approach can significantly improve the sensor ontology alignment's quality under different heterogeneous situations.

Ontology matching is an effective technique to solve the ontology heterogeneous problem in Semantic Web. Since different ontology matchers do not necessarily find the same correct correspondences, usually several competing matchers are applied to the same pair of entities in order to increase evidence towards a potential match or mismatch. How to select, combine and tune various ontology matchers to obtain the high quality ontology alignment is one of the main challenges in ontology matching domain. Recently, Evolutionary Algorithms (EA) has become the most suitable methodology to face this challenge, however, the huge memory consumption, slow convergence and premature convergence limit its application and reduce the solution’s quality. To this end, in this paper, we propose a Hybrid Population-based Incremental Learning algorithm (HPBIL) to automatically select, combine and tune different ontology matchers, which can overcome three drawbacks of EA based ontology matching techniques and improve the ontology alignment’s quality. In one hand, HPBIL makes use of a probabilistic representation of the population to perform the optimization process, which can significantly reduce EA’s the memory consumption and the possibility of the premature convergence. In the other, we introduce the local search strategy into PBIL’s evolving process to trade off its exploration and exploitation, and this marriage between global search and local search is helpful to reduce the runtime. In the experiment, we utilize different scale testing cases provided by the Ontology Alignment Evaluation Initiative (OAEI 2016) to test HPBIL’s performance, and the experimental results show that HPBIL’s results significantly outperform other EA based ontology matching techniques and top-performers of the OAEI competitions.

Ontology is the kernel technology of semantic web, which plays a prominent role for achieving inter-operability across heterogeneous systems and applications by formally describing the semantics of data that characterize a particular application domain. However, different ontology engineers might have potentially opposing world views which could yield the different descriptions on the same ontology entity, raising so called ontology heterogeneous problem. Ontology matching, which aims at identifying the correspondences between the entities of heterogeneous ontologies, is recognized as an effective technology to solve the ontology heterogeneous problem. Due to the complexity of ontology matching process, ontology alignments generated by the automatic ontology matchers should be validated by the users to ensure their qualities, and the technology that makes multiple users collaborate with each other to help the automatic tool create high quality matchings in a reasonable amount of time is called collaborative ontology matching. Such a collaborative ontology matching poses a new challenge of how to reduce users’ workload, but at the same time, increase their involvement’s value. To address this challenge, in this paper, we propose a Compact Interactive Memetic Algorithm (CIMA) based collaborative ontology matching technology, which can reduce users’ workload by adaptively determining the time of getting users involved, presenting the most problematic correspondences for users and helping users to automatic validate multiple conflict mappings, and increase user involvement’s value by propagating the collaborative validation and decreasing the negative effect brought by the error user validations. The experimental results show that our proposal is able to efficiently exploit the collaborative validation to improve its non-interactive version, and the runtime and alignment quality of our approach both outperform state-of-the-art interactive ontology matching systems under different user error rate cases.

With the proliferation of sensors, semantic web technologies are becoming closely related to sensor network. The linking of elements from semantic web technologies with sensor networks is called semantic sensor web whose main feature is the use of sensor ontologies. However, due to the subjectivity of different sensor ontology designer, different sensor ontologies may define the same entities with different names or in different ways, raising so-called sensor ontology heterogeneity problem. There are many application scenarios where solving the problem of semantic heterogeneity may have a big impact, and it is urgent to provide techniques to enable the processing, interpretation and sharing of data from sensor web whose information is organized into different ontological schemes. Although sensor ontology heterogeneity problem can be effectively solved by Evolutionary Algorithm (EA)-based ontology meta-matching technologies, the drawbacks of traditional EA, such as premature convergence and long runtime, seriously hamper them from being applied in the practical dynamic applications. To solve this problem, we propose a novel Compact Co-Evolutionary Algorithm (CCEA) to improve the ontology alignment’s quality and reduce the runtime consumption. In particular, CCEA works with one better probability vector (PV) $$PV_{better}$$ and one worse PV $$PV_{worse}$$ , where $$PV_{better}$$ mainly focuses on the exploitation which dedicates to increase the speed of the convergence and $$PV_{worse}$$ pays more attention to the exploration which aims at preventing the premature convergence. In the experiment, we use Ontology Alignment Evaluation Initiative (OAEI) test cases and two pairs of real sensor ontologies to test the performance of our approach. The experimental results show that CCEA-based ontology matching approach is both effective and efficient when matching ontologies with various scales and under different heterogeneous situations, and compared with the state-of-the-art sensor ontology matching systems, CCEA-based ontology matching approach can significantly improve the ontology alignment’s quality.

There is evidence from Ontology Alignment Evaluation Initiative (OAEI) that ontology matchers do not necessarily find the same correct correspondences. Therefore, usually several competing matchers are applied to the same pair of entities in order to increase evidence towards a potential match or mismatch. How to select the proper matcher's alignments and efficiently tune them becomes one of the challenges in ontology matching domain. To this end, in this paper, we propose to use the Dynamic Alignment Candidates Selection Strategy and Metamodel to raise the efficiency of the process of using NSGA-II to optimize the ontology alignment by prescreening the less promising aligning results to be combined and individuals to be evaluated in the NSGA-II, respectively. The experiment results show that, comparing with the approach by using NSGA-II solely, the utilization of Dynamic Alignment Candidates Selection Strategy and Metamodel is able to highly reduce the time and main memory consumption of the tuning process while at the same time ensures the correctness and completeness of the alignments. Moreover, our proposal is also more efficient than the state-of-the-art ontology aligning systems.

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.

Literature Based Discovery (LBD) aims at building bridges between existing literatures and discovering new knowledge from them. Biomedical ontology is such a literature that provides an explicit specification on biomedical knowledge, i.e., the formal specification of the biomedical concepts and data, and the relationships between them. However, since biomedical ontologies are developed and maintained by different communities, the same biomedical information or knowledge could be defined with different terminologies or in different context, which makes the integration of them becomes a challenging problem. Biomedical ontology matching can determine the semantically identical biomedical concepts in different biomedical ontologies, which is regarded as an effective methodology to bridge the semantic gap between two biomedical ontologies. Currently, Evolutionary Algorithm (EA) is emerging as a good methodology for optimizing the ontology alignment. However, EA requires huge memory consumption and long runtime, which make EA-based matcher unable to efficiently match biomedical ontologies. To overcome these problems, in this paper, we define a discrete optimal model for biomedical ontology matching problem, and utilize a compact version of Evolutionary Algorithm (CEA) to solve it. In particular, CEA makes use of a Probability Vector (PV) to represent the population to save the memory consumption, and introduces a local search strategy to improve the algorithm’s search performance. The experiment exploits Anatomy track, Large Biomed track and Disease and Phenotype track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance. The experimental results show that CEA-based approach can effectively reduce the runtime and memory consumption of EA-based matcher, and determine high-quality biomedical ontology alignments.

Since different sensor ontologies are developed independently and for different requirements, a concept in one sensor ontology could be described with different terminologies or in different context in another sensor ontology, which leads to the ontology heterogeneity problem. To bridge the semantic gap between the sensor ontologies, authors propose a semi-automatic sensor ontology matching technique based on an Interactive MOEA (IMOEA), which can utilize the user's knowledge to direct MOEA's search direction. In particular, authors construct a new multi-objective optimal model for the sensor ontology matching problem, and design an IMOEA with t-dominance rule to solve the sensor ontology matching problem. In experiments, the benchmark track and anatomy track from the Ontology Alignment Evaluation Initiative (OAEI) and two pairs of real sensor ontologies are used to test performance of the authors' proposal. The experimental results show the effectiveness of the approach.

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., ontology metamatching problem, is an open challenge. This paper proposes a novel ontology metamatching approach to improve the sensor ontology alignment’s quality, which utilizes the heterogeneity features on two ontologies to tune the aggregating weight set. In particular, three ontology heterogeneity measures are firstly proposed to, respectively, evaluate the heterogeneity values in terms of syntax, linguistics, and structure, and then, a semiautomatically learning approach is presented to construct the conversion functions that map any two ontologies’ heterogeneity values to the weights for aggregating the similarity measures. To the best of our knowledge, this is the first time that heterogeneity features are proposed and used to solve the sensor ontology metamatching problem. The effectiveness of the proposal is verified by comparing with using state-of-the-art ontology matching techniques on Ontology Alignment Evaluation Initiative (OAEI)’s testing cases and two pairs of real sensor ontologies.

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.

With the explosive growth in generating data in the hydrographical domain, many hydrography ontologies have been developed and maintained to describe hydrographical features and the relationships between them. However, the existing hydrography ontologies are developed with varying project perspectives and objectives, which inevitably results in the differences in terms of knowledge representation. Determining various relationships between two entities in different ontologies offers the opportunity to link hydrographical data for multiple purposes, though the research on this topic is in its infancy. Different from the traditional ontology alignment whose cardinality is 1:1, i.e. one source ontology entity is mapping with one target ontology entity and vice versa, and the relationship is the equivalence, matching hydrography ontologies is a more complex task, whose cardinality could be 1:1, 1:n or m:n and the relationships could be equivalence or subsumption. To efficiently optimize the ontology alignment, in this paper, a discrete optimal model is first constructed for the ontology matching problem, and then a Compact Particle Swarm Optimization algorithm (CPSO) based matching technique is proposed to efficiently solve it. CPSO utilizes the compact real-value encoding and decoding mechanism and the objective-decomposing strategy to approximate the PSO’s evolving process, which can dramatically reduce PSO’s memory consumption and runtime while at the same time ensure the solution’s quality. The experiment exploits the Hydrography dataset in Complex track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance. The experimental results show that CPSO-based approach can effectively reduce PSO’s runtime and memory consumption, and determine high-quality hydrography ontology alignments.