The rapid proliferation of Internet of Medical Things (IoMT) devices has created unprecedented demands for reliable, low-latency, and high-bandwidth communication networks in healthcare environments. Traditional networking approaches struggle to meet the diverse quality of service requirements of medical applications, ranging from real-time patient monitoring to large-scale medical imaging transfers. Intent-based networking (IBN) emerges as a promising paradigm that translates high-level healthcare network intentions into automated network policies, enabling self-managing and self-optimizing networks. This paper presents a novel intelligent routing framework for IoMT communications based on IBN principles, utilizing a dueling double deep Q-network (DDQN) approach to optimize network resource allocation and routing decisions. The proposed method addresses the multi-objective optimization problem of simultaneously satisfying bandwidth, latency, and reliability requirements for heterogeneous medical traffic flows. We model the IBN routing problem as a reinforcement learning task and develop a dueling DDQN algorithm that learns optimal routing policies through interaction with a network environment model. Comparative analysis against baseline methods reveals 12-42% improvements in medical intent satisfaction across application categories. The framework exhibits robust convergence and maintains advantages across network scales from clinics to enterprise systems. Sensitivity analysis validates optimal bandwidth discretization, balancing efficiency with clinical requirements. Results confirm practical applicability for IoMT deployments where network reliability impacts patient care.

The integration of space, air, and ground segments into unified Space-Air-Ground Integrated Networks (SAGINs) enables low-latency, ubiquitous, and scalable computing. However, such systems face critical challenges: ground terminals suffer from weak satellite links, UAV-based edge nodes have limited resources, and highly dynamic environments make it difficult to make efficient offloading and resource allocation decisions. Prior approaches often optimize either communication or computation in isolation and lack adaptability to real-time environmental feedback. This paper presents a novel perception-aware hybrid-action deep reinforcement learning (DRL) framework for joint optimization of task offloading, beamforming, and resource allocation in SAGINs. To improve tractability, the original non-convex problem is first decomposed using Block Coordinate Descent (BCD) and approximated with Successive Convex Approximation (SCA), generating a structured feasible action space. A Soft Actor-Critic (SAC) agent then learns policies over this space, informed by real-time UAV perception via mmWave radar and vision sensors that detect user density, link quality, and environmental blockages. The DRL agent operates over a hybrid action space, combining discrete offloading decisions with continuous controls such as beamforming weights, CPU frequency, and transmission power. We employ a constraint-aware action masking mechanism that prunes infeasible hybrid actions violating delay, power, or SNR limits, thereby accelerating learning while respecting SAGIN-specific constraints. Extensive simulations show that the proposed framework significantly outperforms greedy, no-perception DRL, and state-of-the-art DRL offloading algorithms in reducing latency and energy consumption, while improving offloading success and resource stability. These results highlight the effectiveness of combining analytical optimization structure with adaptive perception-driven learning for robust and scalable control in future SAGINs.

With the implementation of 6G networks and IoT devices within the healthcare sector, the collection, processing, and utilization of medical data has changed significantly and, at the same time, generated massive amounts of patient information in disparate spaces. Such technological integrations allow for real-time monitoring, AI-assisted diagnostics, and individualized treatment protocols that were not possible before. However, centralized healthcare data management systems encounter substantial obstacles, including single points of failure, limited patient control over personal information, vulnerability to cyber-attacks, and complex regulatory compliance issues. This paper proposes BTHRiD, a blockchain-enabled, trustworthy healthcare, reliable IoT data-sharing mechanism for 6G networks. BTHRiD introduces a proof-of-storage consensus mechanism combining block validation with decentralized medical data storage and a layered propagation approach for efficient data distribution across healthcare nodes. Through mathematical modeling, we analyze block propagation latency and network decentralization characteristics, deriving optimal operational points for healthcare data sharing. Experimental results show that the proposed BTHRiD outperforms other solutions by 34% in processing latency and 28% in detection accuracy of malicious behavior. BTHRiD’s practical effectiveness is demonstrated in a real-world implementation example of tracking COVID-19 patient trajectories where data consistency was preserved at 96% during recovery phases while achieving a 42% reduction in storage overhead. Additional telemedicine, EHR sharing, and clinical trial application testing confirm the system’s adaptability to diverse healthcare requirements. The proposed mechanism enables secure, efficient, and trustworthy medical data sharing in 6G-IoT healthcare networks while preserving patient privacy and ensuring data reliability across heterogeneous healthcare environments.

This paper introduces a distributed edge intelligence framework for secure and efficient data sharing in 6G-Internet-of-Vehicles (6G-IoV) environments. The approach integrates a hierarchical blockchain architecture with an innovative asynchronous federated learning algorithm to address the challenges of privacy preservation, communication efficiency, and scalability in 6G-IoV scenarios. The proposed framework employs a multi-layer structure comprising vehicles, roadside units, and cloud servers, each playing a distinct role in the distributed learning process. We present a comprehensive latency model that accounts for computation and communication aspects, considering the unique characteristics of vehicular networks. The asynchronous federated learning algorithm incorporates genetic algorithm-based resource optimization to adaptively allocate communication resources, significantly enhancing efficiency in heterogeneous 6G-IoV environments. Furthermore, we introduce the hierarchical edge intelligence consensus mechanism, a lightweight consensus protocol tailored for edge intelligence scenarios, which accelerates blockchain consensus while maintaining security. Simulations using MNIST and SVHN datasets demonstrate that the proposed framework outperforms state-of-the-art methods regarding model accuracy, communication efficiency, and privacy preservation.

The exponential proliferation of social media and Internet of Things (IoT) technologies has paved the way for transformative applications in public health, particularly for the early detection of mental health concerns. This study introduces an innovative framework leveraging cooperative learning algorithms combined with intelligence of social things (IoST) data to enhance mental health issue detection. By integrating multimodal user data from social platforms, wearable devices, and IoT sensors, the proposed approach achieves superior predictive accuracy, with the random forest-based model outperforming benchmarks at 88% accuracy and a 0.90 receiver operating characteristic area under the curve (ROC-AUC). The incorporation of key features, including social homophily and real-time behavioral metrics, significantly bolsters detection rates. Ethical considerations, including data privacy and bias reduction, are meticulously addressed, ensuring a scalable and user-centered solution. The findings underscore the potential of IoST-driven cooperative algorithms to revolutionize mental health interventions by enabling timely, precise, and ethical detection systems.

The heavy use of social media platforms is generating a high volume of affective data over the internet. This data is being used by researchers in various domains for prediction, qualitative, and quantitative analytical problems such as stock market prediction, opinion mining of online reviews on products, events, and many more. This article leverages X data for the political response analysis of users towards the 2019 Indian General election. In this article, a methodology is proposed that analyses X data to know what topics were mostly discussed during the election time under the #LoksabhaElection2019 hashtag. Also, we have tried to find out the sentiments of people towards different political terms (words) in the topics inferred. For this task, the study has used topic modeling and sentiment analysis of Tweets. This research may be useful for political parties or newsgroups to mine main topics and analyze the sentiments of people towards different entities.

Ontology provides a structured language to encapsulate domain-specific knowledge and harmonize diverse data. Ontology matching identifies similar entities in distinct ontologies, facilitating knowledge integration and information exchange. Similarity features are crucial for ontology matching by measuring entity resemblance, but noisy and redundant features can obscure relevant ones, reducing matching quality. To improve the accuracy of matching results, we propose a dual population genetic programming with an active meta-learning to build a high-quality similarity feature, which owns three novel components. First, a dual population genetic programming is developed to construct high-level similarity feature with a two-layer individual representation, a dual population based co-evolutionary mechanism, and a novel fitness function based on partial standard alignment. Second, a new active learning model is presented to update the partial standard alignment through an efficient interactive procedure, guiding the algorithm towards building more reliable similarity features. Finally, a weighted random forest meta-learning model is designed to train the expert vote aggregation model with their historical behaviors, and fine-tunes the model’s performance with a compact genetic algorithm. Experimental results on the Ontology Alignment Evaluation Initiative’s interactive matching tasks demonstrate that our method consistently achieves higher accuracy and better efficiency compared to advanced matching techniques across various expert error rates.

Ontology is a kernel technique of the semantic web, which defines concepts, properties, and their relationships to establish a shared understanding of domain knowledge. Ontology matching identifies semantically similar entities across different ontologies, which uses similarity features to measure their similarity from different perspectives. However, due to the complexity of the entity heterogeneity, no single similarity feature is universally effective. In recent years, genetic algorithms have proven effective in constructing similarity features for ontology matching, but their potential is limited by the reliance on default classification strategies, empirical determination of the number of high-level features, the requirement for manually selecting, combining these features, and tuning the associated combination parameters. To overcome these drawbacks, we propose a multi-layer hybrid genetic programming approach to automatically construct high-level similarity features. This approach includes three novel components. First, a new multi-layer individual representation is designed, which faciliates the algorithm to adaptively explore the search space of constructing high-level similarity features. Second, to enhance the search effectiveness, a new initialization method and a mutation operator are developed, which use a weight-based strategy to adaptively select and construct a more diverse set of similarity features. Third, a compact genetic algorithm-based optimizer is designed to refine the tree structures of elite individuals. The experimental results on the ontology alignment evaluation initiative’s benchmark show that our algorithm can generate high-quality ontology matching results across various matching tasks, significantly outperforming the state-of-the-art ontology matching methods.

Transportation Networks (TNs) play a critical role in economic and social systems, yet the dynamic nature and inherent heterogeneity of TN data pose challenges for Dynamic Knowledge Integration (DKI). Traditional approaches for matching entities from different knowledge bases often struggle with the complexity and diversity of TN data, which varies across systems and sources such as traffic sensors and GPS devices. To address this issue, this paper proposes a novel Multi-Objective Genetic Programming assisted Stochastic Deep Reinforcement Learning (MOGP-SDRL) for DKI in TNs. Unlike existing methods, the proposed framework combines SDRL and MOGP to achieve superior efficiency, accuracy and adaptability in handling heterogeneous TN data. First, a novel SDRL framework is designed to automate and optimize the selection of Similarity Features (SFs) for entity matching. This framework incorporates a probabilistic action selection mechanism, which enhances exploration during the SF selection process. Second, a novel MOGP is presented to construct high-quality, diverse SFs by exploring non-dominated feature ensembles, enhancing both accuracy and adaptability in matching results, leading to more accurate and adaptable matching results compared to conventional methods. Lastly, new approximate evaluation metrics are developed to assess alignment quality without relying on predefined entity alignments, guiding the optimization process. Experimental evaluations on OAEI’s knowledge graph (KG) dataset and five pairs of real-world TN dataset demonstrate the effectiveness of the MOGP-SDRL framework, which consistently produces high-quality matching results and achieves significant improvements in both accuracy and robustness over existing approaches.

The rapid proliferation of Internet of Things (IoT) devices in healthcare has posed considerable hurdles in data processing, energy efficiency, and real-time response demands. This study presents a novel framework for cognitive energy-Efficient healthcare-enabled aerial computing and trajectory optimization (CEHEAT) to solve these difficulties. CEHEAT enhances energy efficiency by merging cognitive IoT (CIoT) capabilities with autonomous aerial vehicles (AAV) aerial computing to address the varied needs of healthcare applications. The system guarantees efficient data collecting and processing using cognitive skills, such as adaptive sensing and dynamic resource allocation while ensuring trustworthy healthcare monitoring. Simulation results demonstrate that CEHEAT significantly outperforms existing approaches across multiple performance metrics. The framework achieves 25%–35% higher energy efficiency, 3x faster convergence, and improved scalability compared to baseline methods. Moreover, CEHEAT’s adaptive sensing and speed control mechanisms contribute to overall energy savings while maintaining effective coverage of the healthcare IoT network. The findings indicate CEHEAT’s ability to improve the efficiency and effectiveness of smart healthcare systems, especially in situations that demand real-time monitoring, faster response as well as large quantities of data processing. This work gives useful guidelines for deploying the AAV-assisted aerial computing system in health facilities that extend the scope of healthcare delivery systems.

The rapid evolution of the Internet of Everything (IoE) has increased data complexity in urban traffic networks, necessitating the use of the Semantic Sensor Web (SSW) to integrate semantic metadata with sensor data via Sensor Knowledge Graphs (SKGs). However, the heterogeneity of SKGs, with varying focus, terminology and structure, poses challenges for accurate sensor data analysis. To identify semantically identical entities across different SKGs, Similarity Features (SFs) capture entity similarity from multiple perspectives, but the multidimensional heterogeneity of SKGs prevents any single SF from being universally effective. To improve SKG alignment, this paper presents a novel two-phase SKG alignment method, which consists of three new components. First, an automated SF construction framework is developed, which uses Multi-Objective GP (MOGP) and Single-Objective GP (SOGP) to automatically construct and combine the high-quality SFs. Second, new fitness functions are designed to guide the search direction of MOGP and SOGP, without relying on standard alignments. Lastly, lexicase crossover and mutation are proposed to adaptively enhance population diversity, ensuring high-quality SKG alignment. Experiment utilizes two KG datasets from the Ontology Alignment Evaluation Initiative (OAEI), along with ten pairs of practical IoE SKGs, were utilized to evaluate the performance of our approach. The results show that our method outperforms state-of-the-art matching methods, particularly in handling complex entity heterogeneity.

Particularly as environmental issues and data privacy become more important, the fast growth of supply chain systems calls for ever more complex demand forecasting methods. Often, conventional techniques find it difficult to compromise these conflicting needs, which results in poor performance in either data security, environmental effect, or prediction accuracy. Artificial intelligence Internet of Things-enabled federated demand forecasting for green supply chains with a privacy-preserving carbon reference effect mechanism (AFED-Green/PCREM) is presented in this study. This new framework combining artificial intelligence and the Internet of Things (AIoT) features with federated learning provides sustainable supply chain demand forecasting. Using a distributed network of AIoT sensors, our method allows merchants to train demand prediction models cooperatively while preserving data privacy and, including environmental consciousness. The system uses a carbon reference effect mechanism that tracks how past emission levels affect consumer behavior and demand trends. Simulation findings show better performance than state-of-the-art baselines, with a 27% increase in prediction accuracy, a 42% drop in carbon emissions, and information leakage below 0.05%.

The rapid growth of social media platforms has led to an unprecedented increase in user-generated content and social interactions, posing significant challenges for recommendation systems. This article addresses the challenges of recommendation in large-scale dynamic social environments, where user interactions and preferences evolve rapidly across vast networks. In large-scale dynamic social networks, knowledge discovery requires methods to efficiently process vast amounts of data while capturing the evolving nature of user interactions and preferences. Multigraph neural networks offer a promising approach for this task, as they can model complex relationships and temporal dynamics in these environments. This article proposes a novel social-aware multigraph neural network for the session-based recommendation (SAMGNN-SR) model that leverages dynamic social information and multigraph neural networks to enhance recommendation accuracy and knowledge discovery in complex social computing environments. The model constructs a global social-aware interaction graph from all user session sequences and employs an adaptive subgraph sampling strategy to extract relevant collaborative signals efficiently. A dynamic interest extraction module utilizing dual-direction information propagation captures users' evolving preferences, while a social information fusion network based on graph attention mechanisms models the dynamic nature of social influences. Experiments on three real-world datasets (Douban, Delicious, and Yelp) demonstrate the superiority of SAMGNN-SR over nine state-of-the-art baselines, with improvements of up to 6.79% in NDCG@20 and 6.19% in Hit@20. Ablation studies validate the effectiveness of each model component in capturing complex social dynamics and session-based user behaviors.

Due to its superiority in addressing label ambiguity, label distribution learning (LDL) has received wide attention from the community, such as image classification, emotion recognition, and big data processing. To efficiently process the data with label distribution, researchers have proposed to learn label-specific features (LSFs) that are the discriminative features for each class label. Although the LDL literature has seen many algorithms to learn LSFs, most of them ignore the characteristics of label distribution. Label distribution lies in real-value vector space with specific characteristics. In this article, we propose to learn label-distribution-specific features (LDSFs) for processing label distribution data by considering the structures of label distribution. We design a novel LDL method called LDL-LDSF to exploit LDSFs by considering the fuzzy cluster structures of label distribution data. First, LDL-LDSF learns LDSFs for the whole label distribution by jointly learning the label distribution and fuzzy C-means clustering. Second, it learns LDSFs for each label in a similar way. Third, it concatenates the learned LDSFs with the original features to deduce an LDL model. Finally, we conduct extensive experiments to justify that LDL-LDSF statistically outperforms several state-of-the-art LDL methods and validate the advantages of LDSFs for processing label distribution data.

Integrating diverse biomedical knowledge information is essential to enhance the accuracy and efficiency of medical diagnoses, facilitate personalized treatment plans, and ultimately improve patient outcomes. However, Biomedical Information Integration (BII) faces significant challenges due to variations in terminology and the complex structure of entity descriptions across different datasets. A critical step in BII is biomedical entity alignment, which involves accurately identifying and matching equivalent entities across diverse datasets to ensure seamless data integration. In recent years, Large Language Model (LLMs), such as Bidirectional Encoder Representations from Transformers (BERTs), have emerged as valuable tools for discerning heterogeneous biomedical data due to their deep contextual embeddings and bidirectionality. However, different LLMs capture various nuances and complexity levels within the biomedical data, and none of them can ensure their effectiveness in all heterogeneous entity matching tasks. To address this issue, we propose a novel Two-Stage LLM construction (TSLLM) framework to adaptively select and combine LLMs for Biomedical Information Integration (BII). First, a Multi-Objective Genetic Programming (MOGP) algorithm is proposed for generating versatile high-level LLMs, and then, a Single-Objective Genetic Algorithm (SOGA) employs a confidence-based strategy is presented to combine the built LLMs, which can further improve the discriminative power of distinguishing heterogeneous entities. The experiment utilizes OAEI's entity matching datasets, i.e., Benchmark and Conference, along with LargeBio, Disease and Phenotype datasets to test the performance of TSLLM. The experimental findings validate the efficiency of TSLLM in adaptively differentiating heterogeneous biomedical entities, which significantly outperforms the leading entity matching techniques.

Accurate driver emotion prediction is critical for enhancing safety and efficiency in 6G-enabled Intelligent Transportation Systems (ITS). In real-world ITS, drivers may face complex situations, and as a results they may express complex emotion. However, traditional driver emotion analysis struggles to capture the complexity of such blended emotions. To address this challenge, we apply emotion distributions to represent complex driver emotions. Besides, we apply the Label Enhancement (LE) to transform logical emotion labels into more informative emotion distributions, which reflect the varying relevance of multiple emotions. We propose in this paper a novel distributed LE method, called Distributed Label Enhancement with Label Manifold (DLEM). It leverages the manifold structure of logical emotions to enhance them into emotion distributions. DLEM is well-suited for edge and large-scale Internet-of-Vehicles (IoV) systems. Additionally, we design the Distributed Emotion Distribution Learning (DEDL) to learn and predict driver emotions from such enhanced emotion distributions. Finally, we conduct experiments on three large-scale emotion datasets. The experimental results show that DLEM significantly outperforms several state-of-the-art approaches and achieves the best performance.

The rapid evolution of transportation cyber-physical systems (T-CPS) has led to unprecedented advancements in mobility and efficiency. However, ensuring the safety and reliability of these complex systems remains a critical challenge, particularly in verifying and refining their temporal behaviors. This paper presents a novel compositional approach for temporal behavior analysis and synthesis in safety-critical T-CPS, the safety-critical temporal analysis, and refinement for the T-CPS (STAR-TCPS) framework, which combines iterative compositional verification with L*-based learning techniques to analyze and refine timing behaviors efficiently. The method leverages the clock constraint specification language for high-level timing specifications and introduces a systematic refinement process to transform abstract requirements into implementable task models. The framework incorporates innovative constraint handling mechanisms tailored for T-CPS, addressing unique challenges such as vehicle-to-infrastructure communication delays and adaptive traffic management timing. Experimental evaluations, including a case study on an intelligent vehicle system, demonstrate STAR-TCPS’s superiority over existing methods. Results show significant improvements in verification time (up to 63% reduction), memory usage (44% decrease), and task generation efficiency (18-25% fewer tasks) compared to state-of-the-art approaches. The STAR-TCPS framework enhances T-CPS’s safety and reliability by enabling more efficient verification and refinement of critical timing properties, paving the way for safer and more robust transportation systems.

Conventional security architectures and models are considered single-network architecture solutions, which assume that devices authenticated within the network are implicitly trusted. However, such an approach is unsuitable for next-generation networks (NGNs). Zero-trust security was introduced to overcome these challenges using context-aware, dynamic, and intelligent authentication schemes. This paper proposes a novel zero-trust blockchain-enabled framework for secure next-generation healthcare communication network (HCN). The proposed framework integrates zero-trust and blockchain to provide a decentralized, secure, and intelligent solution for healthcare communication in NGNs. The system model comprises three components: HCN user identity modeling, blockchain and risk assessment-based access control, and dynamic trust gateway. The user identity modeling component utilizes attribute-based user behavior trajectory features, while the access control component leverages smart contracts-based risk assessment. The dynamic trust gateway component employs a consensus mechanism to achieve dynamic gateway switching and enhance network resilience. Simulation results demonstrate that the proposed framework achieves 31% lower calculation delays, 3% higher trust values, and 3% better attack detection accuracy compared to best baseline methods. It also exhibits a 2% improvement in access control granularity and maintains 95% network throughput under various failure scenarios.

The integration of Artificial Intelligence with the Internet of Things significantly enhances the functionality of vehicle–road cooperation (VRC) systems by enabling smarter, real-time decision-making and resource optimization across interconnected vehicular networks. To tackle the challenges associated with resource constraints, this study introduces a method where vehicle users can offload tasks to nearby roadside units (RSUs) or service-oriented vehicles to ensure timely application execution. However, this task offloading introduces additional transmission delays and energy expenditures. Consequently, this article first conceptualizes the computation offloading problem, aiming to minimize the total task processing time and energy consumption under the constraints of resources provided by RSUs and service-oriented vehicles. We model the computation offloading issue within the VRC framework as a Markov decision process (MDP) and propose a multiagent reinforcement learning-based resource scheduling method. Each vehicle, acting as an intelligent agent, interacts with and influences decisions within this environment. The method integrates the twin delayed deep deterministic policy gradient algorithm to train deep neural networks for deciding on task offloading and computational resource allocation. Simulation results demonstrate that compared to existing algorithms, the proposed method more effectively utilizes the computational resources available through RSUs and service-oriented vehicles within the VRC system. It achieves joint optimization of latency and energy consumption, thus validating the efficacy of the proposed approach in enhancing the operational efficiency and sustainability of urban transportation systems.

Sensor ontology is the kernel technique of the Intelligent Sensor System, which provides a structured framework to organize and interpret the knowledge of the Internet of Things (IoT). However, the ontology heterogeneity issue hampers the communication of sensor ontologies. Sensor Ontology Matching (SOM) can find semantically identical entities between two ontologies, which is an effective method to address this issue. However, due to their complicated semantic relationships, it is a challenge to construct an effective Similarity Feature (SF) to distinguish the heterogeneous sensor entities. Although Evolutionary Algorithms (EAs) based matching techniques have shown their effectiveness in the ontology matching field, they suffer from drawbacks such as high computational complexity and expert-dependent solution evaluation. To overcome these drawbacks, this paper proposes a novel Light Genetic Programming (L-GP) to automatically construct SF for SOM. First, a simplified evolutionary mechanism is designed to improve the efficiency of the SOM process. Second, a novel fitness function based on the approximate evaluation metric is introduced to automatically guide the search direction of L-GP. Lastly, a two-stage tournament selection operator is presented to balance the quality and complexity of the solutions, improving the accuracy of the SOM results. The experiment uses ten pairs of real-world SOM tasks to test the performance of L-GP, and the experimental results show that L-GP significantly outperforms state-of-the-art matching techniques.

Abstract The edge computing in knowledge‐defined network (KDN) is a kind of distributed computing architecture, and the format of edge resources stored in different edge computing nodes are different, which yields the data heterogeneity problem and hampers the interaction between edge nodes. Ontology is considered as the solution of data heterogeneity on Semantic Web, and matching ontologies is a high‐efficiency method of addressing the data heterogeneity problem. Ontology meta‐matching investigates how to determine the optimal weights to aggregate multiple similarity measures to achieve high‐quality ontology alignment, which is a challenge about nonlinear mathematical problem in ontology matching domain. To face this challenge, unsupervised learning method such as generative adversarial network (GAN) becomes an effective methodology. GAN consists of two models of different targets that are opposed to each other in training to produce the final best result. To improve the GAN's efficiency, this work further proposes a GAN with simulated annealing algorithm (SA‐GAN), where the stagnation counter is introduced to accelerate GAN's the convergence speed. The experiment uses the famous benchmark in the ontology domain, and the comparisons with the advanced ontology matching systems shows that SA‐GAN is able to find high‐quality alignments to help build bridges between edge nodes on edge computing.

An ontology is a state-of-the-art knowledge modeling technique in the natural language domain, which has been widely used to overcome the linguistic barriers in Asian and European countries’ intelligent applications. However, due to the different knowledge backgrounds of ontology developers, the entities in the ontologies could be defined in different ways, which hamper the communications among the intelligent applications built on them. How to find the semantic relationships among the entities that are lexicalized in different languages is called the Cross-lingual Ontology Matching problem (COM), which is a challenge problem in the ontology matching domain. To face this challenge, being inspired by the success of the Genetic Algorithm (GA) in the ontology matching domain, this work proposes a Compact GA with Annealing Re-sample Inheritance mechanism (CGA-ARI) to efficiently address the COM problem. In particular, a Cross-lingual Similarity Metric (CSM) is presented to distinguish two cross-lingual entities, a discrete optimal model is built to define the COM problem, and the compact encoding mechanism and the Annealing Re-sample Inheritance mechanism (ARI) are introduced to improve CGA’s searching performance. The experiment uses Multifarm track to test CGA-ARI’s performance, which includes 45 ontology pairs in different languages. The experimental results show that CGA-ARI is able to significantly improve the performance of GA and CGA and determine better alignments than state-of-the-art ontology matching systems.

As an advanced biomedical knowledge modeling technology, biomedical ontology models the biomedical domain. However, since the lack of uniform standards for constructing biomedical ontologies, the biomedical ontologies obtained by different construction methods for the same thing may be different, which is known as the biomedical ontology heterogeneity problem. To solve this problem, we need to execute the biomedical ontology matching process, where it is important to integrate different similarity measures to improve the quality of alignment. Evolutionary Algorithm (EA) is an effective algorithm to address the biomedical ontology meta-matching problem. However, the classical EA-based biomedical ontology meta-matching technique needs to traverse reference alignment to evaluate the individuals, which makes the algorithm have high running time. To overcome this drawback, we propose an Interpolation Model (IM) based Hybrid EA (IM-HEA), which combines EA with a problem-specific IM to evaluate the individual and execute the local search process. In particular, we use lattice design to divide the feasible domain into several uniform sub-regions, and on this basis, approximately evaluate the fitness of newly generated individual. In addition, to avoid the algorithm falling into the local optimum, we further introduce an IM-based local search process into EA’s evolving process. In the experiment, we test IM-HEA on OAEI’s Benchmark and Anatomy and compared them with classical EA in terms of alignment’s quality and running time. The experimental results show that IM-HEA greatly enhances the efficiency of EA with little sacrifice on the alignment’s quality.

An ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology matching is a ground method to address this problem. Ontology matching technique uses the similarity measure to determine the correspondences between two heterogeneous ontology entities. In order to improve the quality of ontology alignment, it is necessary to combine different kinds of similarity measures, and how to optimize the aggregating weights is called the ontology meta-matching problem. Tin this work, a heuristic evaluating metric on the ontology alignment is presented to evaluate the ontology alignment's quality, and a mathematical model on ontology meta-matching problem is constructed. Then, an Adaptive Evolutionary Algorithm (AEA) is proposed to effectively solve this problem. In particular, when the elite solution remains unchanged, AEA adaptively activates three independent exploring strategies, which, respectively use the adaptive selection, crossover and mutation operators based on the population diversity metric. In the experiment, we compare AEA among EA based matching technique and the state-of-the-art ontology matching technique, and the experimental results show its effectiveness.

Abstract With the advances of deep learning research in the past few years, healthcare and smart medicines have been significantly developed. Inspired by the wide application of deep learning in medical image classification and disease diagnosis, this paper further proposes a variant of the Residual Network framework to classify the oral ulcer images in real‐time. In particular, image pre‐processing and enhancement techniques are used to enrich the datasets and reduce model overfitting. Besides, the transfer learning is further introduced into the residual blocks to improve the classification accuracy, with the later layers trained from the labeled datasets. To validate the performance of authors' proposal, it is compared with other classic deep learning models with respect to the classification sensitivity, specificity, and accuracy. The experimental results show that authors' approach outperforms those classic classification networks when the oral ulcers are classified and diagnosed in real‐time.

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 suffer from the limitation of similarity measure, whose alignment’s quality cannot be ensured. To overcome this drawback, in this work, an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique is proposed, which utilizes both the compact encoding mechanism and expert interacting mechanism 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, which is able to reduce the expert’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 expert knowledge to improve the alignment’s quality, and it also outperforms OAEI’s participants.

With the widespread adoption of sensors, many research efforts in recent years have focused on the wireless sensor networks. Since a wireless sensor network consists of a large number of sensors, its trusted communication become a hot research topic. Sensor ontology matching technology is able to solve the sensor information heterogeneity problem, which ensures the communication quality among different wireless sensor networks. In the matching process, different classes of similarity measures have different contributions in matching sensor ontologies. How to determine the optimal weights to aggregate multiple similarity measures to obtain high quality ontology alignment becomes a challenge in sensor ontology matching domain. To face this challenge, this work proposes a neural network-based sensor ontology matching technique. In particular, a single layer perceptron is used to aggregate multiple similarity measures and the neural model is trained with different training examples to obtain higher ontology matching accuracy. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matchers under different domain knowledge such as bibliographic and real sensor ontologies.

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.

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.

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.

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.

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.

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.

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.

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.

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

The Linked Open Data (LOD) community effort is a cornerstone in the realization of the Semantic Web vision [1]. However, since an instance in the LOD is likely to be denoted with many identifiers (e.g., URIs) by different parties, instance coreference resolution, which identifies different identifiers for the same instance and eliminates the inconsistency between the datasets, has become critical to the development of LOD.

The Suffix Array (SA) is a fundamental data structure which is widely used in the applications such as string matching, text index and computation biology, etc. How to sort the suffixes of a string in lexicographical order is a primary problem in constructing SAs, and one of the widely used suffix sorting algorithms is qsufsort. However, qsufsort suffers one critical limitation that the order of suffixes starting with the same [Formula: see text] characters cannot be determined in the kth round. To this point, in our paper, an efficient suffix sorting algorithm called dsufsort is proposed by overcoming the drawback of the qsufsort algorithm. In particular, our proposal maintains the depth of each unsorted portion of SA, and sorts the suffixes based on the depth in each round. By this means, some suffixes that cannot be sorted by qsufsort in each round can be sorted now, as a result, more sorting results in current round can be utilized by the latter rounds and the total number of sorting rounds will be reduced, which means dsufsort is more efficient than qsufsort. The experimental results show the effectiveness of the proposed algorithm, especially for the text with high repetitions.

Research on multiobjective optimization problems becomes one of the hottest topics of intelligent computation. In order to improve the search efficiency of an evolutionary algorithm and maintain the diversity of solutions, in this paper, the learning automata (LA) is first used for quantization orthogonal crossover (QOX), and a new fitness function based on decomposition is proposed to achieve these two purposes. Based on these, an orthogonal evolutionary algorithm with LA for complex multiobjective optimization problems with continuous variables is proposed. The experimental results show that in continuous states, the proposed algorithm is able to achieve accurate Pareto-optimal sets and wide Pareto-optimal fronts efficiently. Moreover, the comparison with the several existing well-known algorithms: nondominated sorting genetic algorithm II, decomposition-based multiobjective evolutionary algorithm, decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, multiobjective optimization by LA, and multiobjective immune algorithm with nondominated neighbor-based selection, on 15 multiobjective benchmark problems, shows that the proposed algorithm is able to find more accurate and evenly distributed Pareto-optimal fronts than the compared ones.

Instance coreference resolution is an essential problem in studying semantic web, and it is also critical for the implementation of web of data and future integration and application of semantic data. In this paper, we propose to use Memetic Algorithm (MA) to solve this instance coreference problem in a sequential stage, i.e., the instance-level matching is carried out with the result of schema-level matching. We first give the optimization model for schema-level matching and instance-level matching. Then, we, respectively, present profile similarity measures and the rough evaluation metrics with the assumption that the golden alignment for both schema-level matching and instance-level matching is one-to-one. Furthermore, we give the details of the MA. Finally, the experiments of comparing our approach with the state-of-the-art systems on OAEI benchmarks and real-world datasets are conducted and the results demonstrate that our approach is effective.