Optimizing Ontology Alignment Through an Interactive Compact Genetic Algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 1-17
Author(s):  
Xingsi Xue ◽  
Xiaojing Wu ◽  
Junfeng Chen
Keyword(s):  
Genetic Algorithm ◽  
Intelligent Systems ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Knowledge Modeling ◽  
Matching Problem ◽  
Local Optima ◽  
Argumentation Framework ◽  
Compact Genetic Algorithm ◽  
Matching Technique

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.

scholarly journals Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2056 ◽  
Author(s):  
Xingsi Xue ◽  
Junfeng Chen
Keyword(s):  
Firefly Algorithm ◽  
Gray Code ◽  
General Purpose ◽  
Sensor Data ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Matching Technique ◽  
Sensor Information ◽  
Exploitation And Exploration

Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems’ inter-operability, which requires that the sensor ontologies themselves are inter-operable. Therefore, it is necessary to match the sensor ontologies by establishing the meaningful links between semantically related sensor information. Since the Swarm Intelligent Algorithm (SIA) represents a good methodology for addressing the ontology matching problem, we investigate a popular SIA, that is, the Firefly Algorithm (FA), to optimize the ontology alignment. To save the memory consumption and better trade off the algorithm’s exploitation and exploration, in this work, we propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA), which combines the compact encoding mechanism with the co-Evolutionary mechanism. Our proposal utilizes the Gray code to encode the solutions, two compact operators to respectively implement the exploiting strategy and exploring strategy, and two Probability Vectors (PVs) to represent the swarms that respectively focuses on the exploitation and exploration. Through the communications between two swarms in each generation, CcFA is able to efficiently improve the searching efficiency when addressing the sensor ontology matching problem. The experiment utilizes the Conference track and three pairs of real sensor ontologies to test our proposal’s performance. The statistical results show that CcFA based ontology matching technique can effectively match the sensor ontologies and other general ontologies in the domain of organizing conferences.

A Preference-Based Multi-Objective Evolutionary Algorithm for Semiautomatic Sensor Ontology Matching

2018 ◽  
Vol 9 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Xingsi Xue ◽  
Junfeng Chen
Keyword(s):  
Evolutionary Algorithm ◽  
Environmental Data ◽  
User Preference ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Effective Technique ◽  
Multi Objective ◽  
Matching Technique

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.

scholarly journals Multiobjective Sensor Ontology Matching Technique with User Preference Metrics

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hai Zhu ◽  
Xingsi Xue ◽  
Chengcai Jiang ◽  
He Ren
Keyword(s):  
Similarity Measures ◽  
User Preference ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Nondominated Solutions ◽  
Network Applications ◽  
Data Heterogeneity ◽  
Ontology Alignment Evaluation Initiative ◽  
Selection Operator

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.

Semi-Automatic Sensor Ontology Matching Based on Interactive Multi-Objective Evolutionary Algorithm

2020 ◽  
pp. 27-42
Author(s):  
Xingsi Xue ◽  
Junfeng Chen
Keyword(s):  
Evolutionary Algorithm ◽  
Test Performance ◽  
Experimental Results ◽  
Semantic Gap ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Multi Objective ◽  
Dominance Rule ◽  
Ontology Alignment Evaluation Initiative

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.

Optimizing Ontology Alignment Through Compact MOEA/D

2017 ◽  
Vol 31 (04) ◽  
pp. 1759004 ◽  
Author(s):  
Xingsi Xue ◽  
Jianhua Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Similarity Measures ◽  
Main Memory ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Multi Objective ◽  
Matching Process ◽  
Self Tuning ◽  
Optimal Alignments

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.

scholarly journals Matching Sensor Ontologies with Simulated Annealing Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hai Zhu ◽  
Xingsi Xue ◽  
Aifeng Geng ◽  
He Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Location Based Services ◽  
Sensor Data ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Swarm Optimization

In recent years, innovative positioning and mobile communication techniques have been developing to achieve Location-Based Services (LBSs). With the help of sensors, LBS is able to detect and sense the information from the outside world to provide location-related services. To implement the intelligent LBS, it is necessary to develop the Semantic Sensor Web (SSW), which makes use of the sensor ontologies to implement the sensor data interoperability, information sharing, and knowledge fusion among intelligence systems. Due to the subjectivity of sensor ontology engineers, the heterogeneity problem is introduced, which hampers the communications among these sensor ontologies. To address this problem, sensor ontology matching is introduced to establish the corresponding relationship between different sensor terms. Among all ontology matching technologies, Particle Swarm Optimization (PSO) can represent a contributing method to deal with the low-quality ontology alignment problem. For the purpose of further enhancing the quality of matching results, in our work, sensor ontology matching is modeled as the meta-matching problem firstly, and then based on this model, aiming at various similarity measures, a Simulated Annealing PSO (SAPSO) is proposed to optimize their aggregation weights and the threshold. In particular, the approximate evaluation metrics for evaluating quality of alignment without reference are proposed, and a Simulated Annealing (SA) strategy is applied to PSO’s evolving process, which is able to help the algorithm avoid the local optima and enhance the quality of solution. The well-known Ontology Alignment Evaluation Initiative’s benchmark (OAEI’s benchmark) and three real sensor ontologies are used to verify the effectiveness of SAPSO. The experimental results show that SAPSO is able to effectively match the sensor ontologies.

scholarly journals Evaluation of two heuristic approaches to solve the ontology meta-matching problem

2017 ◽  
Author(s):  
Jorge Martinez-Gil ◽  
José F. Aldana-Montes
Keyword(s):  
Genetic Algorithm ◽  
Similarity Measure ◽  
Ontology Matching ◽  
Complex Nature ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Greedy Strategy ◽  
Heuristic Approaches ◽  
Matching Process ◽  
Maximum Similarity

Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies. Ontology meta-matching, which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the ontology meta-matching problem. The first one is called maximum similarity measure, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called genetics for ontology alignments and is based on a genetic algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.

scholarly journals Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xingsi Xue ◽  
Chaofan Yang ◽  
Chao Jiang ◽  
Pei-Wei Tsai ◽  
Guojun Mao ◽  
...  
Keyword(s):  
Resource Sharing ◽  
Building Blocks ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Memory Consumption ◽  
Matching Technique ◽  
Data Heterogeneity ◽  
Ontology Alignment Evaluation Initiative ◽  
Linkage Learning ◽  
Matching Techniques

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.

scholarly journals Matching sensor ontologies with unsupervised neural network with competitive learning

2021 ◽  
Vol 7 ◽  
pp. e763
Author(s):  
Xingsi Xue ◽  
Haolin Wang ◽  
Wenyu Liu
Keyword(s):  
Neural Network ◽  
Domain Knowledge ◽  
Learning Strategy ◽  
Binary Classification ◽  
Competitive Learning ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Training Samples ◽  
Unsupervised Neural Network

Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal’s performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.

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