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.

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.

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.

scholarly journals Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1287
Author(s):  
Xingsi Xue ◽  
Pei-Wei Tsai ◽  
Yucheng Zhuang
Keyword(s):  
Evolutionary Algorithm ◽  
Large Scale ◽  
Biomedical Ontology ◽  
Biomedical Data ◽  
Biomedical Ontologies ◽  
Ontology Alignment ◽  
Multi Objective Optimization ◽  
Matching Problem ◽  
Multi Objective ◽  
Matching Techniques

To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment’s quality, which calculate the alignment’s statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm’s convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s biomedical tracks to test aMMOEA’s performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI’s participants show that aMMOEA is able to effectively determine diverse solutions for decision makers.

scholarly journals Sensor Ontology Metamatching with Heterogeneity Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xingsi Xue ◽  
Jiawei Lu ◽  
Chengcai Jiang ◽  
Yikun Huang
Keyword(s):  
State Of The Art ◽  
Similarity Measures ◽  
Ontology Matching ◽  
Learning Approach ◽  
Ontology Alignment ◽  
Matching Process ◽  
Ontology Alignment Evaluation Initiative ◽  
Matching Techniques ◽  
First Time ◽  
Concept Pairs

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.

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.

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 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.

scholarly journals Optimizing Ontology Alignment through Improved NSGA-II

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yikun Huang ◽  
Xingsi Xue ◽  
Chao Jiang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Evolutionary Process ◽  
User Preferences ◽  
Ontology Matching ◽  
Pareto Optimum ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Nsga Ii ◽  
Monkey King

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.

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