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 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 Aggregating Heterogeneous Sensor Ontologies with Fuzzy Debate Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xingsi Xue ◽  
Xiaojing Wu ◽  
Jie Zhang ◽  
Lingyu Zhang ◽  
Hai Zhu ◽  
...  
Keyword(s):  
Membership Function ◽  
Semantic Distance ◽  
Sensor Data ◽  
Ontology Matching ◽  
Fuzzy Membership Function ◽  
Ontology Alignment ◽  
Fuzzy Similarity ◽  
Ontology Extraction ◽  
Ontology Alignment Evaluation Initiative ◽  
Matching Techniques

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.

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 Integrating Sensor Ontologies with Global and Local Alignment Extractions

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xingsi Xue ◽  
Xiaojing Wu ◽  
Chao Jiang ◽  
Guojun Mao ◽  
Hai Zhu
Keyword(s):  
Ontology Matching ◽  
Local Alignment ◽  
Ontology Alignment ◽  
Local Factor ◽  
Global Factor ◽  
Two Factors ◽  
Ontology Alignment Evaluation Initiative ◽  
Global And Local ◽  
Matching Techniques ◽  
The Internet Of Things

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.

An Integer Linear Programming-Based Method for the Extraction of Ontology Alignment

2021 ◽  
Vol 16 (2) ◽  
pp. 25-44
Author(s):  
Naima El Ghandour ◽  
Moussa Benaissa ◽  
Yahia Lebbah
Keyword(s):  
Linear Programming ◽  
Semantic Web ◽  
Integer Linear Programming ◽  
New Method ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Programming Techniques ◽  
Data Heterogeneity ◽  
Series Of Experiments

The Semantic Web uses ontologies to cope with the data heterogeneity problem. However, ontologies become themselves heterogeneous; this heterogeneity may occur at the syntactic, terminological, conceptual, and semantic levels. To solve this problem, alignments between entities of ontologies must be identified. This process is called ontology matching. In this paper, the authors propose a new method to extract alignment with multiple cardinalities using integer linear programming techniques. The authors conducted a series of experiments and compared them with currently used methods. The obtained results show the efficiency of the proposed method.

scholarly journals Semisupervised Learning-Based Sensor Ontology Matching

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Hai Zhu ◽  
Jie Zhang ◽  
Xingsi Xue
Keyword(s):  
Large Scale ◽  
Similarity Measures ◽  
Semisupervised Learning ◽  
Ontology Matching ◽  
Semantic Relationship ◽  
The Social ◽  
Matching Technique ◽  
Data Heterogeneity ◽  
Training Examples ◽  
The Internet Of Things

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

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.

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.

Ontology Instance Matching based MPEG-7 Resource Integration

2010 ◽  
Vol 1 (2) ◽  
pp. 18-33
Author(s):  
Hanif Seddiqui ◽  
Masaki Aono
Keyword(s):  
Schema Matching ◽  
Alignment Algorithm ◽  
Resource Integration ◽  
Ontology Alignment ◽  
Instance Matching ◽  
Semantic Link ◽  
Data Heterogeneity ◽  
Rapid Pace ◽  
Description Framework ◽  
Matching Techniques

Heterogeneous multimedia contents are annotated by a sharable formal conceptualization, often called ontology, and these contents, regardless of their media, become sharable resources/instances. Integration of the sharable resources and acquisition of diverse knowledge is getting researchers’ attention at a rapid pace. In this regard, MPEG-7 standard convertible to semantic Resource Description Framework (RDF) evolves for containing structured data and knowledge sources. In this paper, the authors propose an efficient approach to integrate the multimedia resources annotated by the standard of MPEG-7 schema using ontology instance matching techniques. MPEG-7 resources are usually specified explicitly by their surrounding MPEG-7 schema entities, e.g., concepts and properties, in conjunction with other linked resources. Therefore, resource integration needed schema matching as well. In this approach, the authors obtained the schema matching using their scalable ontology alignment algorithm and collected the semantically linked resources, referred to as the Semantic Link Cloud (SLC) collectively for each of the resources. Techniques were addressed to solve several data heterogeneity: value transformation, structural transformation and logical transformation. These experiments show the strength and efficiency of the proposed matching approach.

Ontology Instance Matching Based MPEG-7 Resource Integration

2012 ◽  
pp. 143-159
Author(s):  
Hanif Seddiqui ◽  
Masaki Aono
Keyword(s):  
Schema Matching ◽  
Alignment Algorithm ◽  
Resource Integration ◽  
Ontology Alignment ◽  
Instance Matching ◽  
Semantic Link ◽  
Data Heterogeneity ◽  
Rapid Pace ◽  
Description Framework ◽  
Matching Techniques

Heterogeneous multimedia contents are annotated by a sharable formal conceptualization, often called ontology, and these contents, regardless of their media, become sharable resources/instances. Integration of the sharable resources and acquisition of diverse knowledge is getting researchers’ attention at a rapid pace. In this regard, MPEG-7 standard convertible to semantic Resource Description Framework (RDF) evolves for containing structured data and knowledge sources. In this paper, the authors propose an efficient approach to integrate the multimedia resources annotated by the standard of MPEG-7 schema using ontology instance matching techniques. MPEG-7 resources are usually specified explicitly by their surrounding MPEG-7 schema entities, e.g., concepts and properties, in conjunction with other linked resources. Therefore, resource integration needed schema matching as well. In this approach, the authors obtained the schema matching using their scalable ontology alignment algorithm and collected the semantically linked resources, referred to as the Semantic Link Cloud (SLC) collectively for each of the resources. Techniques were addressed to solve several data heterogeneity: value transformation, structural transformation and logical transformation. These experiments show the strength and efficiency of the proposed matching approach.

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