Exploring Pattern Mining for Solving the Ontology Matching Problem

Belhadi, Hiba; Akli-Astouati, Karima; Djenouri, Youcef; Lin, Jerry Chun‐Wei

doi:10.1007/978-3-030-30278-8_11

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Data Mining for Ontology Matching (DMOM) framework compares the cases and the data properties through three stages: exhaustive, statistical, and Frequent Itemset Mining (FIM) using the DBpedia ontology [31]. Similarly, a pattern-matching algorithm proposes to solve ontology matching problems using a pattern mining approach [32]. This method searches for the redundant patterns in the ontology database and matches the target ontology's relevant feature to find efficient matching.…”

Section: Literature Reviewmentioning

confidence: 99%

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

Velu¹,

Thangavelu²

2022

Computers, Materials &Amp; Continua

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The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data analysis, discovery, IR, and decision making. In addition to that, it also evaluates the developed ontology with other state-of-the-art ontologies. The proposed ontology's quality has improved by 39.28% in terms of completeness, and structural complexity has decreased by 45.29%, 11% and 37.7% in Precision and Accuracy. Indian Meteorological Satellite INSAT-3D's ocean data is a typical example of testing the proposed model. The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.

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Section: Literature Reviewmentioning

confidence: 99%

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

Velu¹,

Thangavelu²

2022

Computers, Materials &Amp; Continua

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“…The genetic algorithm is then performed in order to explore the alignment space between two ontologies. Belhadi et al 33 proposed a pattern mining for ontology matching (PMOM), which is a data mining based solution for the ontology matching. The set of frequent patterns of both ontologies are first discovered, instead of exploring the whole set of properties, only these relevant patterns are checked to find the best alignment.…”

Section: Related Workmentioning

confidence: 99%

“…The first algorithm called GFSOM 32 which received the best paper award in the international conference on genetic and evolutionary computing. The second algorithm called PMOM 33 which is recently published in European conference on advances in databases and information systems. The runtime is calculated by seconds, and the accuracy is determined by computing the percentage of the corrected matched.…”

Section: Performance Evaluationmentioning

confidence: 99%

Intelligent blockchain management for distributed knowledge graphs in IoT 5G environments

Djenouri

Srivastava

Belhadi

et al. 2021

Trans Emerging Tel Tech

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This article introduces a new problem of distributed knowledge graph, in IoT 5G setting. We developed an end‐to‐end solution for solving such problem by exploring the blockchain management and intelligent method for producing the better matching of the concepts and relations of the set of knowledge graphs. The concepts and the relations of the knowledge graphs are divided into several components, each of which contains similar concepts and relations. Instead of exploring the whole concepts and the relations of the knowledge graphs, only the representative of these components is compared during the matching process. The framework has outperformed state‐of‐the‐art knowledge graph matching algorithms using different scenarios as input in the experiments. In addition, to confirm the usability of our suggested framework, an in‐depth experimental analysis has been done; the results are very promising in both runtime and accuracy.

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“…However, these approaches have runtimes greater than 700 s, with data properties equal to 100%. More sophisticated solutions to ontology problems attempt to improve the matching process by exploring the search space with the partitioning algorithms, [6][7][8][9][10] high-performance computing (HPC), [11][12][13] and evolutionary computation approaches, [14][15][16][17] among others. However, the overall performance of the ontology matching still needs improvements in particular for complex applications such as related to smart cities.…”

Section: % Propertiesmentioning

confidence: 99%

An ontology matching approach for semantic modeling: A case study in smart cities

Djenouri

Belhadi

Akli-Astouati

et al. 2021

Computational Intelligence

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This paper investigates the semantic modeling of smart cities and proposes two ontology matching frameworks, called Clustering for Ontology Matching-based Instances (COMI) and Pattern mining for Ontology Matching-based Instances (POMI). The goal is to discover the relevant knowledge by investigating the correlations among smart city data based on clustering and pattern mining approaches. The COMI method first groups the highly correlated ontologies of smart-city data into similar clusters using the generic k-means algorithm. The key idea of this method is that it clusters the instances of each ontology and then matches two ontologies by matching their clusters and the corresponding instances within the clusters. The POMI method studies the correlations among the data properties and selects the most relevant properties for the ontology matching process. To demonstrate the usefulness and accuracy of the COMI and POMI frameworks, several experiments on the DBpedia, Ontology Alignment Evaluation Initiative, and NOAA ontology databases were conducted. The results show that COMI and POMI outperform the state-of-the-art ontology matching models regarding computational cost without losing the quality during the matching process. Furthermore, these results confirm the ability of COMI and POMI to deal This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Exploring Pattern Mining for Solving the Ontology Matching Problem

Cited by 6 publications

References 20 publications

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

Intelligent blockchain management for distributed knowledge graphs in IoT 5G environments

An ontology matching approach for semantic modeling: A case study in smart cities

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