scholarly journals Towards a Multi-level Upper Ontology/ foundation Ontology Framework as Background Knowledge for Ontology Matching Problem

2015 ◽  
Vol 50 ◽  
pp. 631-634 ◽  
Author(s):  
Alok Chauhan ◽  
V. Vijayakumar ◽  
Ramesh Ragala
Keyword(s):  
Background Knowledge ◽  
Ontology Matching ◽  
Matching Problem ◽  
Multi Level ◽  
Upper Ontology

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.

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 An overview of current ontology meta-matching solutions

2012 ◽  
Vol 27 (4) ◽  
pp. 393-412 ◽  
Author(s):  
Jorge Martinez-Gil ◽  
José F. Aldana-Montes
Keyword(s):  
Dynamic Systems ◽  
State Of The Art ◽  
Ontology Matching ◽  
Complex Nature ◽  
Matching Problem ◽  
Global Strategies

AbstractNowadays, there are a lot of techniques and tools for addressing the ontology matching problem; however, the complex nature of this problem means that the existing solutions are unsatisfactory. This work intends to shed some light on a more flexible way of matching ontologies using ontology meta-matching. This emerging technique selects appropriate algorithms and their associated weights and thresholds in scenarios where accurate ontology matching is necessary. We think that an overview of the problem and an analysis of the existing state-of-the-art solutions will help researchers and practitioners to identify the most appropriate specific features and global strategies in order to build more accurate and dynamic systems following this paradigm.

Review of ontology matching with background knowledge

2016 ◽  
Author(s):  
Inne Gartina Husein ◽  
Saiful Akbar ◽  
Benhard Sitohang ◽  
Fazat Nur Azizah
Keyword(s):  
Background Knowledge ◽  
Ontology Matching

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 Reading Translingual Literary Text in Polycultural Audience

2019 ◽  
Vol 16 (4) ◽  
pp. 637-643
Author(s):  
Sheker A. Kulieva
Keyword(s):  
Background Knowledge ◽  
Literary Text ◽  
The Novel ◽  
Multi Level

This article offers a technique for working with translingual literature in a multilingual audience. The object of analysis - Russophon text - is considered as a meeting place for languages and cultures, the elements of which can be explicated in the presence of the necessary background knowledge. The author cites as an example a joint reading with the students of the novel cycle of the famous Kazakhstan writer A. Zhaksylykov - a multi-level artistic whole that can be deciphered adequately only within the framework of the translingual approach. The purpose of the article is to help specialists choose a methodology for interpreting the text in which the analysis could become productive.

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