Quality Of Ontology Alignment Research Articles

A multi‐objective particle swarm optimization for matching domain ontologies

As a generic conceptual model for describing domain knowledge, ontology is able to provide a formal and concrete description on the concepts in different domains. Since different ontology engineers have diverse viewpoints on the world, leading to the heterogeneity issue among different ontologies. To solve this problem, this paper investigates how to make certain the correspondences of entities between different domain ontologies, that is, domain ontology matching problem. When solving this problem, how to effectively aggregate different similarity measures to differentiate entities and obtain high‐quality alignment is the kernel issue. In this paper, we first introduce two statistical metrics for measuring ontology alignment's quality, then, an optimization model is established for the multi‐objective ontology matching problem, and a Multi‐Objective Particle Swarm Optimization algorithm (MOPSO) is proposed to optimize the quality of domain ontology alignment. The experimental results illustrate that it outperforms OAEI's participants with respect to both recall and precision.

Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix

The ultimate goal of semantic web (SW) is to implement mutual collaborations among ontology-based intelligent systems. To this end, it is necessary to integrate those domain-independent and cross-domain ontologies by finding the correspondences between their entities, which is the so-called ontology matching. To improve the quality of ontology alignment, in this work, the ontology matching problem is first defined as a sparse multi-objective optimization problem (SMOOP), and then, a multi-objective evolutionary algorithm with a relevance matrix (MOEA-RM) is proposed to address it. In particular, a relevance matrix (RM) is presented to adaptively measure the relevance of each individual’s genes to the objectives, which is applied in MOEA’s initialization, crossover and mutation to ensure the population’s sparsity and to speed up the the algorithm’s convergence. The experiment verifies the performance of MOEA-RM by comparing it with the state-of-the-art ontology matching techniques, and the experimental results show that MOEA-RM is able to effectively address the ontology matching problem with different heterogeneity characteristics.

Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment

Ontology alignment is an essential and complex task to integrate heterogeneous ontology. The meta-heuristic algorithm has proven to be an effective method for ontology alignment. However, it only applies the inherent advantages of meta-heuristics algorithm and rarely considers the execution efficiency, especially the multi-objective ontology alignment model. The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications. In this paper, two multi-objective grasshopper optimization algorithms (MOGOA) are proposed to enhance ontology alignment. One is ε-dominance concept based GOA (EMO-GOA) and the other is fast Non-dominated Sorting based GOA (NS-MOGOA). The performance of the two methods to align the ontology is evaluated by using the benchmark dataset. The results demonstrate that the proposed EMO-GOA and NS-MOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.

Matching heterogeneous ontologies with adaptive evolutionary algorithm

An ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology matching is a ground method to address this problem. Ontology matching technique uses the similarity measure to determine the correspondences between two heterogeneous ontology entities. In order to improve the quality of ontology alignment, it is necessary to combine different kinds of similarity measures, and how to optimize the aggregating weights is called the ontology meta-matching problem. Tin this work, a heuristic evaluating metric on the ontology alignment is presented to evaluate the ontology alignment's quality, and a mathematical model on ontology meta-matching problem is constructed. Then, an Adaptive Evolutionary Algorithm (AEA) is proposed to effectively solve this problem. In particular, when the elite solution remains unchanged, AEA adaptively activates three independent exploring strategies, which, respectively use the adaptive selection, crossover and mutation operators based on the population diversity metric. In the experiment, we compare AEA among EA based matching technique and the state-of-the-art ontology matching technique, and the experimental results show its effectiveness.

Ontology Alignment using Stable Matching

Ontology matching is a solution to the problem of semantic heterogeneity in the integration and sharing of information. It consists in establishing mappings between entities which semantically belong to different ontologies. Most ontology mapping approaches use elementary matching techniques (eg, string-based methods, linguistic methods, etc.). These techniques map the elements by analyzing the entities in isolation and ignoring their relationships with other entities (father/son, brother, etc.). Bypassing the latter aspects, the determination of the semantics of an entity is often difficult. Hence, the structural information of an ontology plays an important role in ontologies mapping. In this work, we study methods of structural alignment. We adopt two alignment methods that are based on the structure. The first, called Method of Similarity of Inheritance (MSI), applies the initial similarity method based on the concepts and integrates the inheritance relation (father/son) into the calculation of similarity. The second, called Method of Sibling Similarity (MSS), involves sibling relationships to enrich the similarity score between two concepts. Moreover, we enhance the mappings selection strategy by integrating the stable marriage algorithm as an optimal matching strategy. This algorithm improves the ontology alignment quality through efficient optimization techniques and creates a faster and more robust alignment method.

Interactive ontology matching based on partial reference alignment

The technique that enables the user and the automatic ontology matching tool to cooperate with each other to generate high-quality alignments in a reasonable amount of time is referred to as the interactive ontology matching. Interactive ontology matching poses a new challenge in a way of how to efficiently leverage user validation to improve the ontology alignment. To address this challenge, this paper presents an innovative interactive ontology matching technique based on Partial Reference Alignment (PRA) to better balance between the large workload posed on users and the demand of improving the quality of ontology alignment. In particular, a PRA-based Interactive Compact Hybrid Evolutionary Algorithm (ICHEA) is proposed to reduce user workload, by adaptively determining the timing of involving users, showing them the most problematic mappings, and helping them to deal with multiple conflicting mappings simultaneously. Meanwhile, it increases the value of user involvement by propagating the confidences of validated mappings, as well as reducing the negative effects brought by the erroneous user validations. The well-known OAEI 2016's benchmark track and interactive track are utilized to test the performance of this approach. The experimental results on benchmark track show that both the f-measure and the f-measure per second of this approach outperform those of the OAEI participants and three state-of-the-art Evolutionary Algorithm (EA) based ontology matching techniques. In addition, the experimental results of three interactive testing cases further show that ICHEA can efficiently determine high-quality ontology alignments under different cases of user error rates, and the performance of the approach is generally better than that of state-of-the-art interactive ontology matching systems.

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

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.

Semantic Constraints Satisfaction Based Improved Quality of Ontology Alignment

Semantic Constraints Satisfaction Based Improved Quality of Ontology Alignment

Semantic Constraints Satisfaction Based Improved Quality of Ontology Alignment

Semantic Constraints Satisfaction Based Improved Quality of Ontology Alignment

Semantic Constraints Satisfaction Based Improved Quality of Ontology Alignment

Development of informative and telecommunication technologies have caused to create much dissimilar information. As well with growing different information resources in ontology designs, the importance of management these dissimilar resources has increased. In spite of most matchers use diverse measures for discovery the mappings, some semantic inconsistencies in final alignment are unavoidable. So it is essential to enhance a post-processing phase to training error patterns in the final alignment. The impartial of this research was refining the ontology semantic constraints over defining semantic constraints by a different measure for suitable weighting to the constraints. The outcomes indicated that the standard evaluation measures better in the suggestive method and comparing with other top ranked matchers the used method can create enhanced outcomes.

Ontology alignment using artificial neural network for large-scale ontologies

Achieving high match accuracy for a large variety of ontologies, considering a single matcher is often not sufficient for high match quality. Therefore, combining the corresponding weights for different semantic aspects, reflecting their different importance or contributions becomes unavoidable for ontology matching. Combining multiple measures into a single similarity metric has been traditionally solved using weights determined manually by an expert, or calculated through general methods e.g. average or sigmoid function, however this does not provide a flexible and self-configuring matching tool. In this paper, an intelligent combination using Artificial Neural Network ANN as a machine learning-based method to ascertain how to combine multiple similarity measures into a single aggregated metric with the final aim of improving the ontology alignment quality is proposed. XMap++ is applied to benchmark and anatomy tests at OAEI campaign 2012. Results show that neural network boosts the performance in most cases, and that the proposed novel approach is competitive with top-ranked system.

Ontology Alignment Quality

Recently semantic web technologies, such as ontologies, have been proposed as key enablers for integrating heterogeneous data schemas in business and governmental systems. Algorithms designed to align different but related ontologies have become necessary as differing ontologies proliferate. The process of ontology alignment seeks to find corresponding entities in a second ontology with the same or the closest meaning for each entity in a single ontology. This research is motivated by the need to provide tools and techniques to support the task of validating ontology alignment statements, since it cannot be guaranteed that the results from automated tools are accurate. The authors present a framework for understanding ontology alignment quality and describe how AlViz, a tool for visual ontology alignment, may be used to improve the quality of alignment results. An experiment was undertaken to test the claim that AlViz supports the task of validating ontology alignments. A promising result found that the tool has potential for identifying missing alignments and for rejecting false alignments.