A Semantic-Based Ontology Matching Process for PDMS

2009 ◽  
pp. 124-135 ◽  
Author(s):  
Carlos Eduardo Pires ◽  
Damires Souza ◽  
Thiago Pachêco ◽  
Ana Carolina Salgado
Keyword(s):  
Ontology Matching ◽  
Matching Process

scholarly journals Using decision trees for inductively driven semantic integration and ontology matching

2021 ◽  
Author(s):  
Bart Gajderowicz
Keyword(s):  
Decision Trees ◽  
Real World ◽  
Semantic Integration ◽  
Use Cases ◽  
Ontology Matching ◽  
Bottom Up ◽  
Matching Algorithm ◽  
Matching Process ◽  
Narrow Context

The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. While different ontologists, experts, and organizations create the vast majority of ontologies, often for internal use of for use in a narrow context, their domains frequently overlap in a wider context, specifically for complementary domains. To assist in the reuse of ontologies, this thesis proposes a bottom-up technique for creating concept anchors that are used for ontology matching. Anchors are ontology concepts that have been matched to concepts in an eternal ontology. The matching process is based on inductively derived decision trees rules for an ontology that are compared with rules derived for external ontologies. The matching algorithm is intended to match taxomonies, ontologies which define subsumption relations between concepts, with an associated database used to derive the decision trees. This thesis also introduces several algorithm evolution measures, and presents a set of use cases that demonstrate the strengths and weaknesses of the matching process.

scholarly journals Using decision trees for inductively driven semantic integration and ontology matching

2021 ◽  
Author(s):  
Bart Gajderowicz
Keyword(s):  
Decision Trees ◽  
Real World ◽  
Semantic Integration ◽  
Use Cases ◽  
Ontology Matching ◽  
Bottom Up ◽  
Matching Algorithm ◽  
Matching Process ◽  
Narrow Context

The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. While different ontologists, experts, and organizations create the vast majority of ontologies, often for internal use of for use in a narrow context, their domains frequently overlap in a wider context, specifically for complementary domains. To assist in the reuse of ontologies, this thesis proposes a bottom-up technique for creating concept anchors that are used for ontology matching. Anchors are ontology concepts that have been matched to concepts in an eternal ontology. The matching process is based on inductively derived decision trees rules for an ontology that are compared with rules derived for external ontologies. The matching algorithm is intended to match taxomonies, ontologies which define subsumption relations between concepts, with an associated database used to derive the decision trees. This thesis also introduces several algorithm evolution measures, and presents a set of use cases that demonstrate the strengths and weaknesses of the matching process.

scholarly journals An Iterative Automatic Final Alignment Method in the Ontology Matching System

2018 ◽  
Vol 42 (1) ◽  
pp. 39-61 ◽  
Author(s):  
Marko Gulić ◽  
Marin Vuković
Keyword(s):  
Heterogeneous Data ◽  
Data Sources ◽  
Ontology Matching ◽  
Alignment Method ◽  
Automatic Adjustment ◽  
Matching Process ◽  
Heterogeneous Data Sources ◽  
Final Alignment

Ontology matching plays an important role in the integration of heterogeneous data sources that are described by ontologies. In order to determine correspondences between ontologies, a set of matchers can be used. After the execution of these matchers and the aggregation of the results obtained by these matchers, a final alignment method is executed in order to select appropriate correspondences between entities of compared ontologies. The final alignment method is an important part of the ontology matching process because it directly determines the output result of this process. In this paper we improve our iterative final alignment method by introducing an automatic adjustment of final alignment threshold as well as a new rule for determining false correspondences with similarity values greater than adjusted threshold. An evaluation of the method is performed on the test ontologies of the OAEI evaluation contest and a comparison with other final alignment methods is given.

scholarly journals Alin: improving interactive ontology matching by interactively revising mapping suggestions

2020 ◽  
Vol 35 ◽  
Author(s):  
Jomar Da Silva ◽  
Kate Revoredo ◽  
Fernanda Baião ◽  
Jérôme Euzenat
Keyword(s):  
State Of The Art ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Ontology Matching ◽  
New Techniques ◽  
Domain Experts ◽  
Matching Process ◽  
Heterogeneous Data Sources ◽  
Expert Answer ◽  
Comparable Quality

Abstract Ontology matching aims at discovering mappings between the entities of two ontologies. It plays an important role in the integration of heterogeneous data sources that are described by ontologies. Interactive ontology matching involves domain experts in the matching process. In some approaches, the expert provides feedback about mappings between ontology entities, that is, these approaches select mappings to present to the expert who replies which of them should be accepted or rejected, so taking advantage of the knowledge of domain experts towards finding an alignment. In this paper, we present Alin, an interactive ontology matching approach which uses expert feedback not only to approve or reject selected mappings but also to dynamically improve the set of selected mappings, that is, to interactively include and to exclude mappings from it. This additional use for expert answers aims at increasing in the benefit brought by each expert answer. For this purpose, Alin uses four techniques. Two techniques were used in the previous versions of Alin to dynamically select concept and attribute mappings. Two new techniques are introduced in this paper: one to dynamically select relationship mappings and another one to dynamically reject inconsistent selected mappings using anti-patterns. We compared Alin with state-of-the-art tools, showing that it generates alignment of comparable quality.

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 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 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 DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching

2020 ◽  
Vol 10 (21) ◽  
pp. 7909
Author(s):  
Jifang Wu ◽  
Jianghua Lv ◽  
Haoming Guo ◽  
Shilong Ma
Keyword(s):  
Network Structure ◽  
Threshold Value ◽  
Representation Learning ◽  
Biomedical Ontology ◽  
Semantic Integration ◽  
Ontology Matching ◽  
Matching Process ◽  
Learning Techniques ◽  
Expert User ◽  
Final Alignment

Ontology Matching (OM) is performed to find semantic correspondences between the entity elements of different ontologies to enable semantic integration, reuse, and interoperability. Representation learning techniques have been introduced to the field of OM with the development of deep learning. However, there still exist two limitations. Firstly, these methods only focus on the terminological-based features to learn word vectors for discovering mappings, ignoring the network structure of ontology. Secondly, the final alignment threshold is usually determined manually within these methods. It is difficult for an expert to adjust the threshold value and even more so for a non-expert user. To address these issues, we propose an alternative ontology matching framework called Deep Attentional Embedded Ontology Matching (DAEOM), which models the matching process by embedding techniques with jointly encoding ontology terminological description and network structure. We propose a novel inter-intra negative sampling skill tailored for the structural relations asserted in ontologies, and further improve our iterative final alignment method by introducing an automatic adjustment of the final alignment threshold. The preliminary result on real-world biomedical ontologies indicates that DAEOM is competitive with several OAEI top-ranked systems in terms of F-measure.

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