scholarly journals ON ONTOLOGY MATCHING PROBLEMS - for building a corporate Semantic Web in a multi-communities organization

2004 ◽  
Keyword(s):  
Semantic Web ◽  
Ontology Matching ◽  
Matching Problems

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.

A weighted graph-oriented ontology matching algorithm for enhancing ontology mapping and alignment in Semantic Web

2019 ◽  
Vol 23 (18) ◽  
pp. 8661-8676 ◽  
Author(s):  
V. Rajeswari ◽  
M. Kavitha ◽  
Dharmistan K. Varughese
Keyword(s):  
Semantic Web ◽  
Weighted Graph ◽  
Ontology Mapping ◽  
Ontology Matching ◽  
Matching Algorithm

scholarly journals Alignment: A Hybrid, Interactive and Collaborative Ontology and Entity Matching Service

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 281 ◽  
Author(s):  
Sotirios Karampatakis ◽  
Charalampos Bratsas ◽  
Ondřej Zamazal ◽  
Panagiotis Filippidis ◽  
Ioannis Antoniou
Keyword(s):  
Semantic Web ◽  
Human Factor ◽  
Similarity Measures ◽  
Level Of Detail ◽  
Ontology Matching ◽  
Entity Matching ◽  
Open World ◽  
The World ◽  
User Friendly ◽  
Essential Problem

Ontology matching is an essential problem in the world of Semantic Web and other distributed, open world applications. Heterogeneity occurs as a result of diversity in tools, knowledge, habits, language, interests and usually the level of detail. Automated applications have been developed, implementing diverse aligning techniques and similarity measures, with outstanding performance. However, there are use cases where automated linking fails and there must be involvement of the human factor in order to create, or not create, a link. In this paper we present Alignment, a collaborative, system aided, interactive ontology matching platform. Alignment offers a user-friendly environment for matching two ontologies with the aid of configurable similarity algorithms.

Large Scale Matching Issues and Advances

2010 ◽  
pp. 208-224
Author(s):  
Sana Sellami ◽  
Aicha-Nabila Benharkat ◽  
Youssef Amghar
Keyword(s):  
Information Technology ◽  
Semantic Web ◽  
Digital Libraries ◽  
Large Scale ◽  
Optimization Techniques ◽  
Ontology Matching ◽  
Hard Problem ◽  
Semantic Heterogeneity ◽  
Matching Techniques ◽  
Open Issues

Nowadays, the Information technology domains (semantic web, E-business, digital libraries, life science, etc) abound with a large variety of data (e.g. DB schemas, XML schemas, ontologies) and bring up a hard problem: the semantic heterogeneity. Matching techniques are called to overcome this challenge and attempts to align these data. In this chapter, the authors are interested in studying large scale matching approaches. They survey the techniques of large scale matching, when a large number of schemas/ontologies and attributes are involved. They attempt to cover a variety of techniques for schema matching called Pair-wise and Holistic, as well as a set of useful optimization techniques. They compare the different existing schema/ontology matching tools. One can acknowledge that this domain is on top of effervescence and large scale matching needs many more advances. Then the authors provide conclusions concerning important open issues and potential synergies of the technologies presented.

A differentor-based adaptive ontology-matching approach

2012 ◽  
Vol 38 (5) ◽  
pp. 459-475 ◽  
Author(s):  
Peigang Xu ◽  
Yadong Wang ◽  
Bo Liu
Keyword(s):  
Semantic Web ◽  
Prior Knowledge ◽  
Similarity Measure ◽  
Web Applications ◽  
Similarity Measures ◽  
Experimental Results ◽  
Ontology Matching ◽  
Semantic Features ◽  
One To One ◽  
The One

Ontology matching, aimed at finding semantically related entities from different ontologies, plays an important role in establishing interoperation among Semantic Web applications. Recently, many similarity measures have been proposed to explore the lexical, structural or semantic features of ontologies. However, a key problem is how to integrate various similarities automatically. In this paper, we define a novel metric termed a “differentor” to assess the probability that a similarity measure can find the one-to-one mappings between two ontologies at the entity level, and use it to integrate different similarity measures. The proposed approach can assign weights automatically to each pair of entities from different ontologies without any prior knowledge, and the aggregation task is accomplished based on these weights. The proposed approach has been tested on OAEI2010 benchmarks for evaluation. The experimental results show that the differentor can reflect the performance of individual similarity measures, and a differentor-based aggregation strategy outperforms other existing aggregation strategies.

scholarly journals Coincidence-Based Scoring of Mappings in Ontology Alignment

2007 ◽  
Vol 11 (7) ◽  
pp. 803-816 ◽  
Author(s):  
Seyed H. Haeri (Hossein) ◽  
◽  
Hassan Abolhassani ◽  
Vahed Qazvinian ◽  
Babak Bagheri Hariri
Keyword(s):  
Genetic Algorithm ◽  
Semantic Web ◽  
Random Walks ◽  
Metric Space ◽  
The Other ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Test Collections ◽  
New Perspective ◽  
Optimum Alignment

Ontology Matching (OM) which targets finding a set of alignments across two ontologies, is a key enabler for the success of Semantic Web. In this paper, we introduce a new perspective on this problem. By interpreting ontologies as Typed Graphs embedded in a Metric Space,coincidenceof the structures of the two ontologies is formulated. Having such a formulation, we define a mechanism to score mappings. This scoring can then be used to extract a good alignment among a number of candidates. To do this, this paper introduces three approaches: The first one, straightforward and capable of finding the optimum alignment, investigates all possible alignments, but its runtime complexity limits its use to small ontologies only. To overcome this shortcoming, we introduce a second solution as well which employs a Genetic Algorithm (GA) and shows a good effectiveness for some certain test collections. Based on approximative approaches, a third solution is also provided which, for the same purpose, measures random walks in each ontology versus the other.

Research on Ontology Matching Approach in Semantic Web

2008 ◽  
Author(s):  
Rubo Zhang ◽  
Ying Wang ◽  
Jing Wang
Keyword(s):  
Semantic Web ◽  
Ontology Matching
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