Shiva: A Framework for Graph based Ontology Matching

Mathur, Iti; Joshi, Nisheeth; Darbari, Hemant; Kumar, Ajai

doi:10.5120/15678-4435

Cited by 11 publications

(11 citation statements)

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“…Research on ontology alignment generally explores a fixed solution. Considering the tools submitted to Ontology Alignment Evaluation Initiative (OAEI) campaigns [1], few are those which can be set up by users or easily adapted to new types of matchers (Mathur et al, 2014;. According to , these tools are generally prepared exclusively to reach the best results in predetermined tests, thus, the results lose their importance in practice.…”

Section: Related Workmentioning

confidence: 99%

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A framework to aggregate multiple ontology matchers

Souza

Siqueira

Nunes

2019

IJWIS

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Purpose Although ontology matchers are annually proposed to address different aspects of the semantic heterogeneity problem, finding the most suitable alignment approach is still an issue. This study aims to propose a computational solution for ontology meta-matching (OMM) and a framework designed for developers to make use of alignment techniques in their applications. Design/methodology/approach The framework includes some similarity functions that can be chosen by developers and then, automatically, set weights for each function to obtain better alignments. To evaluate the framework, several simulations were performed with a data set from the Ontology Alignment Evaluation Initiative. Simple similarity functions were used, rather than aligners known in the literature, to demonstrate that the results would be more influenced by the proposed meta-alignment approach than the functions used. Findings The results showed that the framework is able to adapt to different test cases. The approach achieved better results when compared with existing ontology meta-matchers. Originality/value Although approaches for OMM have been proposed, it is not easy to use them during software development. On the other hand, this work presents a framework that can be used by developers to align ontologies. New ontology matchers can be added and the framework is extensible to new methods. Moreover, this work presents a novel OMM approach modeled as a linear equation system which can be easily computed.

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Section: Related Workmentioning

confidence: 99%

“…Adaptable architectures for ontology alignment have been proposed in Mathur et al (2014) and. The proposal from Mathur et al (2014) makes use of edit distance techniques and a graph similarity method in an architecture that allows insertion of new similarity measures. The result of each measure is added in a score matrix.…”

Section: Related Workmentioning

confidence: 99%

A framework to aggregate multiple ontology matchers

Souza

Siqueira

Nunes

2019

IJWIS

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“…ese metrics are of utmost importance, by which most of the correspondences of two given ontologies, including the ontologies of the anatomy track, could be discovered (Cheatham and Hitzler 2013). To compare such metrics over the anatomy track, we take advantage of the Shiva framework (Mathur et al 2014) which converts the ontology mapping into an assignment problem. In this framework, the similarity between each concept from the source ontology is gauged with all the concepts of the target ontology.…”

Section: Lyam Xmapmentioning

confidence: 99%

Comparison of Ontology Alignment Systems Across Single Matching Task Via the McNemar’s Test

Mohammadi

Atashin

Hofman³

et al. 2018

ACM Trans. Knowl. Discov. Data

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Ontology alignment is widely-used to find the correspondences between different ontologies in diverse fields. A er discovering the alignments, several performance scores are available to evaluate them. e scores typically require the identified alignment and a reference containing the underlying actual correspondences of the given ontologies. e current trend in the alignment evaluation is to put forward a new score (e.g., precision, weighted precision, semantic precision, etc.) and to compare various alignments by juxtaposing the obtained scores. However, it is substantially provocative to select one measure among others for comparison. On top of that, claiming if one system has a be er performance than one another cannot be substantiated solely by comparing two scalars. In this paper, we propose the statistical procedures which enable us to theoretically favor one system over one another. e McNemar's test is the statistical means by which the comparison of two ontology alignment systems over one matching task is drawn. e test applies to a 2 × 2 contingency table which can be constructed in two different ways based on the alignments, each of which has their own merits/pitfalls. e ways of the contingency table construction and various apposite statistics from the McNemar's test are elaborated in minute detail. In the case of having more than two alignment systems for comparison, the family-wise error rate is expected to happen. us, the ways of preventing such an error are also discussed. A directed graph visualizes the outcome of the McNemar's test in the presence of multiple alignment systems. From this graph, it is readily understood if one system is be er than one another or if their differences are imperceptible. e proposed statistical methodologies are applied to the systems participated in the OAEI 2016 anatomy track, and also compares several well-known similarity metrics for the same matching problem.Additional Key Words and Phrases: ontology alignment; McNemar's test; family-wise error rate; anatomy; OAEI; ACM Reference format: Majid Mohammadi, Amir Ahooye Atashin, Wout Hofman, and Yaohua Tan. 2017. Comparison of ontology alignment systems across single matching task via the McNemar's test.

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“…Shiva [14] and Shiva++ [15] are a semi-automated ontology alignment system. They were designed to allow the discovery of correspondence between two ontologies.…”

Section: Existing Matching Tools 241 Shiva and Shiva++mentioning

confidence: 99%