Decision trees in automatic ontology matching

Amrouch, Siham; Mostefai, Sihem; Fahad, Muhammad

doi:10.1504/ijmso.2016.081585

Cited by 15 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e anatomy track has been a part of OAEI since 2011 and its aim is to find the alignment between the Adult Mouse Anatomy and a part of the NCI esaurus related to the human anatomy. We select 10 systems participated in the OAEI 2016 for conducting the comparison: Alin (da Silva 2016), AML (Faria et al 2013), CroMatcher (Achichi et al 2016), DKP-AOM (Amrouch et al 2016), FCA-Map (Zhao and Zhang 2016), Lily (Wang and Xu 2008), LogMapLite (Jiménez-Ruiz and Grau 2011), LPHOM (Megdiche et al 2016), LYAM (Achichi et al 2016), andXMap (Djeddi andKhadir 2010).…”

Section: Resultsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

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

View full text Add to dashboard Cite

show abstract

“…The ML-based matching technique models the ontology matching problem as a classification or regression problem. Common methods include support vector machine (SVM) [10], decision tree (DT) [11], logistic regression (LR) [12], etc. The SVM-based matching method [10] solves the dependence of the learning-based matching method on the instances in the ontology by non-instance learning.…”

Section: Literature Review Of Ontology Matching Techniquementioning

confidence: 99%

Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm

Zhuang

Huang

Liu

2023

Sensors

View full text Add to dashboard Cite

Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor devices is hindered. Sensor ontology matching achieves data integration and sharing between sensors by establishing semantic relationships between sensor devices. Therefore, a niching multi-objective particle swarm optimization algorithm (NMOPSO) is proposed to effectively solve the sensor ontology matching problem. As the sensor ontology meta-matching problem is essentially a multi-modal optimization problem (MMOP), a niching strategy is introduced into MOPSO to enable the algorithm to find more global optimal solutions that meet the needs of different decision makers. In addition, a diversity-enhancing strategy and an opposition-based learning (OBL) strategy are introduced into the evolution process of NMOPSO to improve the quality of sensor ontology matching and ensure the solutions converge to the real Pareto fronts (PFs). The experimental results demonstrate the effectiveness of NMOPSO in comparison to MOPSO-based matching techniques and participants of the Ontology Alignment Evaluation Initiative (OAEI).

show abstract

“…Ontology matching is a complex, time-consuming, and errorprone work, especially when the scale of ontologies is large. Recently, a number of the machine learning (ML) techniques [8][9][10][11][12][13][14] have been proposed to automatically determine the ontology alignment. To improve the matching efficiency, Araújo et al [15] presented the matching system through parallel computing (PC) technique and Amin et al [16] matching ontology based on cloud computing (CC).…”

Section: Related Workmentioning

confidence: 99%

Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences

et al. 2021

View full text Add to dashboard Cite

Data heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matching is able to identify the semantic correspondences of entities in different ontologies, which is an effective method to address the ontology heterogeneity problem. Due to huge memory consumption and long runtime, the performance of the existing ontology matching techniques requires further improvement. In this work, an extended compact genetic algorithm-based ontology entity matching technique (ECGA-OEM) is proposed, which uses both the compact encoding mechanism and linkage learning approach to match the ontologies efficiently. Compact encoding mechanism does not need to store and maintain the whole population in the memory during the evolving process, and the utilization of linkage learning protects the chromosome’s building blocks, which is able to reduce the algorithm’s running time and ensure the alignment’s quality. In the experiment, ECGA-OEM is compared with the participants of ontology alignment evaluation initiative (OAEI) and the state-of-the-art ontology matching techniques, and the experimental results show that ECGA-OEM is both effective and efficient.

show abstract

Decision trees in automatic ontology matching

Cited by 15 publications

References 12 publications

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

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

Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm

Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences

Contact Info

Product

Resources

About