SANOM-HOBBIT: simulated annealing-based ontology matching on HOBBIT platform

Mohammadi, Majid; Hofman, Wout; Tan, Yao‐Hua

doi:10.1017/s026988892000017x

Cited by 3 publications

(3 citation statements)

References 25 publications

(37 reference statements)

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“…After each expert feedback, ALIN modifies the mapping suggestions set using structural analysis of ontologies and alignment anti-patterns. SANOM (Mohammadi, 2019) combines lexical (Jaro-Winkler and WordNet) and structural metrics to map entities of two ontologies. It seems effective in dealing with structural consistency, but has no guarantees towards logical consistency.…”

Section: Ontology Matching Methodsmentioning

confidence: 99%

Alignment Conservativity Under the Ontology Change

Atig¹,

Zahaf

Bouchiha³

et al. 2022

Journal of Information Technology Research

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Recently, many methods have appeared to solve the problem of the evolution of alignment under the change of ontologies. The main challenge for them is to maintain consistency of alignment after applying the change. An alignment is consistent if and only if the ontologies remain consistent even when used in conjunction with the alignment. The objective of this work is to take a step forward by considering the alignment evolution according to the conservativity principle under the change of ontologies. In this context, an alignment is conservative if the ontological change should not introduce new semantic relationships between concepts from one of the input ontologies. We give methods for the conservativity violation detection and repair under the change of ontologies and we carry out an experiment on a dataset adapted from the Ontology Alignment Evaluation Initiative. The experiment demonstrates both the practical applicability of the proposed approach and shows the limits of the alignment evolution methods compared to the alignment conservativity under the change of ontologies.

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Section: Ontology Matching Methodsmentioning

confidence: 99%

Alignment Conservativity Under the Ontology Change

Atig¹,

Zahaf

Bouchiha³

et al. 2022

Journal of Information Technology Research

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“…Faria et al discussed the methodology for automatically selecting background knowledge sources for any given ontologies to match [38]. The simulated annealing, which is a evolutionary algorithm, was also used to find the mappings between two given ontologies while no ground truth is available [39]. However, these works ignore the weak informative ontologies in a lot of practical applications.…”

Section: Related Workmentioning

confidence: 99%

Matching weak informative ontologies

Wang

2021

Sci. China Inf. Sci.

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Most existing ontology matching methods utilize the literal information to discover alignments. However, some literal information in ontologies may be opaque and some ontologies may not have sufficient literal information. In this paper, these ontologies are named as weak informative ontologies (WIOs) and it is challenging for existing methods to matching WIOs. On one hand, string-based and linguistic-based matching methods cannot work well for WIOs. On the other hand, some matching methods use external resources to improve their performance, but collecting and processing external resources is still time-consuming. To address this issue, this paper proposes a practical method for matching WIOs by employing the ontology structure information to discover alignments. First, the semantic subgraphs are extracted from the ontology graph to capture the precise meanings of ontology elements. Then, a new similarity propagation model is designed for matching WIOs. Meanwhile, in order to avoid meaningless propagation, the similarity propagation is constrained by semantic subgraphs and other conditions. Consequently, the similarity propagation model ensures a balance between efficiency and quality during matching. Finally, the similarity propagation model uses a few credible alignments as seeds to find more alignments, and some useful strategies are adopted to improve the performance. This matching method for WIOs has been implemented in the ontology matching system Lily. Experimental results on public OAEI benchmark datasets demonstrate that Lily significantly outperforms most of the state-of-the-art works in both WIO matching tasks and general ontology matching tasks. In particular, Lily increases the recall by a large margin, while it still obtains high precision of matching results.

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“…However, even when performed by professionals, this task can be time-consuming, costly, and prone to inaccuracies. Over time, numerous studies and projects have addressed the topic of schema matching, leading to the creation of various articles [3] [4] [5] and the development of multiple prototypes and commercially available solutions. A substantial number of these approaches rely on predefined sets of methods and parameters [6] [7].…”

Section: Introductionmentioning

confidence: 99%

SchemaLogix: Advancing Interoperability with Machine Learning in Schema Matching

Raoui,

Ennaouri,

Yazidi

et al. 2024

ijacsa

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Schema matching, a fundamental process in data integration, traditionally employs pairwise comparisons to discern semantic correspondences among elements in disparate schemas. However, recent developments underscore the necessity of concurrent matching of interconnected schemas, termed schema alignment, to reconcile heterogeneous elements. This paper presents SchemaLogix, an innovative machine learningbased approach for schema matching. SchemaLogix addresses challenges such as data scarcity and domain-specific constraints through an inventive bootstrapping method, autonomously generating extensive datasets. Furthermore, SchemaLogix capitalizes on inherent alignment context constraints to optimize learning and improve precision across varied schema structures. Additionally, SchemaLogix incorporates user contributions to validate chosen correspondences, refining outputs based on valuable feedback. Empirical evaluations establish SchemaLogix's superiority over traditional methods, achieving an exceptional maximum S1 score of 0.90. These results offer practical insights for real-world applications, substantially advancing data integration and interoperability endeavors.

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SANOM-HOBBIT: simulated annealing-based ontology matching on HOBBIT platform

Cited by 3 publications

References 25 publications

Alignment Conservativity Under the Ontology Change

Alignment Conservativity Under the Ontology Change

Matching weak informative ontologies

SchemaLogix: Advancing Interoperability with Machine Learning in Schema Matching

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