State of the Art on Ontology Alignment

Vargas-Vera, Maria; Nagy, Miklos

doi:10.4018/ijksr.2015010102

Cited by 2 publications

(2 citation statements)

References 37 publications

(56 reference statements)

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“…These works include YAM [52], systematic matching ensemble selection [53] or automatic tuning of the matcher parameters [54,49]. Similar research trends and results are also found for other types of data models, such as ontologies [5,55]. Ontology matching focuses on leveraging semantic information such as taxonomies, domain vocabularies, and resource descriptions, to improve matching techniques proposed for database schemas, which lack such semantic information [56,57].…”

Section: Related Workmentioning

confidence: 87%

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

Hùng

Weidlich

Tâm

et al. 2019

Information Systems

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Data models capture the structure and characteristic properties of data entities, e.g., in terms of a database schema or an ontology. They are the backbone of diverse applications, reaching from information integration, through peer-to-peer systems and electronic commerce to social networking. Many of these applications involve models of diverse data sources. Effective utilisation and evolution of data models, therefore, calls for matching techniques that generate correspondences between their elements. Various such matching tools have been developed in the past. Yet, their results are often incomplete or erroneous, and thus need to be reconciled, i.e., validated by an expert. This paper analyses the reconciliation process in the presence of large collections of data models, where the network induced by generated correspondences shall meet consistency expectations in terms of integrity constraints. We specifically focus on how to handle data models that show some internal structure and potentially differ in terms of their assumed level of abstraction. We argue that such a setting calls for a probabilistic model of integrity constraints, for which satisfaction is preferred, but not required. In this work, we present a model for probabilistic constraints that enables reasoning on the correctness of individual correspondences within a network of data models, in order to guide an expert in the validation process. To support pay-as-you-go reconciliation, we also show how to construct a set of high-quality correspondences, even if an expert validates only a subset of all generated correspondences. We demonstrate the efficiency of our techniques for real-world datasets comprising database schemas and ontologies from various application domains.

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Section: Related Workmentioning

confidence: 87%

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

Hùng

Weidlich

Tâm

et al. 2019

Information Systems

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“…e number of entities and relationships between entities are also increasing. For instance, the number of entities in Anatomy and Food was more than 2500 and 20000, respectively [9]. In addition, the scale of some domain ontologies in complex product design fields was large, such as aircraft, robots, and vehicle domain ontologies.…”

Section: Related Workmentioning

confidence: 99%

Matching Large Scale Ontologies Based on Filter and Verification

Jianhui

Liu

et al. 2020

Mathematical Problems in Engineering

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Ontology matching is an effective method to realize intercommunication and interoperability between heterogeneous systems. The essence of ontology matching is to discover the similar entity pairs between source ontology and target ontology, which is a process calculating the similarity between entities in ontologies. The similarity can be calculated utilizing various features between entity pairs, such as string similarity, structural similarity, and semantic similarity. The larger the ontology scale, the lower the efficiency and accuracy rate of ontology matching. As the ontology scale increases, the amount of entities in ontologies will be larger and the ontologies will become more heterogeneous. This paper proposes an innovative method of matching large scale ontologies based on filter and verification, which firstly reduces the heterogeneous of large scale ontologies in the filter phase and then matches the reduced ontologies in the verification phase. Large scale ontologies will be partitioned into several subontologies to get a proper scale before matching. The benchmark of Anatomy and Food in OAEI is adopted to evaluate the proposed method, and the experimental result illuminates that the recall rate is improved in the situation of retaining efficiency and accuracy rate using the proposed method.

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State of the Art on Ontology Alignment

Cited by 2 publications

References 37 publications

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

Matching Large Scale Ontologies Based on Filter and Verification

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