Using semantic similarity for schema matching of semi-structured and linked data

Kettouch, Mohamed Salah; Luca, Cristina; Hobbs, Mike; Dascalu, Sergiu M.

doi:10.1109/itecha.2017.8101923

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…This stage is responsible for matching between the semantically similar properties of the source and the target (candidate for interlinking) datasets. It is based on a schema matching approach called SimiMatch, previously proposed by the authors [45,46]. SimiMatch is an element-based schema matching approach that targets two data models, the semi-structured (hierarchical) model and Linked Data (graph) model.…”

Section: Property Alignment (Simimatch)mentioning

confidence: 99%

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LinkD: element-based data interlinking of RDF datasets in linked data

Kettouch

Luca

2022

Computing

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One of the main obstacles in publishing in a Linked Data way is to connect the dataset being published externally with related data sources in the cloud, known as Data Interlinking. This paper proposes LinkD, a new element-based interlinking approach. LinkD interlinks an RDF dataset, resulted from transformed semi-structured data, with its counterparts in the web of Linked Data. To provide similarity links, the existence of published data in the Linked Data cloud is done in the first place. Different algorithms for similarity measurement are employed while the domain of the dataset being interlinked is taken into account. The techniques utilised allow the processing of a large number of Linked Data datasets. The evaluation of LinkD shows high precision, recall and performance.

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Section: Property Alignment (Simimatch)mentioning

confidence: 99%

“…An optimal threshold (0.75) used by SimiMatch was chosen after experimenting with different values on the same datasets [46].…”

Section: Property Alignment (Simimatch)mentioning

confidence: 99%

LinkD: element-based data interlinking of RDF datasets in linked data

Kettouch

Luca

2022

Computing

Self Cite

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“…The process involves the identification of two semi-related data objects and the creation of mappings between them. In a recent work [24], the authors utilized the UMBC EBIQUITY CORE technique [19] to obtain the similarity of the comments of schemas. Yet, it may not capture the similarity between the descriptions.…”

Section: Related Workmentioning

confidence: 99%

SMAT: An Attention-Based Deep Learning Solution to the Automation of Schema Matching

Zhang

Shin

Choi

et al. 2021

Lecture Notes in Computer Science

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Schema matching aims to identify the correspondences among attributes of database schemas. It is frequently considered as the most challenging and decisive stage existing in many contemporary web semantics and database systems. Low-quality algorithmic matchers fail to provide improvement while manually annotation consumes extensive human efforts. Further complications arise from data privacy in certain domains such as healthcare, where only schema level matching should be used to prevent data leakage. For this problem, we propose SMAT, a new deep learning model based on state-of-the-art natural language processing techniques to obtain semantic mappings between source and target schemas using only the attribute name and description. SMAT avoids directly encoding domain knowledge about the source and target systems, which allows it to be more easily deployed across different sites. We also introduce a new benchmark dataset, OMAP, based on real-world schema-level mappings from the healthcare domain. Our extensive evaluation of various benchmark datasets demonstrates the potential of SMAT to help automate schema-level matching tasks.

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“…the data providing services annotate the data with semantic description elements from the schema descriptions. Externalised schemas are frequently used in semantic web applications, in the context of IoT as described in [5][6][7], in the context of big datasets [8], or for referring to comparability of data [9].…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the previously mentioned problem setting, the goal of this article is to describe the design and basic concepts of a microservice that is able to manage and administer schemas describing the application semantics of specific application domains. Such meta knowledge about data is relevant for many modern application areas like Big Data and IoT, as mentioned before [5][6][7][8][9] and furthermore, the importance of generic microservices which can be used, deployed and executed in a cloud-based environment in a generic application independent way rises continuously. For the design of such microservices and their interoperability with a schema service adding application-specific meta knowledge, there are several requirements that have to be considered.…”

Section: Introductionmentioning

confidence: 99%

Design of a Web-Service for Formal Descriptions of Domain-Specific Data

Sidler

Braun

Schlachter

et al. 2020

IFIP Advances in Information and Communication Technology

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The growing relevance of Big Data and the Internet of Things (IoT) leads to a need for an efficient handling of this data. One key concept to achieve efficient data handling is their semantic description. In the environmental and energy domain, these issues become more relevant since there are measurement stations that produce large amounts of data that software systems have to deal with. In the context of cloud-based infrastructure and virtualisation via containers, microservice architectures and scalability become important aspects in software engineering. This article presents the design of a web service providing software systems with semantic descriptions of data fostering a microservice architecture. It implements key concepts such as domain modelling, schema versioning and schema modularisation. It is evaluated and demonstrated in the context of a current environmental use case.

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Using semantic similarity for schema matching of semi-structured and linked data

Cited by 6 publications

References 15 publications

LinkD: element-based data interlinking of RDF datasets in linked data

LinkD: element-based data interlinking of RDF datasets in linked data

SMAT: An Attention-Based Deep Learning Solution to the Automation of Schema Matching

Design of a Web-Service for Formal Descriptions of Domain-Specific Data

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