Data integration approach for semi-structured and structured data (Linked Data)

Kettouch, Mohamed Salah; Luca, Cristina; Hobbs, Mike; Fatima, Arooj

doi:10.1109/indin.2015.7281842

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…On top of RDF, the simple schema language RDFS has been defined to offer a distinguished vocabulary to model class and property hierarchies and other basic schema primitives that can be referred to from RDF models. Both RDF and RDFS constitute a newly emerging standard for metadata that is about to turn all kinds of information (structured, semi-structured and even unstructured data) [8] into a machine-understandable knowledge base. RDFS is an XML application that allows for the denotation of facts and schemata in a machinereadable format, building on an elaborate object model for describing concepts and relations.…”

Section: Knowledge-based Modelling and Reasoningmentioning

confidence: 99%

Towards an Environment for Efficient and Transparent Virtual Machine Operations: The ENTICE Approach

Kimovski

Saurabh

Gec

et al. 2016

2016 5th IEEE International Conference on Cloud Networking (Cloudnet)

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Cloud computing is based on Virtual Machines (VM) or containers, which provide their own software execution environment that can be deployed by facilitating technologies on top of various physical hardware. The use of VMs or containers represents an efficient way to automatize the overall software engineering and operation life-cycle. Some of the benefits include elasticity and high scalability, which increases the utilization efficiency and decreases the operational costs. VMs or containers as software artifacts are created using provider-specific templates and are stored in proprietary or public repositories for further use. However, technology specific choices may reduce their portability, lead to a vendor lock-in, particularly when applications need to run in federated Clouds. In this paper we present the current state of development of the novel concept of a VM repository and operational environment for federated Clouds named ENTICE. The ENTICE environment has been designed to receive unmodified and functionally complete VM images from its users, and transparently tailor and optimise them for specific Cloud infrastructures with respect to their size, configuration, and geographical distribution, such that they are loaded, delivered, and executed faster and with improved QoS compared to their current behaviour. Furthermore, in this work a specific use case scenario for the ENTICE environment has been provided and the underlying novel technologies have been presented.

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Section: Knowledge-based Modelling and Reasoningmentioning

confidence: 99%

Towards an Environment for Efficient and Transparent Virtual Machine Operations: The ENTICE Approach

Kimovski

Saurabh

Gec

et al. 2016

2016 5th IEEE International Conference on Cloud Networking (Cloudnet)

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“…The major aim of schema integration is to provide users with a unified global schema, where users can access, retrieve, and utilize the information, instead of relying on a list of database schema [6]. Natural Language Processing (NLP), schema matching, Information Retrieval (IR), Information Extraction (IE), and named-entity recognition are combined in the development of SI technology [7]. The architecture of an SI system consists of four major modules: pre-integration processing, schema matching processing, conflict solving processing, and merging processing.…”

Section: Introductionmentioning

confidence: 99%

Lexical Disambiguation (CKBD): A Tool to Identify and Resolve Semantic Conflicts Using Context Knowledge

Tahat

Ahmad

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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The schema matching process is a fundamental step in a schema integration system, and its quality impacts the overall performance of the system. Recently, a large number of schema matching approaches have been developed. Until today, the performance of schema matching is inherently uncertain and requires improvement. The most difficult task is inferring the realworld semantics of data from the information provided by schema labels in their representations. Usually, schemas with identical semantics are represented by different vocabularies and only their own designers can completely understand. A schema may contain synonyms and homonyms words. Therefore, it is necessary to understand how the schema elements are "presented"; it is often hard to get aware meaning associated with elements names, due to the semantic ambiguity of human language. Semantic ambiguity problem means the capability of being understood in two or more possible senses. Having more than one meaning for an individual schema element would cause confusion in interpretation of schema name. This may affect negatively on the matching result. Therefore, this paper aims to resolve this problem of semantic ambiguity and represent the intended meaning of the schema labels name, by introducing the CKBD (Context Knowledge-Based Disambiguation) approach. The CKBD is obtained by integrating two pieces of context knowledge: semantic domain and more frequency used into a disambiguation processor. Finally, the CKBD is implemented and is tested in a real dataset. The result is deeply grounded in the ability to detect schema name intended meaning.

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SemiLD: mediator-based framework for keyword search over semi-structured and linked data

2018

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Data integration approach for semi-structured and structured data (Linked Data)

Cited by 9 publications

References 17 publications

Towards an Environment for Efficient and Transparent Virtual Machine Operations: The ENTICE Approach

Towards an Environment for Efficient and Transparent Virtual Machine Operations: The ENTICE Approach

Lexical Disambiguation (CKBD): A Tool to Identify and Resolve Semantic Conflicts Using Context Knowledge

SemiLD: mediator-based framework for keyword search over semi-structured and linked data

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