Publishing Data of Ferdowsi University of Mashhad as Linked Data

Paydar, Samad; Kahani, Mohsen; Behkamal, Behshid; Dadkhah, Mehdi; Sekhavaty, Elaheh

doi:10.1109/cise.2010.5676872

Cited by 6 publications

(10 citation statements)

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“…Whereas a very simple but useful introduction about the generation of rich web application by exploiting linked Data Web can be found in [12]. Payder & et al [13] has published a paper describing a similar work as ours. They also applied LinkedData concept to publish the data of Ferdowsi University of Mashhad.…”

Section: Related Work-a Literature Reviewmentioning

confidence: 80%

Linked Data Generation for the University Data From Legacy Database

Sarkar¹,

Marjit²,

Biswas³

2011

IJWesT

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Web was developed to share information among the users through internet as some hyperlinked documents. If someone wants to collect some data from the web he has to search and crawl through the documents to fulfil his needs. Concept of Linked Data creates a breakthrough at this stage by enabling the links within data. So, besides the web of connected documents a new web developed both for humans and machines, i.e., the web of connected data, simply known as Linked Data Web. Since it is a very new domain, still a very few works has been done, specially the publication of legacy data within a University domain as Linked Data.In this paper our approach concentrates on publishing the data from our University's databases as linked Data on the web and also ensuring the trustworthiness of the data by addition of provenance data along with. For this purpose we have introduced a framework to make all the steps towards the Linked Data publication easy for legacy databases within a university domain.

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Section: Related Work-a Literature Reviewmentioning

confidence: 80%

Linked Data Generation for the University Data From Legacy Database

Sarkar¹,

Marjit²,

Biswas³

2011

IJWesT

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“…To this end, our approach is based on several significant quality issues identified in already published datasets on LOD. To the extent of our experience in publishing and interlinking academic data [3,4], we found that many of the published datasets suffer from quality issues. We believe that most of these issues have roots in the deficiencies of the sources from where that data is extracted, and they can be avoided if they are identified in the initial stages of publishing.…”

Section: Our Proposed Approach For Metric Developmentmentioning

confidence: 99%

“…Empirical evaluation of the developed metrics is provided in Sect. 4, and then some guidelines for quality improvement are presented in Sect. 5.…”

Section: Introductionmentioning

confidence: 99%

“…Other kinds of online validators such as URIDebugger 1 and Vapour 2 check the dereferencability of a given URI. Also, there are some command line tools for identifying common errors in RDF documents, such as Jena Eyeball 3 and VRP 4 . Another group of tools are designed for checking common quality issues, such as Lodlaundromat 5 which accepts URLs of dirty datasets and removes syntax errors, duplicates, and blank nodes; and Luzzu 6 which is a framework allowing users to define some metrics and provides queryable quality metadata on the assessed datasets.…”

Section: Introductionmentioning

confidence: 99%

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Quality Metrics for Linked Open Data

Behkamal

Kahani

Bagheri

2015

Lecture Notes in Computer Science

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Abstract. The vision of the Linked Open Data (LOD) initiative is to provide a model for publishing data and meaningfully interlinking such dispersed but related data. Despite the importance of data quality for the successful growth of the LOD, only limited attention has been focused on quality of data prior to their publication on the LOD. This paper focuses on the systematic assessment of the quality of datasets prior to publication on the LOD cloud. To this end, we identify important quality deficiencies that need to be avoided and/or resolved prior to the publication of a dataset. We then propose a set of metrics to measure and identify these quality deficiencies in a dataset. This way, we enable the assessment and identification of undesirable quality characteristics of a dataset through our proposed metrics.

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“…We herein discuss some problems and challenges of linked data, along with possible publisher-oriented approaches to improve the quality of structured, machine-readable and open data on the Web based on our experiences with "FUM-LD" project [9]. FUM-LD is a framework developed for publishing the data of Ferdowsi University of Mashhad (FUM) as Linked Data.…”

Section: Introductionmentioning

confidence: 99%