Methods and Systems for the Linked Data

Nafis, Fouad; Chiadmi, Dalila

doi:10.1007/978-3-319-30298-0_62

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The responsibility of this step is carried out by both the information manager and the data owner. There are several tools that can be used for linking structured data on the web such as SILK, LIMES, KnoFuss, RDF-AI, SERIMI, OKKAM [20].…”

Section: Data Interlinkingmentioning

confidence: 99%

Linked Open Data Life Cycle

Al-Mutawa¹,

Al-Aama²

2017

IJCA

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Open data refers to the publishing of information in standard formats that are interoperable and open in order to ease its access and reuse for many purposes such as to encourage citizen participation in the development of their cities and to improve economic opportunities. The strategies of open data have gained the attention of government organizations to ensure transparency and reusability of the data. Many governments across the world have promoted openness as a strategy. Open data enables a government to be transparent and accountable to the citizens of the country. Smart cities is concerned with improving the urban environment by efficiently using existing resources. Those resources can be better understood using linked open data. In the contemporary context, organizations face difficulties in linked open data management, and there is a lack of awareness about the steps to be taken. Additionally, there is no standardized linked open data life cycle yet. This paper proposes a comprehensive open data life cycle model based on literature covering both data supply and demand processes and involving the roles of internal and external stakeholders. The paper presents thorough details of the suggested open data life cycle. The discussed models in the literature have highlighted technical aspects for a smooth and secure publication. Open data life cycle models have the potential of facilitating the procedure of opening up data. They offer phases and steps related to opening up data thus play a pivotal role in the successful application of open data.

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Section: Data Interlinkingmentioning

confidence: 99%

Linked Open Data Life Cycle

Al-Mutawa¹,

Al-Aama²

2017

IJCA

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“…Using link tools, we can expect to have a single complete Moroccan Heritage data source utilizing multiple data sources. As a result, the stored data is automatically processed to extract useful information for the end-user, adding a significant dimension to the proposed system's architecture [1]. This document is structured as follows: Section II provides a synopsis of how SRS works for CH.…”

Section: Introductionmentioning

confidence: 99%

An Approach based on Machine Learning Algorithms for the Recommendation of Scientific Cultural Heritage Objects

Nafis¹,

Fararni²,

Yahyaouy³

et al. 2021

IJACSA

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The Scientific Cultural Heritage (SCH) of the Drâa-Tafilalet region in south-eastern Morocco is a rich source of data testifying to the ingenuity of an older generation that has shaped the past of the region. These data must be preserved for future generations, particularly with new technologies and the semantic web. Recommendation systems (RS) are intended to assist prospective users in recommending the most suitable services based on their profile and expectations. The collaborative filtering (CF), content filtering (CB) or hybrid filtering (CF) RS has shown promising results in order to explore the problems experienced especially in CH. However, there are some limitations to be resolved, mostly due to the ability of these methods to build a stable and complete framework, which can provide a complete image of the user profile and suggest the most appropriate offers. This paper presents a hybrid recommender system for SCH data; a field little explored despite its historical importance and the value it generates. The results presented in this paper belong to the data collected from the region of Drâa-Tafilalet in southern Morocco.

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Methods and Systems for the Linked Data

Cited by 2 publications

References 4 publications

Linked Open Data Life Cycle

Linked Open Data Life Cycle

An Approach based on Machine Learning Algorithms for the Recommendation of Scientific Cultural Heritage Objects

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