Resource Description Framework Generation for Tropical Disease Using Web Scraping

As a data format, JSON is able to store and exchange data. It can be mapped with RDF (resource description framework), which is an ontology technology in the direction of web resources. This chapter replies to the question about which techniques or methods to utilize for mapping XML to JSON and RDF. However, a plethora of methods have been explored. Consequently, the goal of this survey is to give the whole presentation of the currents approaches to map JSON with XML and RDF by providing their differences.

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Linked Open Statistical Metadata

Data Visualization and Statistical Literacy for Open and Big Data - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-2512-7.ch012 ◽

2017 ◽

pp. 297-320

Author(s):

Franck Cotton ◽

Daniel Gillman

Keyword(s):

Semantic Web ◽

Resource Description Framework ◽

Statistical Data ◽

Open Data ◽

Statistical Analyses ◽

Statistical Literacy ◽

Statistical Process ◽

Description Framework ◽

Resource Description ◽

The Web

Linked Open Statistical Metadata (LOSM) is Linked Open Data (LOD) applied to statistical metadata. LOD is a model for identifying, structuring, interlinking, and querying data published directly on the web. It builds on the standards of the semantic web defined by the W3C. LOD uses the Resource Description Framework (RDF), a simple data model expressing content as predicates linking resources between them or with literal properties. The simplicity of the model makes it able to represent any data, including metadata. We define statistical data as data produced through some statistical process or intended for statistical analyses, and statistical metadata as metadata describing statistical data. LOSM promotes discovery and the meaning and structure of statistical data in an automated way. Consequently, it helps with understanding and interpreting data and preventing inadequate or flawed visualizations for statistical data. This enhances statistical literacy and efforts at visualizing statistics.

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Technique for Transformation of Data From RDB to XML Then to RDF

Web Semantics for Textual and Visual Information Retrieval - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-2483-0.ch004 ◽

2017 ◽

pp. 70-91 ◽

Cited By ~ 1

Author(s):

Kaleem Razzaq Malik ◽

Tauqir Ahmad

Keyword(s):

Semantic Web ◽

Relational Database ◽

Resource Description Framework ◽

Data Science ◽

True Nature ◽

Extensible Markup ◽

Description Framework ◽

Mapping Techniques ◽

Resource Description ◽

The Web

This chapter will clearly show the need for better mapping techniques for Relational Database (RDB) all the way to Resource Description Framework (RDF). This includes coverage of each data model limitations and benefits for getting better results. Here, each form of data being transform has its own importance in the field of data science. As RDB is well known back end storage for information used to many kinds of applications; especially the web, desktop, remote, embedded, and network-based applications. Whereas, EXtensible Markup Language (XML) in the well-known standard for data for transferring among all computer related resources regardless of their type, shape, place, capability and capacity due to its form is in application understandable form. Finally, semantically enriched and simple of available in Semantic Web is RDF. This comes handy when with the use of linked data to get intelligent inference better and efficient. Multiple Algorithms are built to support this system experiments and proving its true nature of the study.

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RDF 1.1: Knowledge Representation and Data Integration Language for the Web

Symmetry ◽

10.3390/sym12010084 ◽

2020 ◽

Vol 12 (1) ◽

pp. 84 ◽

Cited By ~ 2

Author(s):

Dominik Tomaszuk ◽

David Hyland-Wood

Keyword(s):

Knowledge Representation ◽

Resource Description Framework ◽

Relational Databases ◽

Formal Definition ◽

Natural Languages ◽

Description Framework ◽

Generalized Knowledge ◽

Definition Of ◽

Resource Description ◽

The Web

Resource Description Framework (RDF) can seen as a solution in today’s landscape of knowledge representation research. An RDF language has symmetrical features because subjects and objects in triples can be interchangeably used. Moreover, the regularity and symmetry of the RDF language allow knowledge representation that is easily processed by machines, and because its structure is similar to natural languages, it is reasonably readable for people. RDF provides some useful features for generalized knowledge representation. Its distributed nature, due to its identifier grounding in IRIs, naturally scales to the size of the Web. However, its use is often hidden from view and is, therefore, one of the less well-known of the knowledge representation frameworks. Therefore, we summarise RDF v1.0 and v1.1 to broaden its audience within the knowledge representation community. This article reviews current approaches, tools, and applications for mapping from relational databases to RDF and from XML to RDF. We discuss RDF serializations, including formats with support for multiple graphs and we analyze RDF compression proposals. Finally, we present a summarized formal definition of RDF 1.1 that provides additional insights into the modeling of reification, blank nodes, and entailments.

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