Using Ontology as Scaffolding for Authoring Teaching Materials

2007 ◽  
pp. 203-221
Author(s):  
Jin-Tan Yang ◽  
Pao Ta Yu ◽  
Nian Shing Chen ◽  
Chun Yen Tsai ◽  
Chi-Chin Lee ◽  
...  
Keyword(s):  
Resource Description Framework ◽  
Domain Knowledge ◽  
Learning Objects ◽  
Domain Ontology ◽  
School Level ◽  
Future Research ◽  
Teaching Materials ◽  
Rich Description ◽  
Description Framework ◽  
Resource Description

The purpose of this study is to conduct teachers to author a teaching material by using visualized domain ontology as scaffolding. Based on a content repository management system (CRMS), mathematics ontology to support teachers for authoring teaching materials is developed. Although the domain ontology of mathematics at secondary school level in Taiwan provides structured vocabularies for describing domain content, those teachers who want to create a knowledge-rich description of domain knowledge, such as required by the “Semantic Web,” use ontology that turns out to provide only part of knowledge required. In this chapter, we examine problems related to capturing the learning resources or learning objects (LOs) on a CRMS. To construct ontology for a subset of mathematics course descriptions, the representation requirements by resource description framework/resource description framework schema (RDF/RDFS) was implemented. Furthermore, a visualized online authoring tool (VOAT) is designed for authoring teaching materials on the Web. Finally, discussion and future research are addressed.

Resource Description Framework

2017 ◽  
pp. 1-4
Author(s):  
Christian Bizer ◽  
Maria-Esther Vidal ◽  
Michael Weiss
Keyword(s):  
Resource Description Framework ◽  
Description Framework ◽  
Resource Description

Towards Massive RDF Storage in NoSQL Databases

2019 ◽  
pp. 263-284 ◽  
Author(s):  
Zongmin Ma ◽  
Li Yan
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Future Research ◽  
Nosql Databases ◽  
Current State ◽  
Data Store ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description ◽  
The Web

The resource description framework (RDF) is a model for representing information resources on the web. With the widespread acceptance of RDF as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the web, a huge amount of RDF data is being proliferated and becoming available. So, RDF data management is of increasing importance and has attracted attention in the database community as well as the Semantic Web community. Currently, much work has been devoted to propose different solutions to store large-scale RDF data efficiently. In order to manage massive RDF data, NoSQL (not only SQL) databases have been used for scalable RDF data store. This chapter focuses on using various NoSQL databases to store massive RDF data. An up-to-date overview of the current state of the art in RDF data storage in NoSQL databases is provided. The chapter aims at suggestions for future research.

A Survey on JSON Mapping With XML/RDF

2019 ◽  
pp. 92-113
Author(s):  
Gbéboumé Crédo Charles Adjallah-Kondo ◽  
Zongmin Ma
Keyword(s):  
Resource Description Framework ◽  
Data Format ◽  
Web Resources ◽  
Description Framework ◽  
Resource Description ◽  
Exchange Data

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.

A Review of RDF Storage in NoSQL Databases

2016 ◽  
pp. 210-229 ◽  
Author(s):  
Zongmin Ma ◽  
Li Yan
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Future Research ◽  
Nosql Databases ◽  
Current State ◽  
Data Store ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description ◽  
The Web

The Resource Description Framework (RDF) is a model for representing information resources on the Web. With the widespread acceptance of RDF as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the Web, a huge amount of RDF data is being proliferated and becoming available. So RDF data management is of increasing importance, and has attracted attentions in the database community as well as the Semantic Web community. Currently much work has been devoted to propose different solutions to store large-scale RDF data efficiently. In order to manage massive RDF data, NoSQL (“not only SQL”) databases have been used for scalable RDF data store. This chapter focuses on using various NoSQL databases to store massive RDF data. An up-to-date overview of the current state of the art in RDF data storage in NoSQL databases is provided. The chapter aims at suggestions for future research.

Linked Open Statistical Metadata

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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