资源描述框架RDF和SPARQL核心教程RDF and SPARQL Essentials

想动手掌握数据表示和数据体系结构中最热门的趋势？是否想了解IT，制造，大众媒体，金融服务，制药等各个部门的组织如何拆除数据孤岛以构建自我描述性数据集并推动下一级AI和分析的基础？

您来对地方了！本课程是关于资源描述框架（简称为RDF）和SPARQL的，这是用于构建知识图的语义Web堆栈的两个基本层。知识图本质上是经过详细描述并明确链接为网络的数据集。RDF是用于捕获这些丰富网络的简单数据模型，而SPARQL是用于查询以RDF格式表示的知识图的查询语言。

当我们处于信息时代，关系数据库（例如SQL）之类的技术已经陈旧但尚未过时，并且可以肯定地存在很多年后，组织很快意识到需要将其数据集有效地整合到数据中价值流。这就要求我们捕获并描述数据为网络，并构建数据资源的综合图景，从而使我们能够更智能，直观，大规模地回答关键业务问题。

在本课程中，您将从实际的角度学习如何使用RDF和SPARQL。我们将全力以赴，深入研究以Turtle和TriG格式编写的RDF图形，这是用于编写RDF数据的常见的人类友好文本格式。我们将花费大量时间与SPARQL一起工作，一路上将遇到大量有用的示例和问题，并将一路解决。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 91 lectures (1h 50m) | Size: 646.1 MB
A practical introduction to RDF, Turtle, TriG and SPARQL for authoring and querying knowledge graph data

What you’ll learn:
Knowledge graph technologies that are revolutionising the way we store and query data at scale
Author RDF data and perform Create, Read, Update and Delete (CRUD) operations using the SPARQL query language
Comfortably speak RDF and SPARQL and use the jargon in technical conversations with stakeholders
Acquire a rock-solid foundation for taking on more advanced training in semantic approaches such as RDFS and OWL

Requirements
Analytical background with basic mathematical skills
Prior exposure to data analysis, e.g. tabular data in Excel, SQL or no-SQL technologies
[Optional] Basic understanding of knowledge graphs
[Optional] A pre-installed RDF graph database

Description
Want to get hands-on with the hottest trends in data representation and data architecture? Want to learn the building blocks of how organisations across various sectors like IT, Manufacturing, Mass Media, Financial Services, Pharmaceutical and many more, are tearing down data silos to build self-descriptive datasets and drive next-level AI and analytics?

You’ve landed at the right spot! This course is about the Resource Description Framework or RDF for short, and SPARQL, which are two fundamental layers of the Semantic Web Stack for building knowledge graphs. Knowledge graphs are essentially datasets that are richly described and explicitly linked as networks. RDF is a simple data model for capturing these rich networks, and SPARQL is the query language for interrogating knowledge graphs that are expressed in RDF format.

While we are in the Information Age and technologies like relational databases (e.g. SQL) are old but not obsolete, and surely here to stay for many more years to come, organisations are quickly realising that their datasets need to be weaved together efficiently within the data value stream. This requires us to capture and describe data as networks and building a consolidated picture of our data resources, enabling us to answer key business questions more smartly, intuitively and at scale.

In this course, you’ll learn how to work with RDF and SPARQL from a practical perspective. We’re going to roll up our sleeves and dive into authoring RDF graphs in the Turtle and TriG formats, which are common human-friendly text formats for writing RDF data. We’re going spend a great deal of time working with SPARQL and there will be loads of useful examples and problems we’ll go through and solve along the way.

This course is for people who care about the future of data representation, data architecture and data engineering.

Who this course is for
Data professionals curious about knowledge graph technologies that are based on RDF and SPARQL
Professionals who are at the start of their journey in using Semantic Web technologies
Aspiring knowledge graph architects