Web of Data

Start Date: 07/05/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/web-data

About Course

This MOOC – a joint initiative between EIT Digital, Université de Nice Sophia-Antipolis / Université Côte d'Azur and INRIA - introduces the Linked Data standards and principles that provide the foundation of the Semantic web. You will learn how to publish, obtain and use structured data directly from the Web. Learning the principles, languages and standards to exchange Data on the Web will enable you to design and produce new applications, products and services that leverage the volume and variety of data the Web holds. We divided this course into four parts that cover the core technical skills and competencies you need to master to be able to use the Web as a space for giant structure data exchange: • in the first part, “Principals of a Web of Linked Data”: you will learn and practice the principles to publish and obtain data directly on the Web instead of Web pages; • in the second part, “The RDF Data Model”: you will learn the standard data model for the Web and its syntaxes to publish and link data on the Web in your applications and services; • in the third part, “SPARQL Query Language”: you will learn how to directly query and access data sources on the Web and obtain structured data relevant to your activity and domain; • in the fourth and final part, “Integration of other Data Formats and Sources”: you will learn how the Web standards interact and interoperate with other data formats to allow the integration of a variety of data sources. Each week alternates short videos and quizzes, as well as supplementary resources and forums to gradually progress through the different principles and standards. After following this course successfully, you will have the skills to obtain focused and structured datasets from the Web that you can then use to augment you own datasets, enrich their dimensions, feed your applications, perform data mining, machine learning and training, data analysis, AI processing and reasoning and other data management.

Course Syllabus

The RDF data model
SPARQL Query Language
Integration of other data formats and sources

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Course Introduction

Web of Data Ever wonder how websites load and display data? This course will introduce students to the basic concepts of web data and how it is organized on many common web servers. Through a survey of web data structures we will introduce the various facets of web data, including data types, typeset, typesetr, and typesetr. We will cover these topics in the context of the structured and unstructured web data types, which are commonly used in web development. The survey course has been designed to ensure a high-level understanding of web data, and provides the foundation for the course. There is no single "cookie" that must be set to access web data. Rather, a student’s understanding of web data and the structured web data types is needed to complete the web data challenges.Events and Scenarios Web Data Types Structured Web Data Types Writing in JavaScript In the first course of this specialization, we will learn the fundamentals of JavaScript, and begin to implement some of the most common web applications we use every day. We will use a basic version of the JavaScript standard library (JSX), and introduce a couple of the more involved libraries that make JavaScript runnable on modern webpages. We will also learn how to make JavaScript programs run in the browser. We will cover the basics of using the JavaScript object system. You will also learn how to use functions and prototypes

Course Tag

search for occurrences of a query graph publish Linked Open Data on the Web access remotely data sources on the Web use and mix together existing data to obtain new data

Related Wiki Topic

Article Example
Data Web Tim Berners-Lee has suggested that Data Web may be a more appropriate name for the Semantic Web. Tim O'Reilly, who coined the term Web 2.0 has mentioned that the long-term vision of the Semantic Web as a web of data, where sophisticated applications manipulate the data web.
Semantic web data space Data in data spaces are linked across spaces and domains to enhance the meaning of internal data, this supports the work of the linked data project which is part of the Semantic Web effort. This has the benefit of being a useful point for querying about information across domains, and assists the development of a Web of Data.
Data feed The Web is evolving into a web of data or Semantic Web. Data will be encoded by Semantic Web languages like RDF or OWL according to many experts' visions. So, it is not difficult to envision data feeds will be also in the form of RDF or OWL. A big advantage of providing semantic data feeds, i.e. feeding data in Semantic Web standards, is that the data can then be readily consumed and reused by other computers.
Social web Mobile social Web applications are built using various APIs. These APIs allow for the interaction and interconnection of data on one social database, be it Facebook, Twitter, or Google Account, thus creating a literal web of data connections. These applications then add to the user experience specific to the application itself. Examples include TweetDeck and Blogger.
Semantic Web According to the W3C, "The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries". The term was coined by Tim Berners-Lee for a web of data that can be processed by machines.
Semantic Web Stack The Semantic Web is a collaborative movement led by international standards body the World Wide Web Consortium (W3C). The standard promotes common data formats on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web, dominated by unstructured and semi-structured documents into a "web of data". The Semantic Web stack builds on the W3C's Resource Description Framework (RDF).
Data Web The Data Web transforms the Web from a distributed file system into a distributed database system.
Web data services Web data services refers to service-oriented architecture (SOA) applied to data sourced from the World Wide Web and the Internet as a whole. Web data services enable maximal mashup, reuse, and sharing of structured data (such as relational tables), semi-structured information (such as Extensible Markup Language (XML) documents), and unstructured information (such as RSS feeds, content from Web applications, commercial data from online business sources).
Web data services In a Web data services environment, applications may subscribe to and consume information, provide and publish information for others to consume, or both. Applications that can serve as a consumer-subscriber and/or provider-publisher of Web data services include mobile computing, Web portals, enterprise portals, online business software, social media, and social networks. Web data services may support business-to-consumer (B2C) and business-to-business (B2B) information-sharing requirements. Increasingly, enterprises are including Web data services in their SOA implementations, as they integrate mashup-style user-driven information sharing into business intelligence, business process management, predictive analytics, content management, and other applications, according to industry analysts.
Hyperdata Hyperdata are data objects linked to other data objects in other places, as hypertext indicates text linked to other text in other places. Hyperdata enables formation of a web of data, evolving from the "data on the Web" that is not inter-related (or at least, not linked).
Web data services To speed development of Web data services, enterprises can deploy technologies that ease discovery, extraction, movement, transformation, cleansing, normalization, joining, consolidation, access, and presentation of disparate information types from diverse internal sources (such as data warehouses and customer relationship management (CRM) systems) and external sources (such as commercial market data aggregators). Web data services build on industry-standard protocols, interfaces, formats, and integration patterns, such as those used for SOA, Web 2.0, Web-Oriented Architecture, and Representational State Transfer (REST). In addition to operating over the public Internet, Web data services may run solely within corporate intranets, or across B2B supply chains, or even span hosted software-as-a-service (SaaS) or Cloud computing environments.
Semantic Web The concept of the "Semantic Network Model" was formed in the early 1960s by the cognitive scientist Allan M. Collins, linguist M. Ross Quillian and psychologist Elizabeth F. Loftus as a form to represent semantically structured knowledge. When applied in the context of the modern internet, it extends the network of hyperlinked human-readable web pages by inserting machine-readable metadata about pages and how they are related to each other. This enables automated agents to access the Web more intelligently and perform more tasks on behalf of users. The term "Semantic Web" was coined by Tim Berners-Lee, the inventor of the World Wide Web and director of the World Wide Web Consortium ("W3C"), which oversees the development of proposed Semantic Web standards. He defines the Semantic Web as "a web of data that can be processed directly and indirectly by machines".
Recommender system Mobile recommendation systems have also been successfully built using the "Web of Data" as a source for structured information. A good example of such system is SMARTMUSEUM The system uses semantic modelling, information retrieval, and machine learning
Web mining Web Usage Mining is the application of data mining techniques to discover interesting usage patterns from Web data in order to understand and better serve the needs of Web-based applications.
Web analytics The fundamental goal of web analytics is to collect and analyze data related to web traffic and usage patterns. The data mainly come from four sources:
Semantic web data space A semantic web data space is a container for domain specific portable data, which is provided in human and/or machine friendly structures. Data in a data space can be referenced by an identifier, and is linked with other data across spaces and domains, and thus can be viewed in an object-oriented fashion.
Web analytics Other methods of data collection are sometimes used. Packet sniffing collects data by sniffing the network traffic passing between the web server and the outside world. Packet sniffing involves no changes to the web pages or web servers. Integrating web analytics into the web server software itself is also possible. Both these methods claim to provide better real-time data than other methods.
Web mining Usage data captures the identity or origin of Web users along with their browsing behavior at a Web site.
Web scraping Web scraping is used for contact scraping, and as a component of applications used for web indexing, data mining, online price change monitoring and price comparison, product review scraping (to watch the competition), gathering real estate listings, weather data monitoring, website change detection, research, tracking online presence and reputation, web mashup and, web data integration.
Web analytics Off-site web analytics is based on open data analysis, social media exploration, share of voice on web properties.