Introduction to

Introduction to: Linked Data Platform

Nametag: Hello, my name is Linked Data PlatformIn its ongoing mission to lead the World Wide Web to its full potential, the W3C recently released the first specification for an entirely new kind of system. Linked Data Platform 1.0 defines a read-write Linked Data architecture, based on HTTP access to web resources described in RDF. To put that more simply, it proposes a way to work with pure RDF resources almost as if they were web pages.

Because the Linked Data Platform (LDP) builds upon the classic HTTP request and response model, and because it aligns well with things like REST, Ajax, and JSON-LD, mainstream web developers may soon find it much easier to leverage the power and benefits of Linked Data. It’s too early to know how big of an impact it will actually make, but I’m confident that LDP is going to be an important bridge across the ever-shrinking gap between todays Web of hyperlinked documents and the emerging Semantic Web of Linked Data. In today’s post, I’m going to introduce you to this promising newcomer by covering the most salient points of the LDP specification in simple terms. So, let’s begin with the obvious question…

 

What is a Linked Data Platform?

A Linked Data Platform is any client, server, or client/server combination that conforms in whole or in sufficient part to the LDP specification, which defines techniques for working with Linked Data Platform Resources over HTTP. That is to say, it allows Linked Data Platform Resources to be managed using HTTP methods (GET, POST, PUT, etc.). A resource is either something that can be fully represented in RDF or otherwise something like a binary file that may not have a useful RDF representation. When both are managed by an LDP, each is referred to as a Linked Data Platform Resource (LDPR), but further distinguished as either a Linked Data Platform RDF Source (LDP-RS) or a Linked Data Platform Non-RDF Source (LDP-NR).

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Introduction to: Reasoners

Name Tag: Hello, we are ReasonersReasoning is the task of deriving implicit facts from a set of given explicit facts. These facts can be expressed in OWL 2 ontologies and stored RDF triplestores. For example, the following fact: “a Student is a Person,” can be expressed in an ontology, while the fact: “Bob is a Student,” can be stored in a triplestore. A reasoner is a software application that is able to reason. For example, a reasoner is able to infer the following implicit fact: “Bob is a Person.”

Reasoning Tasks

Reasoning tasks considered in OWL 2 are: ontology consistency, class satisfiability, classification, instance checking, and conjunctive query answering.

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Introduction to: OWL Profiles

Name Tag: Hello, we are the OWL familyOWL, the Web Ontology Language has been standardized by W3C as a powerful language to represent knowledge (i.e. ontologies) on the Web. OWL has two functionalities. The first functionality is to express knowledge in an unambiguous way. This is accomplished by representing knowledge as set of concepts within a particular domain and the relationship between these concepts. If we only take into account this functionality, then the goal is very similar to that of UML or Entity-Relationship diagrams. The second functionality is to be able to draw conclusions from the knowledge that has been expressed. In other words, be able to infer implicit knowledge from the explicit knowledge. We call this reasoning and this is what distinguishes OWL from UML or other modeling languages.

OWL evolved from several proposals and became a standard in 2004. This was subsequently extended in 2008 by a second standard version, OWL 2. With OWL, you have the possibility of expressing all kinds of knowledge. The basic building blocks of an ontology are concepts (a.k.a classes) and the relationships between the classes (a.k.a properties).  For example, if we were to create an ontology about a university, the classes would include Student, Professor, Courses while the properties would be isEnrolled, because a Student is enrolled in a Course, and isTaughtBy, because a Professor teaches a Course.

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Introduction to: Triplestores

Badge: Hello, my name is TriplestoreTriplestores are Database Management Systems (DBMS) for data modeled using RDF. Unlike Relational Database Management Systems (RDBMS), which store data in relations (or tables) and are queried using SQL, triplestores store RDF triples and are queried using SPARQL.

A key feature of many triplestores is the ability to do inference. It is important to note that a DBMS typically offers the capacity to deal with concurrency, security, logging, recovery, and updates, in addition to loading and storing data. Not all Triplestores offer all these capabilities (yet).

Triplestore Implementations

Triplestores can be broadly classified in three types categories: Native triplestores, RDBMS-backed triplestores and NoSQL triplestores. Read more

Getting Started with the Semantic Web Using SPARQL with R

A new article on R Bloggers explains how to get “up and running on the Semantic Web” using SPARQL with R in under five minutes. The article states, “We’ll use data at the Data.gov endpoint for this example. Data.gov has a wide array of public data available, making this example generalizable to many other datasets. One of the key challenges of querying a Semantic Web resource is knowing what data is accessible. Sometimes the best way to find this out is to run a simple query with no filters that returns only a few results or to directly view the RDF. Fortunately, information on the data available via Data.gov has been cataloged on a wiki hosted by Rensselaer. We’ll use Dataset 1187 for this example. It’s simple and has interesting data – the total number of wildfires and acres burned per year, 1960-2008.” Read more

New “Linked Data” Book Launches – 50% Discount for Our Readers

Cover of Linked Data book by David Wood et alThis week, Manning Publications is launching the book “Linked Data,” by David Wood, Marsha Zaidman, Luke Ruth, and Michael Hausenblas.

As part of that launch, Manning is offering a one-day 50% discount for readers of SemanticWeb.com. The discount applies to all versions of “Linked Data”: eBook, print books, and Manning’s “MEAP” books (more on MEAP below). To claim the discount, use coupon code “12linksw” when ordering.

This offer expires at 11:59 pm (US EST) on December 6, so if you’re interested, act fast!

About the Book (description by David Wood):

The flexible, unstructured nature of the Web is being extended to act as a global database of structured data. Linked Data is a standards-driven model for representing structured data on the Web that gives developers, publishers, and information architects a consistent, predictable way to publish, merge and consume data. The Linked Data model offers the potential to standardize Web data in the same way that SQL standardized large-scale commercial databases. Linked Data has been adopted by many well-known institutions, including Google, Facebook, IBM, Oracle and government agencies, as well as popular Open Source projects such as Drupal.

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Introduction to: Open World Assumption vs Closed World Assumption

Nametag: Hello, my name is O.W.A.If you are learning about the Semantic Web, one of the things you will hear is that the Semantic Web assumes the Open World. In this post, I will clarify the distinction between the Open World Assumption and the Closed World Assumption.

The Closed World Assumption (CWA) is the assumption that what is not known to be true must be false.

The Open World Assumption (OWA) is the opposite. In other words, it is the assumption that what is not known to be true is simply unknown.

Consider the following statement: “Juan is a citizen of the USA.” Now, what if we were to ask “Is Juan a citizen of Colombia?” Under a CWA, the answer is no. Under the OWA, it is I don’t know.

When do CWA and OWA apply?

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Introduction to: SKOS

Nametag: "Hello, my name is SKOS"SKOS, which stands for Simple Knowledge Organization System, is a W3C standard, based on other Semantic Web standards (RDF and OWL), that provides a way to represent controlled vocabularies, taxonomies and thesauri. Specifically, SKOS itself is an OWL ontology and it can be written out in any RDF syntax.

Before we dive into SKOS, what is the difference between Controlled Vocabulary, Taxonomy and Thesaurus?

controlled vocabulary is a list of terms which a community or organization has agreed upon. For example: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday are the days of the week.

taxonomy is a controlled vocabulary organized in a hierarchy. For example, we can have the terms Computer, Tablet and Laptop and the concepts Tablet and Laptop are subclasses of Computer because a Tablet and Laptop are types of Computers.

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An Introduction to the Semantic Web: The Brass Tacks

Lee Feigenbaum of CMSWire has written an article discussing the “what” and “why” of semantic web technologies. He writes, “In my first article on The Semantic Web and the Modern Enterprise, I introduced the vision of the Semantic Web. I also discussed how the progress made while working towards that vision provides a strong foundation to help enterprises better deal with their information management challenges. In this article, we’ll take a high-level look at what the core Semantic Web technologies are, why they’re different from conventional technology approaches and how they deliver tangible benefits for enterprise information management.” Read more

Introduction to: RDF vs XML

 There has always been a misconception between the relationship of RDF and XML. The main difference: XML is a syntax while RDF is a data model.

RDF has several syntaxes (Turtle, N3, etc) and XML is one of those (known as RDF/XML). Actually, RDF/XML is the only W3C standard syntax for RDF (Currently, there is Last Call on Turtle, a new W3C standard syntax for RDF). Therefore, comparing XML and RDF is like comparing apples with oranges. What can be compared is their data models. The RDF data model is a graph while the XML data model is a tree.

Comparing RDF with XML

Joshua Tauberer has an excellent comparison between RDF and XML, which I recommend. Two advantages of RDF are highlighted: flexibility of the data model and use of URIs as global unique identifiers.

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