The document discusses the Semantic Web and RDF data formats. It provides an overview of RDF syntaxes like RDF/XML, N3, N-Triples, RDF/JSON, and RDFa. It also discusses software APIs for working with RDF data in languages like Java, PHP, and Ruby. The document outlines handling RDF data using statement-centric, resource-centric, and ontology-centric models, as well as named graphs. It provides examples of reading RDF data from files and querying RDF data using SPARQL.
This document provides an overview of the Resource Description Framework (RDF). It begins with background information on RDF including URIs, URLs, IRIs and QNames. It then describes the RDF data model, noting that RDF is a schema-less data model featuring unambiguous identifiers and named relations between pairs of resources. It also explains that RDF graphs are sets of triples consisting of a subject, predicate and object. The document also covers RDF syntax using Turtle and literals, as well as modeling with RDF. It concludes with a brief overview of common RDF tools including Jena.
The document discusses representing data in the Resource Description Framework (RDF). It describes how relational data can be represented as RDF triples with rows becoming subjects, columns becoming properties, and values becoming objects. It also discusses using URIs instead of internal IDs and names to allow data integration. The document then covers serializing RDF data in different formats like RDF/XML, N-Triples, N3, and Turtle and describes syntax for representing literals, language tags, and abbreviating subject and predicate pairs.
This document provides an overview of software architectures for semantic web applications, including local access, mixed access, and remote access architectures. Local access architectures involve storing and querying RDF data locally using a triplestore and API. Remote access architectures involve querying RDF data owned by a third party using the SPARQL Protocol over HTTP or SOAP. The SPARQL Protocol is an abstract specification for remotely executing SPARQL queries in a standards-based way.
The forth lecture of the course I'm giving on "Interoperability and Semantic Technologies" at Politecnico di Milano in the academic year 2015-16. It presents an introduction to RDF. It starts presenting the data model. Then it presents the turtle serialization. It compares XML vs. RDF. Finally, it provides few informations about RDFa and Linked Data.
SPARQL 1.1 introduced several new features including:
- Updated versions of the SPARQL Query and Protocol specifications
- A SPARQL Update language for modifying RDF graphs
- A protocol for managing RDF graphs over HTTP
- Service descriptions for describing SPARQL endpoints
- Basic federated query capabilities
- Other minor features and extensions
The document discusses ontologies and the RDF-S/OWL languages for defining ontologies. It defines ontologies as formal, explicit specifications of shared conceptualizations and describes some key parts of ontologies including concepts, relations, instances, and axioms. It provides an example ontology about artists and the works they create. RDF-S semantics are discussed for defining subclasses, subproperties, domains, and ranges within an ontology.
An introduction to Semantic Web and Linked DataFabien Gandon
Here are the steps to answer this SPARQL query against the given RDF base:
1. The query asks for all ?name values where there is a triple with predicate "name" and another triple with the same subject and predicate "email".
2. In the base, _:b is the only resource that has both a "name" and "email" triple.
3. _:b has the name "Thomas".
Therefore, the only result of the query is ?name = "Thomas".
So the result of the SPARQL query is:
?name
"Thomas"
FedX - Optimization Techniques for Federated Query Processing on Linked Dataaschwarte
The final slides of our talk about FedX at the 10th International Semantic Web Conference in Bonn. For details about FedX see http://www.fluidops.com/fedx/
This document discusses the need for named graphs in RDF to represent contextual information like provenance and source of RDF data. It proposes extensions to the RDF/XML syntax to associate RDF descriptions and statements with named graphs. This allows modeling things like different hypotheses, temporal aspects, points of view, and distributed storage in a way that is currently not possible without named graphs in the RDF model.
This document discusses programming with Linked Open Data (LOD) using the Ruby programming language. It provides an overview of LOD principles and demonstrates how to read, write, load, merge and query RDF data using the RDF.rb library in Ruby. Code examples are provided to illustrate how to retrieve and inspect RDF statements from DBpedia, serialize and write RDF in different formats, load RDF graphs from multiple sources, and perform basic SPARQL queries.
Although RDF is a corner stone of semantic web and knowledge graphs, it has not been embraced by everyday programmers and software architects who need to safely create and access well-structured data. There is a lack of common tools and methodologies that are available in more conventional settings to improve data quality by defining schemas that can later be validated. Two technologies have recently been proposed for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL). In the talk, we will review the history and motivation of both technologies. We will also and enumerate some challenges and future work with regards to RDF validation.
ShEx is a language for validating RDF data. It allows defining shapes that specify constraints on nodes and triples. ShEx expressions can be used to validate if RDF graphs conform to the defined shapes. The ShEx language is inspired by languages like RelaxNG and provides different serialization formats like ShExC, ShExJ, and ShExR. There are open-source implementations of ShEx validators in languages like JavaScript, Scala, Ruby, Python, and Java. ShEx provides a concise way to define RDF shapes and validate instance data against those shapes.
This document provides an overview of SPARQL, the query language for the Semantic Web. SPARQL allows querying RDF data by matching triple patterns and combining them with operations like optional and union patterns. Key features discussed include the anatomy of SPARQL queries, matching RDF literals and numerical values, filtering solutions, and defining datasets with the FROM clause. The document also covers SPARQL result forms and resources for learning more about SPARQL implementations and extensions.
- SPARQL is a query language for retrieving and manipulating data stored in RDF format. It is similar to SQL but for RDF data.
- SPARQL queries contain prefix declarations, specify a dataset using FROM, and include a graph pattern in the WHERE clause to match triples.
- The main types of SPARQL queries are SELECT, ASK, DESCRIBE, and CONSTRUCT. SELECT returns variable bindings, ASK returns a boolean, DESCRIBE returns a description of a resource, and CONSTRUCT generates an RDF graph.
The document discusses the Semantic Web, providing an overview of identification languages, integration, storage and querying, browsing and viewing technologies. It describes languages like RDF, RDF Schema and OWL, and how they add machine-understandable semantics and shared ontologies to the web. It also discusses tools for querying, visualizing and presenting Semantic Web data like SPARQL, RDF browsers, Fresnel lenses, and Yahoo Pipes for aggregating and filtering RDF feeds.
The document discusses Semantic Web technologies including RDF, SPARQL and ontologies. It provides:
1) An introduction to the Semantic Web vision of machines being able to understand and respond to complex requests based on meaning. This requires information to be semantically structured.
2) A brief overview of key concepts in RDF including triples, nodes, blank nodes, and predefined RDF structures like bags and lists.
3) An explanation of the SPARQL query language, which is similar to SQL but interrogates the Semantic Web. SPARQL clauses like SELECT, CONSTRUCT, DESCRIBE and ASK are covered.
4) A discussion of ontological representations including R
This query will not return any results. The pattern specified in the WHERE clause contains two triples, but the second triple contains a syntax error - it is missing the property between ?x and ?ema. A valid property like email would need to be specified, such as:
SELECT ?name WHERE {
?x name ?name .
?x email ?email
}
This query will select and return the ?name of any resources ?x that have both a name and email property specified.
This document provides an overview of the Semantic Web, RDF, SPARQL, and triplestores. It discusses how RDF structures and links data using subject-predicate-object triples. SPARQL is introduced as a standard query language for retrieving and manipulating data stored in RDF format. Popular triplestore implementations like Apache Jena and applications of linked data like DBPedia are also summarized.
Semantic Technology Solutions For Recovery Gov And Data Gov With Transparenc...Mills Davis
The Obama administration has set the goal of achieving and unprecedented level of openness, participation, transparency, and collaboration in government. This applies especially to the accessibility of government information and the tracking of stimulus expenditures. This presentation discusses ways that cloud computing, web 2.0, and web 3.0 semantic technologies can be used to deliver citizen-friendly solutions for recovery.gov and data.gov that fulfill the goals of the new administration.
From the Feb 19 2014 NISO Virtual Conference: The Semantic Web Coming of Age: Technologies and Implementations
The Web of Data - Ralph Swick, Domain Lead of the Information and Knowledge Domain at W3C
Semantic Web 2.0: Creating Social Semantic Information SpacesJohn Breslin
This tutorial provides an overview of applying Semantic Web technologies to emerging Web 2.0 applications and social media to create "Social Semantic Information Spaces." It discusses adding semantics to blogs, wikis, forums, and social networks through standards like RDF and ontologies. The goal is to overcome limitations of these applications and enable more automated information sharing and discovery across interconnected sites and communities.
W3C Tutorial on Semantic Web and Linked Data at WWW 2013Fabien Gandon
The document provides an introduction to Semantic Web and Linked Data. It discusses key concepts such as RDF, which represents data as subject-predicate-object triples that can be connected to form a graph. RDF has several syntaxes including XML, Turtle, and JSON. Properties in RDF triples can link to other resources or contain literal values. Types are identified with URIs and vocabularies are extensible. The goal of Linked Data is to publish structured data on the web and link it to other data to form a global data web.
Intro to the Semantic Web Landscape - 2011LeeFeigenbaum
An introduction to the Semantic Web landscape as it stands near the end of 2011. Includes an introduction to the core technologies in the Semantic Web technology stack.
This material was presented at the November, 2011, Cambridge Semantic Web meetup.
The document introduces the Semantic Web and how it allows for the integration and merging of disparate datasets. It provides an example of merging two bookstore datasets that have similar information but are structured differently. By exporting the datasets as RDF triples, mapping identical resources, and adding a few statements to link equivalent terms, the datasets can be merged. This allows for new queries to be answered by combining information from both original datasets. The Semantic Web provides technologies to automate this kind of data integration and enable more powerful queries across multiple sources of data.
These slides were originally a tutorial presented for the SIG preceding the May 2009 meeting of the PRISM Forum.
They attempt to give a survey of the technologies, tools, and state of the world with respect to the Semantic Web as of the first half of 2009.
The document discusses the Semantic Web and metadata standards. It describes the Semantic Web as a web of data that can be processed by machines. It explains how the Semantic Web is being developed both top-down through more intelligent applications and bottom-up through increased use of structured data formats and standards like URIs, RDF, and OWL. It provides examples of applications using these standards and discusses metadata standards like RDA, DCMI, and their relationship.
This document provides an introduction to the Semantic Web, covering topics such as what the Semantic Web is, how semantic data is represented and stored, querying semantic data using SPARQL, and who is implementing Semantic Web technologies. The presentation includes definitions of key concepts, examples to illustrate technical aspects, and discussions of how the Semantic Web compares to other technologies. Major companies implementing aspects of the Semantic Web are highlighted.
The document discusses semantic computing and its benefits. It provides an agenda for introducing semantic software, IoT/big data, and semantic computing concepts. Semantic computing transforms unstructured data into structured triples that can be queried using ontologies to add context and meaning. It discusses how semantic computing supports applications in various domains like finance, government, and healthcare by integrating diverse data sources and enabling expanded analytics. The US Navy case study shows how semantic computing helped the Navy reduce energy costs.
The document discusses the Semantic Web as Web 3.0. It explains that while current web pages use HTML to describe structure, not meaning, the Semantic Web aims to allow computers to understand the meaning behind information by recognizing things like people, places, events. This is done through techniques like embedding semantic annotations directly into data using standards like RDFa, microformats, and querying data with SPARQL. The Semantic Web will enable new applications by making the web more machine-readable.
The document discusses semantic computing and its benefits. It provides an agenda for introducing semantic software, including discussions of IoT, big data, semantic concepts, and benefits for government. Key concepts explained are semantic interoperability, ontologies, and the semantic web stack. Examples are given of relationship queries and applications of semantic computing in finance, healthcare, and for the US Navy to improve energy efficiency. Semantic computing can integrate diverse data sources, perform advanced analytics, and provide personalized insights.
Semantic Software is an Australian cognitive computing company that has developed a comprehensive semantic computing platform to help analyze large amounts of data. Their platform uses various AI techniques like deep learning, machine learning, natural language processing, and semantic computing with reasoning and inferencing to help computers analyze data without needing as much human input. They believe semantic computing is key to truly cognitive applications and that their platform is one of the most advanced for these purposes.
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How to Make Awesome SlideShares: Tips & TricksSlideShare
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This document provides an overview of semantic web technologies including the Resource Description Framework (RDF), which is a standard model for data interchange on the web. It describes RDF concepts like triples, graphs, and syntaxes like RDF/XML and Turtle. It also covers ontologies, SPARQL for querying RDF data, linked data, and tools for working with RDF and semantic web technologies.
This document summarizes SPARQL, the SPARQL query language used for querying and retrieving data stored in RDF format. It discusses key concepts such as RDF, terms, syntax, patterns, and constraints. RDF represents information as subject-predicate-object triples that can be queried using SPARQL. SPARQL allows constructing basic and complex graph patterns to match against the RDF graph. It also supports value filters, ordering, pagination and other solution modifiers. The document provides examples of SPARQL queries to retrieve data from RDF graphs based on different conditions and constraints.
This document discusses using Linked Open Data and RDF with Ruby. It provides an overview of RDF support in Ruby including libraries for reading, writing, querying, and storing RDF data. It also demonstrates how to perform basic RDF graph manipulation and querying using these libraries. Resources for additional documentation and examples using Ruby for semantic web applications are also mentioned.
RDFa is a method for embedding Rich Data Formats metadata within HTML documents. It allows metadata like titles, descriptions and URLs to be added to HTML pages in a way that is readable both by humans and machines. The summary describes how RDFa works by defining things with URIs and assigning them properties and values as triples. It also mentions the RDFa distiller tool that can extract the RDF metadata from HTML pages marked up with RDFa.
RDFa Introductory Course Session 2/4 How RDFaPlatypus
RDFa is a method for embedding Rich Data Formats metadata within HTML documents. It allows metadata like titles, descriptions and URLs to be added to HTML pages in a way that is readable both by humans and machines. The summary describes how RDFa works by defining resources with URIs and properties, and how this extracted data can be distilled and validated using various RDFa tools on the W3C website.
Tutorial on RDFa, to be held at ISWC2010 in Shanghai, China. (I was supposed to hold the tutorial but last minute issues made it impossible for me to travel there...)
A Hands On Overview Of The Semantic WebShamod Lacoul
The document provides an overview of the Semantic Web and introduces key concepts such as RDF, RDFS, SPARQL, OWL, and Linked Open Data. It begins with defining what the Semantic Web is, why it is useful, and how it differs from the traditional web by linking data rather than documents. It then covers RDF for representing data, RDFS for defining schemas, and SPARQL for querying RDF data. The document also discusses OWL for building ontologies and Linked Open Data initiatives that have published billions of RDF triples on the web.
This presentation is an introduction to RDFa, as the fourth assignment of the IST 681 in iSchool, Syracuse University. The presentation is made by Kai Li, who is a library student in Syracuse University..
The document discusses two graph data models: RDF and Property Graphs. It provides an overview of each model, including examples and technologies used to access and query each type of graph. The key conclusions are that RDF emphasizes information modeling and knowledge graphs, while Property Graphs emphasize data syntax and graph algorithms. Simply layering the data models on top of each other leads to dissatisfaction, but the models could potentially share technologies while still keeping their separate focuses and tools.
RDF handles limitations of XML by providing machine understandable documents that describe relationships between entities. The basic RDF model has three components - resources, properties, and statements. RDF can be represented using XML syntax or other syntactic representations. The RDF model represents relationships using a directed graph of these three components. Query languages like RQL and SPARQL can be used to query and retrieve information from RDF graphs.
This document introduces SPARQL, the SPARQL query language used to retrieve and manipulate RDF data. It provides an example SPARQL query to return full names from a sample RDF graph. It then describes what a SPARQL Service Description is, which is a vocabulary for discovering and describing SPARQL services and endpoints. It outlines several properties and classes used in SPARQL Service Descriptions.
SPARQL is a standard query language for retrieving and manipulating data stored in RDF format. It consists of three parts: a query language, a result format, and an access protocol. The query language uses graph patterns to match against RDF graphs. It supports keywords like SELECT, FROM, and WHERE to identify values to return, data sources, and triple patterns to match. SPARQL can be run over HTTP or SOAP and returns XML results. It provides a unified method for querying RDF data distributed across the web.
This document provides an overview and introduction to RDFa, which allows embedding RDF data into HTML and other XML-based languages like XHTML and SVG. It discusses RDFa Lite syntax including vocab, prefix, property and typeof. It also covers RDFa Core syntax, including about, rel, href, and datatype. The goal of RDFa is to embed RDF data into documents while maintaining document structure and semantics.
The document provides an overview of the semantic web including its goals of making data meaningful and discoverable. It discusses approaches to building the semantic web such as RDF, RDFS, OWL, and SPARQL. It also covers microformats as a more practical approach and provides examples of using RDF, OWL, SPARQL, and various microformats.
The document discusses linked data and how it can be used to share information on the web in a structured format. It provides an overview of linked data and the Resource Description Framework (RDF), describes how URIs can be used to name things and link data on the web, and gives examples of publishing and querying linked data using RDF and SPARQL. Recent developments in using linked data by Facebook, Google, and other companies are also mentioned.
This document provides an introduction to Resource Description Framework (RDF) and RDF XML. It defines key RDF concepts like URI references, qualified names, basic RDF triples, RDF graphs, and RDF Schema. It also explains how to represent RDF models and descriptions in RDF XML format using elements like rdf:RDF, rdf:Description, and properties. Examples are provided to illustrate RDF triples and RDF XML representations.
The document discusses using JSON-LD and RDF to add semantic meaning to web APIs while maintaining compatibility with existing JSON formats. It explains how RDF uses triples to make statements about resources, and how JSON-LD allows embedding RDF semantics in JSON without changing the format. This allows merging data from multiple sources and facilitates data interchange and evolution of schemas over time.
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2011 4IZ440 Semantic Web – RDF, SPARQL, and software APIs
1. The
Seman)c
Web
RDF,
SPARQL
and
so0ware
APIs
4IZ440
Knowledge
Representa4on
and
Reasoning
on
the
WWW
Josef
Petrák
|
me@jspetrak.name
2. Worth
a
note
§ Open
Data
iniDave
around
world
and
Europe
open
staDsDcal
and
government
data,
usually
in
RDF
format.
§ It
has
several
success
stories
§ hLp://headtoweb.posterous.com/open-‐data-‐success-‐stories
§ Google
is
integraDng
RDF
encoded
as
RDFa
in
pages
into
its
search
results.
§ UEP
is
not
behind
–
DIKE
research
group
KEG
started
Czech–Slovak
semanDc
iniDaDve
called
SemanDcs
§ And
we
have
a
RDF-‐based
website
deployed,
see
hLp://keg.vse.cz/
3. Outline
§ RDF
representaDon
formats
§ Data
handling
approaches
§ So0ware
APIs
overview
§ Approaches
in
examples
§ MoDvaDon
example:
web
applicaDon
5. Syntaxes
§ RDF
has
several
syntaxes.
§ A
“graph”
is
the
reference
syntax.
§ The
W3C
endorsed
file
format
is
RDF/XML
§ Other
file
formats:
§ N-‐Triples
§ N3
§ RDF/JSON
§ RDFa
6. RDF
graph
rdf:type
gd:Person
gd:name
John
Example
gd:affiliaDon
Sample
Corp.
Inc.
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX gd: <http://rdf.data-vocabulary.org/#>
16. RDF
models
§ Statement–centric
§ Working
with
triples
of
?subject
?predicate
?object
§ Resource–centric
§ Working
with
resources
having
properDes
and
their
values
§ Ontology–centric
§ Working
with
classes,
properDes,
and
individuals
as
defined
in
selected
vocabulary/schema/ontology
§ Named
graph
§ Triples
belongs
to
a
graph
with
URI
name
§ Working
with
quads
of
?graph
?subject
?predicate
?object
22. File-‐read
§ Task:
Read
RDF
data
from
a
NotaDon3
file
format
into
memory
model.
23. Read
N3
(Jena)
InputStream
is
=
FileManager.get().open("samples/people.n3");
Model
model
=
ModelFactory.createDefaultModel();
RDFReader
r
=
model.getReader("N3");
r.read(model,
is,
null);
is.close();
26. Read
N3
(RAP)
$model
=
ModelFactory::getDefaultModel();
$model-‐>load('samples/people.n3');
§ This
code
won’t
run,
because
there
is
N3Reader
bug.
§ Works
also
for
RDF/XML
and
N–Triples
formats.
27. Traverse
triples
§ Task:
Take
a
memory
model
and
list
all
or
parDcular
RDF
triples.
28. Traverse
triples
(Jena)
Model
model
=
ModelFactory.createDefaultModel();
//
Load
data
into
model
StmtIterator
i
=
model.listStatements();
while
(i.hasNext())
{
Statement
stmt
=
i.nextStatement();
Resource
subject
=
stmt.getSubject();
Property
predicate
=
stmt.getPredicate();
RDFNode
object
=
stmt.getObject();
//
Printing
out
}
29. Query
Model
(Jena)
Resource
rPerson
=
model.getResource("http://rdf.data-‐
vocabulary.org/#Person");
Property
rName
=
model.getProperty("http://rdf.data-‐vocabulary.org/
#name");
StmtIterator
si
=
model.listStatements(null,
RDF.type,
rPerson);
while
(si.hasNext())
{
Resource
r
=
si.nextStatement().getSubject();
StmtIterator
sii
=
model.listStatements(r,
rName,
(RDFNode)null);
if
(sii.hasNext())
{
System.out.println(sii.nextStatement().getObject().toString());
}
}
31. Query
model
(RDF.rb)
GD
=
RDF::Vocabulary.new('http://rdf.data-‐vocabulary.org/#')
repository
=
RDF::Repository.new
#
Load
data
query
=
RDF::Query.new({
:person
=>
{
RDF.type
=>
GD.Person,
GD.name
=>
:name
}
})
query.execute(graph).each
do
|person|
puts
"name=#{person.name}"
end
32. Query
SPARQL
Endpoint
§ Task:
Query
a
SPARQL
endpoint
and
write
out
the
data.
E.g.
from
DBPedia,
write
one
random
blackboard
gag
wriLen
by
Bart
Simpson.
39. Knowledge
Engineering
Group
Website
§ hLp://keg.vse.cz/
§ RDF
outside
§ Data
dumps
in
RDF/XML
§ Web
pages
enriched
with
RDFa
§ RDF
inside
§ Data
created
by
SPARQL
INSERT
§ Data
queried
by
SPARQL
SELECT
§ Data
updated
by
SPARQL
DELETE
and
SELECT
§ Data
manipulated
by
user–friendly
forms
§ Ongoing:
data
integraDon
with
ISIS
VŠE
and
other
department
applicaDons
43. References
§ hLp://dsic.zapisky.info/RDF/FOAF/parsingWithPHP/
§ hLp://zapisky.info/?item=zverejnime-‐akademicke-‐projekty-‐
samozrejme-‐semanDcky
§ BOOK
–
John
Hebeler
(Author),
MaLhew
Fisher
(Author),
Ryan
Blace
(Author),
Andrew
Perez-‐Lopez
(Author),
Mike
Dean
(Foreword):
Seman4c
Web
Programming,
Wiley,
2009