SPARQL is a query language for retrieving and manipulating data stored in RDF format. It allows users to write queries against remote SPARQL endpoints to query RDF triples stored in a database. SPARQL queries are composed of triple patterns, similar to RDF triples, that can include variables to retrieve variable bindings from the queried data. Query results are returned as solutions that assign values to the variables. Common queries include SELECT, ASK, CONSTRUCT, and DESCRIBE. SPARQL endpoints provide programmatic access to issue SPARQL queries against remote SPARQL-accessible stores.
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/
Two graph data models : RDF and Property Graphsandyseaborne
Talk given at ApacheConEU Big Data 2015.
This talk describes the two common graph data approaches, RDF and Property Graphs. It concludes with observations about the different emphasis of each and where each is focused.
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/
Two graph data models : RDF and Property Graphsandyseaborne
Talk given at ApacheConEU Big Data 2015.
This talk describes the two common graph data approaches, RDF and Property Graphs. It concludes with observations about the different emphasis of each and where each is focused.
This is a lecture note #10 for my class of Graduate School of Yonsei University, Korea.
It describes SPARQL to retrieve and manipulate data stored in Resource Description Framework format
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
Semantic Web technologies (such as RDF and SPARQL) excel at bringing together diverse data in a world of independent data publishers and consumers. Common ontologies help to arrive at a shared understanding of the intended meaning of data.
However, they don’t address one critically important issue: What does it mean for data to be complete and/or valid? Semantic knowledge graphs without a shared notion of completeness and validity quickly turn into a Big Ball of Data Mud.
The Shapes Constraint Language (SHACL), an upcoming W3C standard, promises to help solve this problem. By keeping semantics separate from validity, SHACL makes it possible to resolve a slew of data quality and data exchange issues.
Presented at the Lotico Berlin Semantic Web Meetup.
Although RDF can be considered the corner stone of semantic web and knowledge graphs, it has not been embraced by everyday programmers and software architects who want 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 briefly introduce both technologies using some examples and compare them. We will also present some challenges and applications related with RDF data shapes.
Talk given at: KTH Royal Institute of Technology, School of Industrial Engineering and Management, Mechatronics Division, 7th February, 2020
This is a lecture note #10 for my class of Graduate School of Yonsei University, Korea.
It describes SPARQL to retrieve and manipulate data stored in Resource Description Framework format
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
Semantic Web technologies (such as RDF and SPARQL) excel at bringing together diverse data in a world of independent data publishers and consumers. Common ontologies help to arrive at a shared understanding of the intended meaning of data.
However, they don’t address one critically important issue: What does it mean for data to be complete and/or valid? Semantic knowledge graphs without a shared notion of completeness and validity quickly turn into a Big Ball of Data Mud.
The Shapes Constraint Language (SHACL), an upcoming W3C standard, promises to help solve this problem. By keeping semantics separate from validity, SHACL makes it possible to resolve a slew of data quality and data exchange issues.
Presented at the Lotico Berlin Semantic Web Meetup.
Although RDF can be considered the corner stone of semantic web and knowledge graphs, it has not been embraced by everyday programmers and software architects who want 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 briefly introduce both technologies using some examples and compare them. We will also present some challenges and applications related with RDF data shapes.
Talk given at: KTH Royal Institute of Technology, School of Industrial Engineering and Management, Mechatronics Division, 7th February, 2020
Global & local oxygen control in in vitro systemsMAASTRO clinic
Lecture by Humbert Flamm in the context of the Course: "Tumour Hypoxia: From Biology to Therapy III".
For the complete e-Course see http://www.myhaikuclass.com/MaastroClinic/metoxia
THIS MAGAZINE BRINGS TOGETHER PREMIUM PRODUCT MANUFACTURERS AND LEADING BUILDERS TO CREATE BETTER, DIFFERENTIATED HOMES AND BUILDINGS THAT USE LESS ENERGY, SAVE WATER AND REDUCE OUR
IMPACT ON THE ENVIRONMENT.
In this presentation, Pragash talks about an automatic DD vending machine that uses ATM card for authentication. This will save time and effort for the bank and the customers will be saved from the long queues.
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
This webinar will break the roadblocks that prevent many from reaping the benefits of heavyweight Semantic Technology in small scale projects. We will show you how to build Semantic Search & Analytics proof of concepts by using managed services in the Cloud.
Re-using Media on the Web: Media fragment re-mixing and playoutMediaMixerCommunity
A number of novel application ideas will be introduced based on the media fragment creation, specification and rights management technologies. Semantic search and retrieval allows us to organize sets of fragments by topical or conceptual relevance. These fragment sets can then be played out in a non-linear fashion to create a new media re-mix. We look at a server-client implementation supporting Media Fragments, before allowing the participants to take the sets of media they have selected and create their own re-mix.
Wi2015 - Clustering of Linked Open Data - the LODeX toolLaura Po
Presentation of the tool LODeX (http://www.dbgroup.unimore.it/lodex2/testCluster) at the 2015 IEEE/WIC/ACM International Conference on Web Intelligence, Singapore, December 6-8, 2015
This is part 2 of the ISWC 2009 tutorial on the GoodRelations ontology and RDFa for e-commerce on the Web of Linked Data.
See also
http://www.ebusiness-unibw.org/wiki/Web_of_Data_for_E-Commerce_Tutorial_ISWC2009
This is part 2 of the ISWC 2009 tutorial on the GoodRelations ontology and RDFa for e-commerce on the Web of Linked Data.
See also
http://www.ebusiness-unibw.org/wiki/Web_of_Data_for_E-Commerce_Tutorial_ISWC2009
These slides were presented as part of a W3C tutorial at the CSHALS 2010 conference (http://www.iscb.org/cshals2010). The slides are adapted from a longer introduction to the Semantic Web available at http://www.slideshare.net/LeeFeigenbaum/semantic-web-landscape-2009 .
A PDF version of the slides is available at http://thefigtrees.net/lee/sw/cshals/cshals-w3c-semantic-web-tutorial.pdf .
a system called natural language interface which transforms user's natural language question into SPARQL query
find related papers here https://sites.google.com/site/fadhlinams81/publication
Usage of Linked Data: Introduction and Application ScenariosEUCLID project
This presentation introduces the main principles of Linked Data, the underlying technologies and background standards. It provides basic knowledge for how data can be published over the Web, how it can be queried, and what are the possible use cases and benefits. As an example, we use the development of a music portal (based on the MusicBrainz dataset), which facilitates access to a wide range of information and multimedia resources relating to music.
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
3. Definitions and Motivation
• Statement (or triple):
– Small piece of knowledge (a single fact).
– It has Subject-Predicate-Object.
– Ex. Evidence A is_a physiotherapy evidence.
– Ex. <subj0> <pred0> <obj0>
• Subject (resource) and Object (value):
– Names for things in the world.
• Predicate (property):
– Name of a relation that connects two things.
3
4. Definitions and Motivation
• Semantic Web:
– It is built on top of the current Web.
– Besides the HTML constructs, it contains some
“statements” that can be collected by an agent.
– The agent organizes and connects the statements
into a graph format (data integration).
– Automatic data integration on the Web can be
powerful and can help a lot when it comes to
information discovery and retrieval.
4
5. Definitions and Motivation
• Query-based-language:
– The agent should be able to process some
common queries that are submitted against the
statements it has collected. After all, without
providing a query interface, the collected
statements will not be of too much use to us.
5
6. Definitions and Motivation
• Linked Data:
– A collection of machine-understandable
statements, published without having them
related to any Web site at all.
• Web of Data:
– Interchangeable terms for the Semantic Web.
6
7. Definitions and Motivation
• Resource description framework (RDF):
– The building block for the Semantic Web.
– Standard for encoding metadata.
• Metadata: describe the data contained on the Web.
• Machine understandable (also interoperability).
• Domain independent.
– Describe any resources and their relations existing
in the real world.
– RDF is for the Semantic Web what HTML has been
for the Web.
7
8. Definitions and Motivation
• RDF Schema (RDFS):
– Stands for RDF Schema.
– Common language, or, a vocabulary, where
classes, sub-classes, properties, and also relation
between the classes and properties are defined.
– Domain-specific.
– Allow the creation of distributed RDF documents.
8
9. Definitions and Motivation
• Web Ontology Language (OWL):
– Is the most popular language to use when creating
ontologies.
– Is build upon RDF Schema.
– Has the same purpose as RDF Schema.
• Classes, properties, and their relationships for a specific
application domain.
– Provides the capability to express much more
complex and richer relationships (better
expressiveness).
9
10. Definitions and Motivation
• Web Ontology Language (OWL):
– Axiom: basic statement (basic piece of
knowledge).
– A collection of axioms is an OWL Ontology.
– Protégé is free OWL editor.
• IRI:
– Stands for Internationalized Resource Identifiers
(like URIs with Unicode characters).
10
11. Definitions and Motivation
• Computer Ontology:
– Reflects the structure of the world.
– Is often about structure of the concepts.
– Each statement collected by an agent represents a
piece of knowledge. Therefore, there has to be a
way (a model) to represent knowledge on the
Web. Furthermore, this model of representing
knowledge has to be easily and readily processed
(understood) by machines.
11
12. Definitions and Motivation
• Computer Ontology:
– An application can understand a given ontology;
that means the application can parse the ontology
and create a list of axioms based on the ontology,
and all the facts are expressed as RDF statements.
12
14. SPARQL
• SPARQL: Querying the Semantic Web.
– Pronounced “splarkle”
– Stands for SPARQL Protocol and RDF Query
Language.
– Locate specific information on the machine-
readable Web.
– The Web can be viewed as a gigantic database.
14
15. SPARQL (cont)
• Related concepts:
– RDF data store: is a special database system built
for the storage and retrieval of RDF statements.
• Every record is a short statement in the form of
subject-predicate-object.
• Store RDF statements and retrieve them by using a
query language.
– Triple pattern: any or all the subject, predicate,
and object values can be a variable.
• <http://danbri.org/foaf.rdf#danbri> foaf:name ?name.
15
16. SPARQL (cont)
• Graph pattern: is used to select triples from a
given RDF graph.
– Is a collection of triple patterns.
– { ?who foaf:name ?name.
?who foaf:interest ?interest.
?who foaf:knows ?others. }
– Note: FOAF (friend of a friend) is an Ontology (a
group of properties that describes a person) and a
collection of RDF statements.
16
17. SPARQL (cont)
• SPARQL engine:
– Tries to match the triples contained in the graph
patterns against the RDF graph, which is a
collection of triples.
– Once a match is successful, it will bind the graph
pattern’s variables to the graph’s nodes, and one
such variable binding is called a query solution.
17
18. SPARQL (cont)
• SPARQL endpoint:
– Interface that users (human or apps) can access to
query an RDF data store by using SPARQL query
language.
• Web-based application.
• Set of APIs that can be used by an agent.
– Ex. Joseki Web-based SPARQL.
• http://sparql.org/sparql.html
18
20. SPARQL (cont)
• Structure of a SELECT Query:
– # base directive
BASE <URI>
# list of prefixes
PREFIX pref: <URI>
...
# result description
SELECT...
# graph to search
FROM . . .
# query pattern
WHERE {
...
}
# query modifiers
ORDER BY... 20
21. SPARQL (cont)
• Ex. Find all the picture formats used by Dan Brickley’s friends (from graph
http://danbri.org/foaf.rdf#danbri).
21
22. SPARQL (cont)
• The query finds all the picture format used by Dan Bricley's friends.
• Base define the source file (graph) which link is: http...
• prefix define the ontology of persons foaf which link is: http...
• the other prefix define the image format ontology dc which link is: http...
• select * from the source graph
• where is defined the term dambri in the foaf ontology throw the knows
attribute and store that information in the variable ?friend
• where ?friend has a description of the image throw the attribute
foaf:depiction and store that information in the variable ?picture
• where ?picture has the name of the image format throw attribute
dc:format and store the name in the variable ?imageFormat
22
24. SPARQL (cont)
• Optional keyword: is needed because RDF
data graph is only a semi-structured data
model.
– i.e. two instances of the same class type in a given
RDF graph may have different set of property
instances created for each one of them.
– The query says, find all the people known by Dan
Brickley and show their name, e-mail, and home
page information if any of this information is
available.
24
27. SPARQL (cont)
• Solution modifiers:
– Distinct: eliminate duplicate solutions from the
result.
– Order by:
• Asc (): ascending.
• Desc (): descending.
– Limit: set the maximum number of solutions.
– Offset: sets the number of solutions to be skipped.
27
29. SPARQL (cont)
• Union keyword:
– A query expressed by multiple graph patterns that
are mutually exclusive, and any solution will have
to match exactly one of these patterns
(alternative match).
29
31. SPARQL (cont)
• Construct query:
– Returns a new RDF graph.
• Describe query:
– Return an RDF graph whose statement are
determined by the query processor.
• Ask query:
– The query processor simply returns a true or false
value.
31
33. SPARQL (cont)
• Other operators and functions:
– NOT EXISTS
– MINUS
– Concat() : for expressions in a query.
– INSERT DATA
– DELETE DATA
– CREATE [SILENT] GRAPH <uri>
– DROP [SILENT] GRAPH <uri>
33
34. References
• Liyang Yu (2011). A Developer’s Guide to the
Semantic Web. Springer. ISBN: 978-3-642-
15969-5.
• (2011) Huang J, Abadi D.J. and Ren K. Scalable
SPARQL Querying of Large RDF Graphs.
34