This document provides an overview of the Web Ontology Language (OWL). It discusses OWL's purpose in extending RDF Schema to provide a full knowledge representation language for the web. It outlines OWL's key features such as logical expressions, cardinality constraints, enumerated classes, and property characteristics. It also describes OWL's three sublanguages - OWL Lite, OWL DL, and OWL Full - which differ in their expressiveness and computational guarantees. The document concludes by discussing the Rule Interchange Format (RIF) and its role in defining rule languages for the semantic web.
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
This presentation is about:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions -Property Restrictions
- Class Expressions -Cardinality Restrictions
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.
The OWL Web Ontology Language enables software engineers to define ontologies of domain knowledge which can be queried and reasoned over by software agents. OWL facilitates greater machine interpretability of content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/owl-web-ontology-language.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=5Kr4JzqDO_w
The lecture covers:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions? -Property Restrictions
- Class Expressions? -Cardinality Restrictions
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
This presentation is about:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions -Property Restrictions
- Class Expressions -Cardinality Restrictions
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.
The OWL Web Ontology Language enables software engineers to define ontologies of domain knowledge which can be queried and reasoned over by software agents. OWL facilitates greater machine interpretability of content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/owl-web-ontology-language.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=5Kr4JzqDO_w
The lecture covers:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions? -Property Restrictions
- Class Expressions? -Cardinality Restrictions
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
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
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.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
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
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.
A presentation about Ontology Learning with an overview of the area and some methods used, specially techniques of Ontology Learning from Text. This presentation was part of a seminary in the MSc Course in Computer Science at UFPE - Recife - Brazil.
A non-technical explanation of the main ideas and notions in OWL.This talk was also recorded on video, and is available on-line at http://videolectures.net/koml04_harmelen_o/
Semantic Web languages: Expressivity vs scalabilitynvitucci
- Semantic Web and huge data sources are becoming more and more popular
- Reasoning should scale well, but the whole point of DLs is to be expressive
- Different approaches to representation and to reasoning are needed
- Research is moving towards scalable reasoning for expressive logics
The formulation of constraints and the validation of RDF data against these constraints is a common requirement and a much sought-after feature, particularly as this is taken for granted in the XML world. Recently, RDF validation as a research field gained speed due to shared needs of data practitioners from a variety of domains. For constraint formulation and RDF data validation, several languages exist or are currently developed. Yet, none of the languages is able to meet all requirements raised by data professionals.
We have published a set of constraint types that are required by diverse stakeholders for data applications. We use these constraint types to gain a better understanding of the expressiveness of solutions, investigate the role that reasoning plays in practical data validation, and give directions for the further development of constraint languages.
We introduce a validation framework that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type in a way that mappings from high-level constraint languages to an intermediate generic representation can be created straight-forwardly. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, and consists of a very simple conceptual model with a small lightweight vocabulary. We demonstrate that using another layer on top of SPARQL ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages.
UNIT III MINING COMMUNITIES
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in Social
Networks - Evaluating Communities – Core Methods for Community Detection & Mining Applications of Community Mining Algorithms - Node Classification in Social Networks.
Presentation at the ESWC 2011 PhD Symposium in May 2011, by Michael Schneider, FZI. Included are backup slides that have not been presented at the event. The corresponding PhD proposal can be found in the ESWC proceedings at <http: />. Alternatively, the PhD proposal can be downloaded from <http: />.
Although animals do not use language, they are capable of many of the same kinds of cognition as us; much of our experience is at a non-verbal level.
Semantics is the bridge between surface forms used in language and what we do and experience.
Language understanding depends on world knowledge (i.e. “the pig is in the pen” vs. “the ink is in the pen”)
We might not be ready for executives to specify policies themselves, but we can make the process from specification to behavior more automated, linked to precise vocabulary, and more traceable.
Advances such as SVBR and an English serialization for ISO Common Logic means that executives and line workers can understand why the system does certain things, or verify that policies and regulations are implemented
UMBEL: Subject Concepts Layer for the WebMike Bergman
This is an intro to UMBEL (Upper Mapping and Binding Exchange Layer), a lightweight ontology for relating Web content and data to a standard set of 20,000 subject concepts. Connecting to the UMBEL structure gives context and coherence to Web data. Via UMBEL, Web content, data and metadata can be linked, made interoperable, and more easily navigated and discovered. These subject concepts have defined relationships between them, and can act as semantic binding nodes for any Web content or data. The UMBEL subject concepts are derived from the OpenCyc version of the proven Cyc knowledge base.
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.
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!
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.
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.
# 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.
6. 6
Stack of Languages
XML
Surface syntax, no semantics
XML Schema
Describes structure of XML documents
RDF
Datamodel for relations" between things“
RDF Schema
RDF Vocabulary Definition Language
OWL
A more expressive Vocabulary Definition Language
7. 7
RDF Schema recap
RDFS provides
Classes
Class hierarchies
Properties
Property hierarchies
Domain and range restrictions
RDFS does not provide
Property characteristics (inverse, transitive, ...)
Local range restrictions
Complex concept definitions
Cardinality restrictions
Disjointness axioms
8. 8
Extending RDF Schema
OWL extends RDF Schema to a full-edged
knowledge representation language for the
Web
Logical expressions (and, or, not)
(in)equality
local properties
required/optional properties
required values
enumerated classes
symmetry, inverse
9. 9
Design Goals for OWL
Shareable
Changing over time
Interoperability
Inconsistency detection
Balancing expressivity and complexity
Ease of use
Compatible with existing standards
Internationalisation
10. 10
Requirements for OWL
Ontologies are object on the Web
with their own meta-data, versioning, etc...
Ontologies are extendable
They contain classes, properties, data-
types, range/domain, individuals
Equality (for classes, for individuals)
Classes as instances
Cardinality constraints
XML syntax
12. 12
Language layers of OWL
OWL Lite supports those users primarily
needing a classification hierarchy and
simple constraints.
OWL DL supports those users who want the
maximum expressiveness while retaining
computational completeness (all conclusions are
guaranteed to be computable) and
decidability (all computations will finish in finite
time).
13. 13
Language layers of OWL
OWL Full is meant for users who want
maximum expressiveness and the syntactic
freedom of RDF with no computational
guarantees.
14. 14
Language layers of OWL: in simple words
OWL Lite
Classification hierarchy
Simple constraints
OWL DL
Maximal expressiveness
While maintaining tractability
Standard formalization in a DL
OWL Full
Very high expressiveness
Losing tractability
All syntactive freedom of RDF (self-modifying)
15. 15
Features of OWL language layers
OWL DL
Negation
Disjunction
Full cardinality
Enumerated types
hasValue
OWL Full
Meta-classes
Modify language
OWL Lite
• (sub)classes, individuals
• (sub)properties, domain,
range
• conjunction
• (in)equality
• cardinality 0/1
• data types
• inverse, transitive,
• symmetric properties
• someValuesFrom
• allValuesFrom
20. 20
SKOS is not enough…
SKOS it is not a complete solution
it concentrates on the concepts only
no characterization of properties in general
simple from a logical perspective
ie, few inferences are possible
Ontology is the alternative
21. 21
Application may want more…
Complex applications may want more
possibilities:
characterization of properties
identification of objects with different URI-s
disjointness or equivalence of classes
construct classes, not only name them
more complex classification schemes
can a program reason about some terms? E.g.:
“if «Person» resources «A» and «B» have the same
«foaf:email» property, then «A» and «B» are identical”
etc.
22. 22
Web Ontology Language = OWL
OWL is an extra layer, a bit like RDFS or SKOS
own namespace, own terms
it relies on RDF Schemas
It is a separate recommendation
actually… there is a 2004 version of OWL (“OWL 1”)
and there is an update (“OWL 2”) published in 2009
23. 23
Term equivalences
For classes:
owl:equivalentClass: two classes have the same
individuals
owl:disjointWith: no individuals in common
For properties:
owl:equivalentProperty
remember the a:author vs. f:auteur?
owl:propertyDisjointWith
24. 24
Term equivalences
For individuals:
owl:sameAs: two URIs refer to the same concept
(“individual”)
owl:differentFrom: negation of owl:sameAs
25. 25
Other example: connecting to French
owl:equivalentClass
a:Novel f:Roman
owl:equivalentProperty
a:author f:auteur
26. 26
Typical usage of owl:sameAs
Linking our example of Amsterdam from one
data set (DBpedia) to the other (Geonames):
<http://dbpedia.org/resource/Amsterdam>
owl:sameAs <http://sws.geonames.org/2759793>;
This is the main mechanism of “Linking” in the
Linked Open Data project
27. 27
Property characterization
In OWL, one can characterize the behavior of
properties (symmetric, transitive, functional,
reflexive, inverse functional…)
One property can be defined as the “inverse” of
another
28. 28
What this means is…
If the following holds in our triples:
:email rdf:type owl:InverseFunctionalProperty.
29. 29
What this means is…
If the following holds in our triples:
:email rdf:type owl:InverseFunctionalProperty.
<A> :email "mailto:a@b.c".
<B> :email "mailto:a@b.c".
30. 30
What this means is…
If the following holds in our triples:
:email rdf:type owl:InverseFunctionalProperty.
<A> :email "mailto:a@b.c".
<B> :email "mailto:a@b.c".
<A> owl:sameAs <B>.
then, processed through OWL, the following
holds, too:
31. 31
Keys
Inverse functional properties are important for
identification of individuals
think of the email examples
But… identification based on one property may
not be enough
32. 32
Keys
Identification is based on the identical values of
two properties
The rule applies to persons only
“if two persons have the same emails and the same
homepages then they are identical”
34. 34
What it means is…
If:
<A> rdf:type :Person ;
:email "mailto:a@b.c";
:homepage "http://www.ex.org".
<B> rdf:type :Person ;
:email "mailto:a@b.c";
:homepage "http://www.ex.org".
<A> owl:sameAs <B>.
then, processed through OWL, the following
holds, too:
35. 35
Classes in OWL
In RDFS, you can subclass existing classes…
that’s all
In OWL, you can construct classes from
existing ones:
enumerate its content
through intersection, union, complement
etc
36. 36
Enumerate class content
I.e., the class consists of exactly of those
individuals and nothing else
:Currency
rdf:type owl:Class;
owl:oneOf (:€ :£ :$).
37. 37
Union of classes
Other possibilities: owl:complementOf,
owl:intersectionOf, …
:Novel rdf:type owl:Class.
:Short_Story rdf:type owl:Class.
:Poetry rdf:type owl:Class.
:Literature rdf:type owl:Class;
owl:unionOf (:Novel :Short_Story :Poetry).
38. 38
For example…
If:
:Novel rdf:type owl:Class.
:Short_Story rdf:type owl:Class.
:Poetry rdf:type owl:Class.
:Literature rdf:type owl:Class;
owl:unionOf (:Novel :Short_Story :Poetry).
<myWork> rdf:type :Novel .
<myWork> rdf:type :Literature .
then the following holds, too:
39. 39
It can be a bit more complicated…
If:
:Novel rdf:type owl:Class.
:Short_Story rdf:type owl:Class.
:Poetry rdf:type owl:Class.
:Literature rdf:type owlClass;
owl:unionOf (:Novel :Short_Story :Poetry).
fr:Roman owl:equivalentClass :Novel .
<myWork> rdf:type fr:Roman .
<myWork> rdf:type :Literature .
then, through the combination of different terms, the
following still holds:
40. 40
What we have so far…
The OWL features listed so far are already
fairly powerful
E.g., various databases can be linked via
owl:sameAs, functional or inverse functional
properties, etc.
Many inferred relationship can be found using a
traditional rule engine
41. 41
However… that may not be enough
Very large vocabularies might require even
more complex features
some major issues
the way classes (i.e., “concepts”) are defined
handling of datatypes like intervals
OWL includes those extra features but… the
inference engines become (much) more
complex
42. 42
Example: property value restrictions
New classes are created by restricting the
property values on a class
For example: how would I characterize a “listed
price”?
it is a price that is given in one of the “allowed”
currencies (€, £, or $)
this defines a new class
43. 43
But: OWL is hard!
The combination of class constructions with
various restrictions is extremely powerful
What we have so far follows the same logic as
before
extend the basic RDF and RDFS possibilities with
new features
define their semantics, ie, what they “mean” in terms
of relationships
expect to infer new relationships based on those
However… a full inference procedure is hard
not implementable with simple rule engines, for
example
44. 44
OWL “species” or profiles
OWL species comes to the fore:
restricting which terms can be used and under what
circumstances (restrictions)
if one abides to those restrictions, then simpler
inference engines can be used
They reflect compromises: expressiveness vs.
implementability
46. 46
OWL Species
• OWL 2 EL
Useful in applications employing ontologies that contain very
large numbers of properties and/or classes.
Basic reasoning problems can be performed in time that is
polynomial with respect to the size of the ontology
OWL 2 QL
is aimed at applications that use very large volumes of instance
data, and where query answering is the most important reasoning
task
OWL 2 RL :
is aimed at applications that require scalable reasoning
without sacrificing too much expressive power
47. 47
OWL RL
Goal: to be implementable with rule engines
Usage follows a similar approach to RDFS:
merge the ontology and the instance data into an
RDF graph
use the rule engine to add new triples (as long as it
is possible)
48. 48
What can be done in OWL RL?
Many features are available:
identity of classes, instances, properties
subproperties, subclasses, domains, ranges
union and intersection of classes (but with some
restrictions)
property characterizations (functional, symmetric, etc)
property chains
keys
some property restrictions
All examples so far could be inferred with OWL
RL!
49. 49
Improved Search via Ontology (GoPubMed)
Search results are re-ranked using ontologies
related terms are highlighted
50. 50
Improved Search via Ontology (Go3R)
Same dataset, different ontology
(ontology is on non-animal experimentation)
52. 52
Why rules on the Semantic Web?
Some conditions may be complicated in ontologies
(ie, OWL)
eg, Horn rules: (P1 & P2 & …) → C
In many cases applications just want 2-3 rules to
complete integration
rules may be an alternative to (OWL based)
ontologies
53. 53
Things you may want to express
An example from our bookshop integration:
“I buy a novel with over 500 pages if it costs less
than €20”
something like (in an ad-hoc syntax):
{
?x rdf:type p:Novel;
p:page_number ?n;
p:price [
p:currency :€;
rdf:value ?z
].
?n > "500"^^xsd:integer.
?z < "20.0"^^xsd:double.
}
=>
{ <me> p:buys ?x }
54. 54
Things you may want to express
p:Novel
?
x
?
n
:€
?
z
?z<20
?n>500
p:buys ?
x
me
55. 55
RIF (Rule Interchange Format)
The goals of the RIF work:
define simple rule language(s) for the (Semantic)
Web
define interchange formats for rule based systems
RIF defines several “dialects” of languages
RIF is not bound to RDF only
eg, relationships may involve more than 2 entities
there are dialects for production rule systems
59. 59
RIF Core
The simplest RIF “dialect”
A Core document is
directives like import, prefix settings for URI-s, etc
a sequence of logical implications
60. 60
RIF Core example
Document(
Prefix(cpt http://example.com/concepts#)
Prefix(person http://example.com/people#)
Prefix(isbn http://…/isbn/)
Group
(
Forall ?Buyer ?Book ?Seller (
cpt:buy(?Buyer ?Book ?Seller):- cpt:sell(?Seller ?Book ?Buyer)
)
cpt:sell(person:John isbn:000651409X person:Mary)
)
)
This infers the following relationship:
cpt:buy(person:Mary isbn:000651409X person:John)
61. 61
Expressivity of RIF Core
Formally: definite Horn without function
symbols, a.k.a. “Datalog”
eg, p(a,b,c) is fine, but p(f(a),b,c) is not
Includes some extra features
built-in datatypes and predicates
“local” symbols, a bit like blank nodes
62. 62
Expressivity of RIF Core
There are also “safeness measures”
eg, variable in a consequent should be in the
antecedent
this secures a straightforward implementation
strategy (“forward chaining”)
63. 63
RIF Syntaxes
RIF defines
a “presentation syntax”
a standard XML syntax to encode and exchange the
rules
there is a draft for expressing Core in RDF
just like OWL is represented in RDF
64. 64
What about RDF and RIF?
Typical scenario:
the “data” of the application is available in RDF
rules on that data is described using RIF
the two sets are “bound” (eg, RIF “imports” the data)
a RIF processor produces new relationships
65. 65
To make RIF/RDF work
Some technical issues should be settled:
RDF triples have to be representable in RIF
various constructions (typing, datatypes, lists)
should be aligned
the semantics of the two worlds should be
compatible
There is a separate document that brings these
together
66. 66
Remember the what we wanted from Rules?
{
?x rdf:type p:Novel;
p:page_number ?n;
p:price [
p:currency :€;
rdf:value ?z
].
?n > "500"^^xsd:integer.
?z < "20.0"^^xsd:double.
}
=>
{ <me> p:buys ?x }
71. 71
RIF vs. OWL?
The expressivity of the two is fairly identical
the emphasis are a bit different
Using rules vs. ontologies may largely depend
on
available tools
personal technical experience and expertise
taste…
72. 72
What about OWL RL?
OWL RL stands for “Rule Language”…
OWL RL is in the intersection of RIF Core and
OWL
inferences in OWL RL can be expressed with RIF rules
RIF Core engines can act as OWL RL engines
73. 73
Inferencing and SPARQL
Question: how do SPARQL queries and
inferences work together?
RDFS, OWL, and RIF produce new relationships
on what data do we query?
Answer: in current SPARQL, that is not defined
But, in SPARQL 1.1 it is…
74. 74
SPARQL 1.1 and RDFS/OWL/RIF
RDF Data with extra triples
SPARQL Pattern
entailment
pattern
matching
RDF Data
RDFS/OWL/RIF data
SPARQL Pattern
Query result
SPARQL Engine with entailment
76. 76
Available specifications:
Primers, Guides
The “RDF Primer” and the “OWL Guide” give a
formal introduction to RDF(S) and OWL
SKOS has its separate “SKOS Primer”
GRDDL Primer and RDFa Primer have been
published
The W3C Semantic Web Activity Wiki has links
to all the specifications
77. 77
“Core” vocabularies
There are also a number “core vocabularies”
Dublin Core: about information resources, digital
libraries, with extensions for rights, permissions,
digital right management
FOAF: about people and their organizations
DOAP: on the descriptions of software projects
SIOC: Semantically-Interlinked Online Communities
vCard in RDF
…
One should never forget:
ontologies/vocabularies must be shared and
reused!
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Some books
J. Pollock: Semantic Web for Dummies, 2009
G. Antoniu and F. van Harmelen: Semantic
Web Primer, 2nd edition in 2008
D. Allemang and J. Hendler: Semantic Web for
the Working Ontologist, 2008
P. Hitzler, R. Sebastian, M. Krötzsch:
Foundation of Semantic Web Technologies,
2009
…See the separate Wiki page collecting book references
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Further information
Planet RDF aggregates a number of SW blogs:
http://planetrdf.com/
Semantic Web Interest Group
a forum developers with archived (and public)
mailing list, and a constant IRC presence on
freenode.net#swig
anybody can sign up on the list
http://www.w3.org/2001/sw/interest/
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Thank you for your attention!
These slides are also available on the Web:
http://www.w3.org/2010/Talks/0622-SemTech-IH/