The document discusses semantic web technology, which aims to make information on the web better understood by machines by giving data well-defined meaning. It outlines the evolution of web technologies from the initial web to the semantic web. Key aspects of semantic web technology include ontologies to define common vocabularies, semantic annotations to associate meaning with data, and reasoning capabilities to enable complex queries and analyses. Languages, tools, and applications are needed to implement these semantic web standards and make the web of linked data usable.

1. Semantic Web Technology
STANLEY WANG
SOLUTION ARCHITECT, TECH LEAD
@SWANG68
http://www.linkedin.com/in/stanley-wang-a2b143b

2. What is Semantic Web?
• The current Web activities are
mostly focus on Machine-to-
Human;
• Machine-to-Machine activities
are not particularly well
• supported by software tools.
“The Semantic Web is an extension of the current web in which
information is given well-defined meaning, better enabling
computers and people to
work in co-operation.“ [Berners-Lee, 2001]

3. Evolution of Web Technology
Web (since 1992)
• HTTP
• HTML/CSS/JavaScript
Semantic Web
•Reasoning
•Logic, Rules
•Trust
Social Web (since 2003)
• Folksonomies/Tagging
• Reputation, sharing
• Groups, relationships
Data Web (since 2006)
• URI de-reference
• CBD
• RDF serializations

4. From Web of Document to Web of Linked Data
Many Web sites
containing unstructured,
textual content
Few large Web sites
are specialized on
specific content types
Many Web sites containing
& semantically syndicating
arbitrarily structured
content
Pictures
Video
Encyclopedic
articles
+ +
Web 1.0 Web 2.0 Web 3.0

5. Semantic Web Stack
• Machine Processable,
Global Web Standards:
• Assigning Unambiguous
Names (URI)
• Expressing data, including
metadata (RDF, RDFS)
• Modelling Ontologies
(OWL)
• Query and Retrieve
(SPARQL)

6. Key Functions of Semantic Web Technology
• Ontology Modeling
 Agreement with a common vocabulary, conceptual models and
domain Knowledge;
 Schema + Knowledge base
 Agreement is what enables interoperability
 Formal description - Machine processability is what leads to
automation
• Semantic Annotation
 Metadata Extraction: Associating meaning with data, or labeling
data so it is more meaningful to the system and people.
 Can be manual, semi-automatic (automatic with human verification),
automatic
• Reasoning Computation
 semantics enabled search, integration, answering complex queries,
connections and analyses (paths, sub graphs), pattern finding,
mining, hypothesis validation, discovery, visualization

7. 7
Semantic Technology Market Forecasting
Semantic solution, services & software markets will
grow rapidly, topping $60B by 2020

8. Semantic Web: Annotations
Semantic
Annotations
Ontologies Logical Support
Languages Tools Applications /
Services
Web content
UsersCreatorsWWW
and
Beyond
Semantic
Web
Semantic Web
content
Users
Semantic
Web and
Beyond
Creators
applications
agents
Semantic annotations are
specific sort of metadata,
which provides information
about particular domain
objects, values of their
properties and relationships, in
a machine-processable, formal
and standardized way.

9. Semantic Web: Ontologies
Semantic
Annotations
Ontologies Logical Support
Languages Tools Applications /
Services
Web content
UsersCreatorsWWW
and
Beyond
Semantic
Web
Semantic Web
content
Users
Semantic
Web and
Beyond
Creators
applications
agents
Ontologies make metadata
interoperable and ready for
efficient sharing and reuse. It
provides shared and common
understanding of a domain, that
can be used both by people and
machines. Ontologies are used as
a form of agreement-based
knowledge representation about
the world or some part of it and
generally describe: domain
individuals, classes, attributes,
relations and events.

10. Semantic Web: Rules
Semantic
Annotations
Ontologies Logical Support
Languages Tools Applications /
Services
Web content
UsersCreatorsWWW
and
Beyond
Semantic
Web
Semantic Web
content
Users
Semantic
Web and
Beyond
Creators
applications
agents
Logical support in form of rules is needed to infer
implicit content, metadata and ontologies from
the explicit ones. Rules are considered to be a
major issue in the further development of the
semantic web. On one hand, they can be used in
ontology languages, in conjunction with or as an
alternative to description logics. And on the other
hand, they will act as a means to draw
inferences, to configure systems, to express
constraints, to specify policies, to react to
events/changes, to transform data, to specify
behavior of agents, etc.

11. Semantic Web: Languages
Semantic
Annotations
Ontologies Logical Support
Languages Tools Applications /
Services
Web content
UsersCreatorsWWW
and
Beyond
Semantic
Web
Semantic Web
content
Users
Semantic
Web and
Beyond
Creators
applications
agents
Languages are needed for machine-processable
formal descriptions of: metadata (annotations) like e.g.
RDF; ontologies like e.g. OWL.; rules like e.g.
RuleML. The challenge is to provide a framework for
specifying the syntax (e.g. XML) and semantics of all
of these languages in a uniform and coherent way.
The strategy is to translate the various languages into
a common 'base' language (e.g. CL or Lbase)
providing them with a single coherent model theory.

12. Semantic Web: Tools
Semantic
Annotations
Ontologies Logical Support
Languages Tools Applications /
Services
Web content
UsersCreatorsWWW
and
Beyond
Semantic
Web
Semantic Web
content
Users
Semantic
Web and
Beyond
Creators
applications
agents
User-friendly tools are needed for
metadata manual creation (annotating
content) or automated generation, for
ontology engineering and validation, for
knowledge acquisition (rules), for
languages parsing and processing, etc.

13. Semantic Web: Applications and Services
Semantic
Annotations
Ontologies Logical Support
Languages Tools Applications /
Services
Web content
UsersCreatorsWWW
and
Beyond
Semantic
Web
Semantic Web
content
Users
Semantic
Web and
Beyond
Creators
applications
agents
Utilization of Semantic Web
metadata, ontologies, rules,
languages and tools enables to
provide scalable Web applications
and Web services for consumers and
enterprises" making the web 'smarter'
for people and machines.

14. The Idea of Web Linked Data
• Think of the semantic web as building on the ideas behind Linked Data;
• Linked Data is nor a specification, but a set of best practices for providing
a data infrastructure that makes it easier to share data across the web;
• Use semantic web technologies such as RDFS, OWL, and SPARQL to build
applications around that data;
Four Principles of Linked Data:
1. Use URIs as names for things;
2. Use HTTP URIs so that people can look up those names;
3. When looking up a URI, provide useful information with the standards
such as RDF, RFFS, OWL, SPARQL;
4. Include links to other URIs to discover more information;

15. Foundation for Future Enterprise Systems
• Semantic technology as a software technology allows the
meaning of information to be known and processed at
execution time. For a semantic technology there must be a
knowledge model of some part of the world that is used by
one or more applications at execution time.
Semantic Technologies represent meanings separately from
data, content, or program code, using the open standards
for the semantic web such as RDF and OWL W3C standards.

16. 16
Drivers for the Semantic Web Technology
• Business models develop rapidly these days, so
infrastructure that supports change is needed;
• Organizations are increasingly forming and disbanding
collaborations;
• Data is growing so quickly that it is no longer possible for
individuals to identify patterns in their heads;
• Increasing recognition of the benefits of collective
intelligence;
It is, essentially, the Web of Data.
“Semantic Web Technologies” is a collection of standard
technologies to realize a Web of Data

17. Semantics Web Technology in Nut Shell
• “Semantics” provides a universal framework to describe and
link different data so that it can be better understood and
searched holistically, allowing both people and computers to
see and discover relationships in the data;