Chapter 1,2 Semantic Web Intro

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22 Οκτ 2013 (πριν από 3 χρόνια και 11 μήνες)

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Semantic Web

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2

Introduction


Information management facilities not keeping
pace with the capacity of our information storage.


Information Overload


haphazardly created hierarchical directory structure


diversity of information formats and access methods


Information cannot be shared (stovepipe system)


Poor Content Aggregation


Using Semantic Web to improve knowledge
management .


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3

The future of the Web


--

Tim Berners
-
Lee



A more collaborative medium.


Understandable, and thus processable, by
machines.


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4

Need RDF

Additional meta data is needed

for machines to be able to process

Information on the Web.

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5

From Applications to Data


With the Web, Extensible Markup
Language (XML), and now the emerging
Semantic Web, the shift of power is
moving from applications to data.


The path to machine
-
processable data is
to make the data smarter.



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Smart Data Continuum

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Smart Data Continuum


Text and databases (pre
-
XML).
Most
data is proprietary to an application. Thus,
the “smarts” are in the application and not
in the data.


XML documents for a single domain.
Data achieves application independence
within a specific domain. Data is now
smart enough to move between
applications in a single domain.

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Smart Data Continuum


Taxonomies and documents with mixed
vocabularies.
In this stage, data can be
composed from multiple domains and accurately
classified in a hierarchical taxonomy. In fact, the
classification can be used for discovery of data.
Simple relationships between categories in the
taxonomy can be used to relate and thus
combine data. Thus, data is now smart enough
to be easily discovered and sensibly combined
with other data.


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Smart Data Continuum


Ontologies and rules.
In this stage, new data can
be inferred from existing data by following logical rules.
In essence, data is now smart enough to be described
with concrete relationships, and sophisticated formalisms
where logical calculations can be made on this “semantic
algebra.” This allows the combination and recombination
of data at a more atomic level and very fine
-
grained
analysis of data. Thus, in this stage, data no longer
exists as a blob but as a part of a sophisticated
microcosm. An example of this data sophistication is the
automatic translation of a document in one domain to the
equivalent (or as close as possible) document in another
domain.


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Definition


Semantic Web: a machine processable
web of smart data.


Smart data: data that is application
-
independent, composeable, classified, and
part of a larger information ecosystem
(ontology).


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11

XML


XML is the syntactic foundation layer of the
Semantic Web.


All other technologies providing features for the
Semantic Web will be built on top of XML.


Requiring other Semantic Web technologies
(like the Resource Description Framework) to be
layered on top of XML guarantees a base level
of interoperability.


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XML


The technologies that XML is built upon
are Unicode characters and Uniform
Resource Identifiers (URIs).


The
Unicode characters

allow XML to be
authored using international characters.


URI
s are used as unique identifiers for
concepts in the Semantic Web.


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13

XML is not enough


XML only provides syntactic
interoperability.


sharing an XML document adds meaning to
the content; however, only when both parties
know and understand the element names.


Ex:

<price> $12.00 </price>

<cost> $12.00 </cost>




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Web Services


Web services are software services identified by
a URI that are described, discovered, and
accessed using Web protocols.


Web services consume and produce XML.


Web services fit into the Semantic Web by


furthering the adoption of XML, or more smart data.


solve the Web service discovery problem.


enabling Web services to interact with other Web
services.


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Next Trend


The next big trend in Web services will be
semantic
-
enabled Web services, where we can
use information from Web services from different
organizations to perform correlation, aggregation,
and orchestration.


Research programs


TAP at Stanford


bridging the gap between disparate Web service
-
based data
sources and "creating a coherent Semantic Web from
disparate chunks.“


enables semantic search capabilities, using ontology
-
based
knowledge bases of information.

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Semantic Web services

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What's after Web Services


Logical assertions



Classification



Formal class models



Rules



Trust


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Logical assertions



An assertion is the smallest expression of useful
information.


One way is to model the key parts of a sentence
by connecting a subject to an object with a verb.


RDF captures these associations between
subjects and objects.


Example:


HP’s RDF processing software “Jena”


Adobe's Extensible Metadata Platform “XMP”

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Formal class models


A formal representation of classes and
relationships between classes to enable
inference requires rigorous formalisms even
beyond conventions used in current object
-
oriented programming languages like Java and
C#.


Ontologies are used to represent such formal
class hierarchies, constrained properties, and
relations between classes.


The W3C is developing a Web Ontology
Language (abbreviated as OWL).

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Ontology Example

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Rules


The Semantic Web can use information in an
ontology with logic rules to infer new
information.


If a person C is a male and childOf a person A, then
person C is a "sonOf" person A.


If a person B is a male and siblingOf a person A,
then person B is a "brotherOf" person A.


If a person C is a "sonOf" person A, and person B is
a "brotherOf" person A, then person B is the
"uncleOf" person C.


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Trust


By allowing anyone to make logical
statements about resources, smart
applications will only want to make
inferences on statements that they can
trust.


Verifying the source of statements is a key
part of the Semantic Web.

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In the Future


"By 2005," the Gartner Group reports,
"lightweight ontologies will be part of 75
percent of application integration projects."

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What Is the Semantic

Web Good For?


Decision Support


Business Development


Information Sharing and Knowledge
Discovery


Administration and Automation

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25

Semantic Web

Technologies

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26

Enterprise Efforts


Adobe is reorganizing its software meta data around
RDF, and they are using Web ontology
-
level power for
managing documents. Because of this change, "the
information in PDF files can be understood by other
software even if the software doesn't know what a PDF
document is or how to display it.“


IBM is making significant investments in Semantic Web
research.


Germany's Ontoprise are making a business out of
ontologies, creating tools for knowledge modeling,
knowledge retrieval, and knowledge integration.