Concept, Technologies, Tool

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Nov 5, 2013 (3 years and 8 months ago)

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CS690L
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Lecture 2

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CS690L

Semantic Web and Knowledge Discovery:
Concept, Technologies, Tool

Yugi Lee

STB #555

(816) 235
-
5932

leeyu@umkc.edu

www.sice.umkc.edu/~leeyu


This presentation was designed based on

SWWS
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01 symposium report and
Farquhar’s Ontology tutorial.

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Lecture 2

Semantic Web?


"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 cooperation."
--

Tim Berners
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Lee, James
Hendler, Ora Lassila,
The Semantic Web
,
Scientific American, May 2001


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Lecture 2

Semantic Interoperability


Ihe interoperability
layer to migrate from
the syntactic to the
semantic!


From Data space to
Knowledge space


Integration and
composition


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Interoperability



Object Interoperability:



This is the layer at which the current middleware
products are aimed in the industry.


However these objects are primarily defined as
containers for software and for streamlining the
software development process.


The CORBA, EJB object models are examples of
standards at this layer.

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Interoperability


Meta
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Model Interoperability:



This is the layer at which the cross
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over from the
"data" space to the "knowledge" space takes
place.


The objects here are viewed as containers of
knowledge to be fleshed out by upper layers.


The OKBC and RDF(S) core models are
examples of standards at this layer.

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Interoperability



Ontology Interoperability:



This is the layer where ontologies, schemas and
classifications are built upon common underlying
standardized meta
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models.


The ability to use different ontologies to specify and
query information constitutes interoperability at this
layer.


Ontology Standardization

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Interoperability


Meta
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Data (View/Query) Interoperability:



Semantic metadata descriptions can be constructed
from one or more underlying ontologies.


Issues at this layer would be to decompose information
requests into those supported by the individual
semantic metadata descriptions corresponding to the
information sources.


Ontology Query Language

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Interoperability


Process/Services Interoperability:



Semantic process/service descriptions can be constructed from
resources on the Web and one or more underlying ontologies.


Issues at this layer would be to enable a better discovery,
selection, composition, monitoring, and interoperability of
services/process.


A resource description, informally called its “semantics”,
includes that information about the resource that can be used by
computers
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not just for display purposes, but for using it for
automatic processing in various applications.


Service Ontology, Semantic web service workflow, Web
Service Discovery, addressing semantic heterogeneity handling,
QoS specification for Web Services and Processes.

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Lecture 2

Practical Motivation: Semantic Web/Application


The Semantic Web is more than simply some sort of
academic foolishness or rewarmed AI vision.


The applications showed real technology and tools are
being built in the Semantic web community, and that
there is a lot of interest in these technologies on the part
of industry and government.


The web services community showed one area where
there is tremendous industrial interest and where
semantic web technology could be an important part of
the work.


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Challenging Example: Query Answering


“How many acres of cotton are planted in China?”


Response from today’s Web


Some documents
--

some of which may contain
the answer
--

somewhere


Response from the Semantic Web of the future

“15,485,000 in 2000, says the
USDA”


Deductive query answering rather than document retrieval


Ontologies will be a primary source of knowledge for reasoning:
enable derivation of answers not explicitly on a Web site


Even simple Web sites may reference large and distributed
ontologies: a challenge to query
-
answering reasoners


Ontologies could include special purpose query
-
answering
reasoners


For proving instances of atomic formulas in the ontology’s vocabulary and
making inferences from sentences in the ontology’s vocabulary


Requires an API for special purpose reasoners

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Challenging Example: Semantic Search

Tap’s Semantic Search (Stanford University)



Retrieves real
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time data relevant to a quer: Determines the
“semantic type” of individuals in the query, Uses models
of relevancy based on types


Uses a background ontology and large KB of individuals


~3,000 class and ~72,000 individuals, Downloadable in
RDF
-
S or DAML+OIL


Can be augmented with use
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specific and user
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specific
ontologies and used to retrieve data relevant to a task
being performed

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Lecture 2

Ontology
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What Is an Ontology?
[A. Farquhar]


To communicate, plan, think we need aconceptualization
of the world


What kinds of things are there? What are their properties? What
are their relationships?


These things define our ontology


We all have ontologies (e.g., of organizations, computers,
animals)


Some are very idiosyncratic. Some are shared!


Communication and interaction require common shared
ontologies.

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Ontology
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Problems in Communication


People, organizations,
software programs must
communicate


Different needs and
backgrounds imply
different viewpoints,
assumptions, jargon


This divergence is natural
and valuable


But leads to problems in
communication,
interaction, and
understanding



Explicit ontologies are crucial for


Communication


Education


Interoperation


Integration


Adaptive agents

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Ontology
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Example


Researchers in molecular biology need to


share results and check consistency between their
models, data, and reported models and data


The Riboweb project (Stanford, SMI)


Building an ontology for ribosomes, models, data,
reports


Molecular structure, experimental data, tests, …


Encoding (by hand) relevant literature


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Ontology
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Example


Doctors, clinics, hospitals, insurance companies,
government agencies need to share information



Clinical guidelines, drug interactions, covered
procedures, best practices


Several efforts are addressing aspects of this
problem



UMLS (unified medical language system)


SNOMED (standard nomenclature for medicine)


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Ontology
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Example


There are many workflow management systems
available


In order to share information across them and
support interoperation, we need to define an
integrated ontology that covers


Processes, resources, products, services, organizations


Several groups are involved in such an effort


NIST, WfMC, PIF, TOVE

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Ontology
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Example


Collaborative engineering projects need to
communicate across discipline boundaries


Several projects (e.G., PACT, Boeing) have
worked to build ontologies for the subdisciplines
and span them



Goals include:


Automated notifications on design modifications


Cross
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disciplinary simulation


Improved design process

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Ontology
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Benefits


Explicit ontologies support


Shared understanding among people


Interoperability between tools


Systems engineering


Reusability


Declarative specification


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Lecture 2

Semantic Web Language

XML


Language for describing the structure of document content e.g., declare data to be
a retail price, a sales tax, a book title, ...


Uniform method for describing and exchanging data using HTTP


Provides a “syntactic schema”


<
Publication

URL = "ftp://db.stanford … xml.ps”>


<Title> From Semistructured Data ... Language </Title>


<Author> R. Goldman </Author>


<Published> Proceedings of ... Databases </Published>


<Location>
Location of what?


<City> Philadelphia </City>


<State> Pennsylvania </State>


</Location>


<Date>


<Month> June </Month>


<Year> 1999 </Year>


</Date>

</Publication>

When in June?

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Semantic Web Language

XML Is Not Enough



Ontologies enable independently developed programs
to exchange data: XML provides “syntactic schema”


Ontologies specify intended meaning in a computer
interpretable form: XML provides no means of
specifying intended meaning of tags

“XML is like HTML, where you make up your own tags.”

“But in XML, you can’t say what your tags mean.”

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W3C Semantic Web Activity


Semantic Web Activity (http://www.w3.org/2001/sw/)


“Established to serve a leadership role, in both the design of enabling specifications and the
open, collaborative development of technologies that support the automation, integration and
reuse of data across various applications.”


Successor to the W3C Metadata Activity


RDF Core Working Group (http://www.w3.org/2001/sw/RDFCore/)


Responsible for the Resource Description Framework (RDF)


Web Ontology Working Group (http://www.w3.org/2001/sw/WebOnt/)


Charter: Build upon the RDF Core work a language for defining structured web based ontologies
which will provide richer integration and interoperability of data among descriptive
communities


Developing Ontology Web Language (OWL)


Based on DAML+OIL, developed in DARPA’s Agent Markup Language program


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Lecture 2

Open Issues


What can we do as individuals and as part of the semantic web
community?


Everyone was frustrated by the "waiting around" for Semantic
Web infrastructure to appear,


that creating "some" infrastructure was more important that
resolving the remaining "expressivity vs. tractability" dilemmas
(for example).


there is always risk solving the easy parts of problems first,
because that can make it harder to solve the harder parts later.
Nevertheless, the consensus was "forge ahead!"

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Open Issues


Do we need to standardize on foundational models first?


agree on minimalist semantics (expressivity) and a
syntax in which to represent units of meaning,



leaving for distributed, incremental, and local
development the problem of creating actual ontologies



that would be expressed, represented and
communicated using the foundational model.

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Open Issues


Is the current Semantic Web standards development
process adequate?


This addresses the dilemma posed by a general
acknowledgement that the Semantic Web poses new challenges;


The current standards process may be the best that we know
how to create, and it still may be inadequate
-

because, for
instance, it deals with distributed semantics.


At worst, it needs field
-
testing and feedback from actual use.


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Open Issues


Do we need Semantic Web glossaries? ("pumpkins?")


Even if there was not consensus on the definitions, all agreed
that Semantic Web glossaries would be a big help;


they would be something to disagree with, and catalyze
alternative definitions for important concepts.



Do we need some ontology ontologies?


Everyone recognized the "ontology ontology" problem and that
it's lack of resolution was an impediment to progress, and that
"we are all part of the problem."


That is, it's hard to find out what ontologies exist, and whether
they are worth using, etc. This is part, but not all, of the deep
ontology re
-
use challenge.


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Open Issues


How do we deal with the diversity of languages and tools that are
starting to emerge for semantic content.


Currently XML, XML schema, RDF(S), DAML+OIL, WebML,
and various other tools are available for metadata storage,
querying, etc.


It is clear that there is a need for unifying frameworks, toolkits,
etc.


Do we need well
-
defined semantics in the metadata languages?


Many of the applications were using ontology languages like
DAML, or extensions of RDF(S).


Consensus was that completing the RDFS standard, and moving
to a web ontology standard that extended RDFS and XML
Schema was important for these applications.


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Open Issues


Do we all believe that experimentation should continue?


expressivity vs. tractability


We have no proof that proposed Semantic Web standards and
tools are useful or even work at all.


The chicken/egg problem:


Without semantic markup, there's not a lot of motivation for the
industrial base to pay attention to the semantic web.


Without industry investment/support, the W3C and others have
trouble developing standards and getting sources marked up.


Current government funding helps to jump start this level, but the
semantic web community needs to figure out how to both
publicize these efforts and increase the dissemination of this
technology.


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Discussion


What will make the semantic web have a life of
its own?


What are key ontologies that need to be created?


What are the “killer apps” for the semantic web?


Do you have ontologies you could contribute?