Transcript Semantic Web Dushyant Rajput (05005003) Neelmani Singh (05005019) Pratik Jawanpuria (05005022) Jayant Nagda (05D05001) Nirdesh Chauhan (05D05002)

Semantic Web
Dushyant Rajput (05005003)
Neelmani Singh (05005019)
Pratik Jawanpuria (05005022)
Jayant Nagda (05D05001)
Nirdesh Chauhan (05D05002)
Motivation
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Problem: Web was built for humans
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web content today is designed for humans to read, not for computer programs to
read and manipulate.
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Computers have no reliable way to process the semantics and bring structure to
the meaningful content on the web.
Solution: make the Web friendlier for machines
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we need “machine-understandable” content (not “machine-readable”, we already
have that)
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“machine-understandable” means content with accessible formal semantics
Why semantic web?
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Extend the current existing paradigm of human computer interaction which will
allow machines to analysis data in a much better way which they merely display
right now.
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The Semantic Web is an evolving
extension of the World Wide Web in which
the semantics of information and services
on the web is defined, making it possible
for the web to understand and satisfy the
requests of people and machines to use
the web content.
- Wikipedia
Semantic Web Architecture
Semantic Web Agents
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Nirdesh Chauhan
World in the reign
of Semantic Web
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Semantic Web Agents
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An agent is an intermediary who makes a complex organization
externally accessible.
For example, travel agent.
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Semantic Web Agent:
Programs that collect Web content from diverse sources, process
the information and exchange the results with other agents.
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Semantic Web may be viewed as:
1. An expert system with a distributed knowledge base.
2. A society of agents that solve complex knowledge-based tasks.
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Agents are viewed as primary consumers of knowledge. Agents
capture the notion of the use of entities to solve complex problems
on the Web.
Characteristics of Agents
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There is no such standard definition for an agent. However some
peculiar characteristics that a Semantic Web Agent must possess
are:
Intelligence
Autonomy
Reactivity
Pro-activeness
Social ability
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These definitions are subject to different interpretations.
For example, there are many different definitions of what it means
for an agent to be truly autonomous.
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All that we require is that our agent can interact with other agents,
and do useful work for us.
Multi-Agent System
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MAS is a system composed of several software agents, collectively
capable of reaching goals that are difficult to achieve by an
individual agent
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MAS are necessarily distributed and concurrent in nature.
However, A distributed system prescribes a static pattern of
behavior towards a common goal.
Whereas an agent system is more dynamic with individual agents
acting autonomously towards their own goals.
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Agents in a MAS:
1. Work autonomously to achieve their own goal.
2. Interoperate with other agents as a part of MAS.
Reactive Agents
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A purely reactive agent does not perform any kind of deduction.
Hence, relatively straightforward to implement and map environment
states directly to actions. E.g. Stock price monitoring.
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They act as a sensor on the environment and are triggered by
specific events.
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Engineered to respond to changes in the environment which we
represent as input events. A(environment state) → action
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Implementation
Equation-based approach - for events that occur many times, such
as price fluctuations.
State-based approach - for events that are largely one-time, such as
messages.
Practical Reasoning Agents
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The practical reasoning agents based on the BDI model.
B(beliefs)
- knowledge about the current environment state.
D(desires) - state of affairs the agent would like to bring about.
I(intentions) - desires that the agent has committed to achieving.
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Commitment strategies used to determine the persistence of the
intentions: Blind, Single-minded and Open-minded.
Reason(B, D, I)
Do
p ← next percept
// Changes in the behaviour triggered by external events
B ← revise(B, Ò)
// Agents’ beliefs are revised in light of these percepts
D ← options(B, I)
// No. of possible options arise for action
I ← deliberate(B, D, I)
// Agent deliberates on competing options
P ← plan(B, I, A)
// performs a means–ends analysis on the intentions.
execute(P)
// Once a suitable plan (P) has been formed, we execute
while true
Semantic Web: Logic Layer
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Neelmani Singh
Semantic Web : Logic Layer
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For the Semantic Web, semantic indicates that the meaning
of data on the Web that can be discovered—not just by
people, but also by computers
But this discovery is not possible without logic.
Computers will need to apply logical reasoning to all kinds of
“statements” , and those statements will be distributed across
the Web.
Roles of Logic for the SW
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Application and evaluation of rules
Inferring facts that haven’t been explicitly stated.
Explaining why a particular conclusion has been reached.
Detecting contradictory statements and claims.
Specifying ontologies and vocabularies of all kinds.
Representing knowledge.
Playing a key role in the statement and execution of queries
to obtain information from stores of data on the Semantic
Web.
Combining information from distributed sources in a
coherent way.
Rules
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Specialized kind of rule used , the kind often used
by so-called expert systems:
IF <logical conditions are met>
THEN <perform specified actions>
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Evaluating the truth of the logical conditions involves logic,
but there is more to it. Rules are often chained together.
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The Semantic Web will have several additional needs:
 A Web-compatible language for expressing rules
 The ability to specify the kinds of rules and their
relationships and constraints.
 Ways to handle incompatible rules.
Inferring facts and Explanations
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The Semantic Web will be a very large, open system. So
inferring facts from given facts and rules is essential
(Open World model).
The ability to explain a train of reasoning may emerge as
one of the most important capabilities a Semantic Web
reasoning system can have.
If Pratik is allowed access to Neelmani's bank account, it
may become important why the conclusion was reached.
If Mr. Dushyant's mother’s name is “Sunita” then “Sunita”
must be a woman.
Contradictions and Interpretations
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What will happen if a Semantic Web system encounters a
contradiction?
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Isn’t trivial because in pure logic, a contradiction would allow
anything to be proved.
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The obvious thing to do would be to regard each statement
as a kind of claim that may or may not be strongly
supported.
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To provide an alternative, the current version of the RDF
specifications define a way to understand the meaning of a
collection of RDF statements that can deal with the
possibility of contradictory information at the cost of more
computing power.
Combining information
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Problems can arise from trying to combine
information from multiple sources on the
Semantic Web. Here are some of the most
prominent:
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Different sources may use different ontologies
(different vocabularies for the same things)
 Different sources may have different semantics for the
(apparently) same things.
 Different sources may contain contradictory
information.
 Different sources may have different degrees of
reliability.
Ontologies
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An ontology establishes the things that a system can
talk and reason about. This means the vocabulary.
The terms have logical relationships to each other that
need to be specified, and this in turn means that any
ontology system must adopt some variety of logic.
Ontology supplies the concepts and terms.
logic provides ways to make statements that define and
use them, and to reason about collections of statements
that use the concepts and terms.
In the Semantic Web, the role of logic will be very different
from the role of most other components of the Semantic Web
layer cake.
But WHY ??
Because it isn’t information to be exchanged.
Semantic Web Components
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Dushyant Rajput
Semantic Web Components
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Extensible Markup Language(XML) provides an elemental
syntax to structure data.
XML schema provides and restricts the structure and content
of elements in XML documents.
Resource Description Framework (RDF) is a language for
expressing data models in XML syntax.
RDF schema is a vocabulary for describing properties and
classes of RDF-based resources.
Web Ontology Language (OWL) provides additional
vocabulary for describing properties and relations between
classes.
Resource Description Framework
The need for RDF : Make all data and metadata accessible to computers and
addressable over networks.
 Provide standard way to refer to any particular bit of
information.
To achieve these RDF uses a simple data model : Resources refers to objects.
 Statements links two resources. It encodes information as
triples subject – predicate/property - object/value.
RDF (continued ...)
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Example:
(Person, Name, “Neelmani Singh”)
Subject
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Predicate
Object
Resource Identification :URI (Uniform Resource Identifier) references
= URI + fragment identifier
(the part that follows the # sign after a URI, if any).
Eg:- http://www.w3.org/1999/02/22-rdf-syntax-ns #
RDF Graph representation
Ontology Framework
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Resource Description Framework Schema
Specification (RDFS) provides semantics for
generalized-hierarchies of properties and classes.
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Web Ontology Language (OWL)
 Uses properties of RDFS along with new ones.
 Has W3C recommendations.
Semantic Web Services
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Jayant Nagda
Web Services
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Web services are a new breed of Web application. They are
self-contained, selfdescribing, modular applications that can
be published, located, and invoked across the Web. Web
services perform functions, which can be anything from
simple requests to complicated business processes.
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Once a Web service is deployed, other applications (and
other Web services) can discover and invoke the deployed
service.
Architecture
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Web:
 URIs: specific addresses of web-elements
 HTML: way of describing documents.
 HTTP: a protocol that is used to retrieve information on
web.
Semantic Web:
 UDDI: provides a mechanism of finding web services.
 WSDL: defines a service.
 SOAP: a message layout specification that defines a
uniform way of passing XML-encoded data.
Describing a web service
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WSDL (Web Services Description Language) is an XMLbased language that provides a model for describing public
interface to a Web services.
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Services as a collection of ports. Messages are abstract.
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A client program connecting to a web service can read the
WSDL to determine what functions are available on the
server.
Discovering web services
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UDDI (Universal Description Discovery and Integration)
is a platform-independent, XML-based registry for businesses
worldwide to list themselves on the Internet.
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UDDI is an open industry initiative, sponsored by OASIS,
enabling businesses to publish service listings and discover
each other.
SOAP
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SOAP (Simple Object Access Protocol) is a protocol for
exchanging XML-based messages over computer networks,
normally using HTTP.
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SOAP uses RPC (Remote Procedure Call)
Semantic Web Trust
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Pratik Jawanpuria
Semantic Web Trust
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The concept of Semantic Web is great, but who would trust
such as system if anyone can say anything
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If one person says that x is blue, and another says that x is
not blue, doesn't the whole Semantic Web fall apart?
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So how to know which is trustworthy and whom to believe?
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To confront these situation, we have Semantic Web Trust.
Trust Policies in Day to Day Life
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A trust policy is a subjective procedure used for
evaluating the trustworthiness of information in a
specific situation.
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Try Dominos's pizza good but not pasta.
Trust Pratik on science fiction movies but not on political
news.
Believe in media only on sports news.
See a movie if its IMDB's rating in above 7.
Trust professors on their research field.
We have to allow similar range of trust policies on
the Semantic web
Digital Signature
Web of Trust
Policy-based Trust Management
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Trust relies on "STRONG SECURITY" mechanisms such as
digital signatures and trusted certification authorities.
Seen as a solution of problem of authorization and access
control in open systems.
Reputation-Based Trust
Management
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include rating systems like the one used by eBay and Web-OfTrust mechanisms.
 IMDB's movie ratings
 Social networks like Orkut, Facebook.
Context-Based Trust Mechanisms
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Use meta information about the circumstances in which
information has been claimed e.g. who said, what, when and
why.
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Trust a toothpaste if a dental doctor prescribes it
Include role based trust mechanisms, using the author's role or
his membership in the specific group, for trust decisions.
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Distrust all members of organization X
Content-Based Trust Mechanisms
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Use rules and axioms together with the information content
itself and related information about the same topic given by
other providers
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Distrust the cricket news if India makes less than 100 runs.
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Distrust product prices that are more than 50% below the average price.
Integrated View of Trust
Management
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Each of the above mechanism address the
problem from a different perspective.
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In many cases it will be desirable to combine
them to handle situations like :
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a seller is interested in protecting an item on sale in
different ways depending on the value of the item:
based on reputation if the price is few hundreds of
rupees (e.g. a T-shirt) or based on policies if it is of
thousands (e.g. requiring a digital signature for flight
ticket)
Prototype of trust architecture
- retrieved information is used within the
application’s context
- provides functionality to browse through
explanations why data should be trusted
- handles the actual trust decisions
using query-specific trust policies
- stores the aggregate information
- handles the aggregation of information
from different sources
- add provenance metadata to the
information
Conclusion
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Ontologies  Reasoning  Agents  Trust
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Knowledge representation is very well developed in
semantic web.
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We need to
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move from tools to autonomous systems that work on our behalf
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introduce formal semantics (machine-understandable content)
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Semantic web trust and agent communication still remains the least
explored of all the layers of semantic web.
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Search engines based on ontologies have already come up
http://swoogle.umbc.edu/
Bibliography
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Explorers-guide-to-the-semantic-web (by Thomas B. Passin)
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Agency and the Semantic web (by Christopher Walten)
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Using Context- and Content-Based Trust Policies on the Semantic Web.
WWW2004 : Christian Bizer, Radoslaw Oldakowski.
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An Integration of Reputation-Based and Policy-Based Trust Management
The Semantic Web and Policy Workshop at ISWC2005
(citeseer.ist.psu.edu/576212.html)
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http://www.scientificamerican.com/article.cfm?articleID=00048144-10D21C70-84A9809EC588EF21&catID=2 (by Tim Berners-Lee)
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Semantic Web Services (IEEE, 2001), Sheila A. McIlraith, Tran Cao Son,
and Honglei Zeng, Stanford University.
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Wikipedia (http://www.wikipedia.com)
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http://logicerror.com/semanticWeb-long