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Semantic Web
&
Cased Based Reasoning
October 2015
 Semantic Web Overview
 Semantic Web
 Motivations
 Ontology Languages
 Semantic Web and Cased Based Reasoning
 Cased Based Reasoning Overview
 Cased Based Reasoning
 CBR Process
 Conversational Cased Based Reasoning
Semantic Web Overview
 “The Semantic Web is a major research initiative of the World Wide
Web Consortium (W3C) to create a metadata-rich Web of resources
that can describe themselves not only by how they should be
displayed (HTML) or syntactically (XML), but also by the meaning of the
metadata.”
From W3C Semantic Web Activity Page
 “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-Lee, James Hendler, Ora Lassila,
The Semantic Web, Scientific American, May 2001
Motivations
 Difficulties to find, present, access, or maintain
available electronic information on the web
 Need for a data representation to enable software
products (agents) to provide intelligent access to
heterogeneous and distributed information.
The Semantic Stack and Ontology Languages
XML, XML Schema
RDF
DAML,
OIL,
DAML+OIL OWL Lite
RDF Schema
OWL DL
OWL Full
From “The Semantic Web” technical report by PierceThe Semantic Language Layer for the Web
A
B
A = Ontology languages based on XML syntax
B = Ontology languages built on top of RDF and RDF Schema
Resource Description Framework (RDF) - I
 Resource Description Framework (RDF) is a framework for
describing and interchanging metadata (data describing the web
resources).
 RDF provides machine understandable semantics for metadata.
This leads,
 better precision in resource discovery than full text search,
 assisting applications as schemas evolve,
 interoperability of metadata.
Resource Description Framework (RDF)- II
 RDF has following important concepts
 Resource : The resources being described by RDF are anything
that can be named via a URI.
 Property : A property is also a resource that has a name, for
instance Author or Title.
 Statement : A statement consists of the combination of a
Resource, a Property, and an associated value.
Example: Alice is the creator of the resource http://www.cs.indiana.edu/~Alice.
The Dublin Core Definition Standard
 RDF is dependent on metadata conventions for definitions.
 The Dublin Core is an example definition standard which
defines a simple metadata elements for describing Web
authoring.
 It is named after 1995 Dublin (Ohio) Metadata Workshop.
 Following list is the partial tag element list for Dublin Core
standard.
 Creator: the primary author of the content
 Date: date of creation or other important life cycle events
 Title: the name of the resource
 Subject: the resource topic
 Description: an account of the content
 Type: the genre of the content
 Language: the human language of the content.
Example
http://www.cs.indiana.edu/~Alice
creator
=
http://purl.org/dc/elements/1.1/creator
Alice is the creator of the resource http://www.cs.indiana.edu/~Alice.
• Property “creator” refers to a specific definition. (in this example by Dublin Core
Definition Standard). So, there is a structured URI for this property. This URI makes this
property unique and globally known.
• By providing structured URI, we also specified the property value Alice as following.
“http://www.cs.indiana.edu/People/auto/b/Alice”
Alice
Resource
Property
Property
Value
Inspired from “The Semantic Web” technical report by Pierce
Example
Alice is the creator of the resource http://www.cs.indiana.edu/~Alice.
Inspired from “The Semantic Web” technical report by Pierce
<rdf:RDF xmlns:rdf=”http://www.w3c.org/1999/02/22-rdf-syntax-ns##”
xmlns:dc=”http://purl.org/dc/elements/1.1”
xmlns:cgl=”http://cgl.indiana.edu/people”>
<rdf:Description about=” http://www.cs.indiana.edu/~Alice”>
<dc:creator>
<cgl:staff> Alice </cgl:staff>
</dc:creator>
</rdf:RDF>
• Information in the graph can be modeled in diff. XML organizations. Human readers would
infer the same structure, however, general purpose applications would not.
•Given RDF model enables any general purpose application to infer the same structure.
Why bother to use
RDF instead of XML?
RDF Schema (RDFS )
 RDF Schema is an extension of Resource Description Framework.
 RDF Schema provides a higher level of abstraction than RDF.
 specific classes of resources ,
 specific properties,
 and the relationships between these properties and other resources can be
described.
 RDFS allows specific resources to be described as instances of more
general classes.
 RDFS provides mechanisms where custom RDF vocabulary can be
developed.
 Also, RDFS provides important semantic capabilities that are used by
enhanced semantic languages like DAML, OIL and OWL.
It resembles
objected-oriented
programming
 No standard for expressing primitive data types such as integer, etc. All
data types in RDF/RDFS are treated as strings.
 No standard for expressing relations of properties (unique, transitive,
inverse etc.)
 No standard for expressing whether enumerations are closed.
 No standard to express equivalence, disjointedness etc. among
properties
Limitations of RDF/RDFS
 RDFRDFS define a framework, however they have limitations. There is a
need for new semantic web languages with following requirements
 They should be compatible with (XML, RDF/RDFS)
 They should have enough expressive power to fill in the gaps in
RDFS
 They should provide automated reasoning support
 Ontology Inference Layer (OIL) and DARPAAgent Markup Language
(DAML) are two important efforts developed to fulfill these requirements.
 Their combined efforts formed DAML+OIL declarative semantic language.
DAML, OIL and DAML+OIL - I
 DAML+OIL is built on top of RDFS.
 It uses RDFS syntax.
 It has richer ways to express primitive data types.
 DAML+OIL allows other relationships (inverse and transitivity) to be
directly expressed.
 DAML+OIL provides well defined semantics, This provides followings:
 Meaning of DAML+OIL statements can be formally specified.
 Machine understanding and automated reasoning can be supported.
 More expressive power can be provided.
DAML, OIL and DAML + OIL - II
Example: T. Rex is not herbivore and not a currently living species.
 This statement can be expressed in DAML+OIL, but not in RDF/RDFS
since RDF/RDFS cannot express disjointedness.
 DAML+OIL provides automated reasoning by providing such expressive
power.
 For instance, a software agent can find out the “list of all the carnivores that
won’t be any threat today” by processing the DAML+OIL data representation
of the example above.
 RDF/RDFS does not express “is not” relationships and exclusions.
Example
How is DAML+OIL is
different than RDF/RDFS?
From “The Semantic Web” technical report by Pierce
 Web Ontology Language (OWL) is another effort developed by the OWL
working group of the W3Consorsium.
 OWL is an extension of DAML+OIL.
 OWL is divided following sub languages.
 OWL Lite
 OWL (Description Logics) DL
 OWL Full – limited cardinality
 OWL Lite provides many of the facilities of DAML+OIL provides. In
addition to RDF/RDFS tags, it also allows us to express equivalence,
identity, difference, inverse, and transivity.
 OWL Lite is a subset of OWL DL, which in turn is a subset of OWL Full.
Web Ontology Language (OWL)
 Developing new tools, applications and architectures on top of the
Semantic Web is the real challenge.
 AI techniques should be used to utilize the Semantic Web up to its
potentials.
 CBR is an AI technique based on reasoning on stored cases.
 CBR technique can be applied to do intelligent retrieval on metadata of
codes related Earthquake Science.
From Semantic Web to Cased Based
Reasoning
 CBR is reasoning by remembering: It is a starting point for new
reasoning
 Problem-solving: CBR solves new problems by retrieving and adapting
records from similar prior problems.
 Interpretive/classification: CBR understands new situations by
comparing and contrasting them to similar situations in the past
 Case-based reasoning is a methodology of reasoning from specific
experiences, which may be applied using various technologies (Watson
98)
What is CBR?
Overview of Case-Based Reasoning
Everyday Examples of CBR
 Remembering today’s route from the place you live to campus and
taking the same route.
 Diagnosing a computer problem based on a similar prior problem.
 Predicting an opponent’s actions based on how they acted under
similar past circumstances
 Assessing a hiring candidate by comparing and contrasting to existing
employees
What is CBR?
CBR Process
 What is a Case?
 Input cases are descriptions of a specific problem.
 Stored cases encapsulate previous specific
problem situations with solutions.
 Another way to look at it:
 Stored cases contain a lesson and a specific
context where the lesson applied.
 The context is used to determine when the
lesson may apply again.
CBR Process
 When and how are cases used?
Given a Problem Description (P.D.) to be solved,
CBR follows a cyclical process.
 REtrieve the most similar case(s)
 REuse the case(s) to attempt to solve the problem
 REvise the proposed solution if necessary
 REtain the new solution as a part of new case.
CBR Process
Problem
Retrieve
Reuse
Revise
Retain
Proposed solutionConfirmed solution
Case-Base
The CBR Cycle
 CCBR is a method of CBR where user interacts
with the system to retrieve the right cases.
 System responds with ranked cases and questions
at each step
 Question-answer-ranking cycle continues until
success or failure
 CCBR facilities
 Question management facility
 Case management facility
 GUI for user-system interaction
 Facilities to display questions or cases
A Prototype CCBR Application
 Purpose
 Intelligent retrieval on metadata describing codes written for
earthquake science.
 Guidance on how to run the codes to get reasonable results.
 Guidance for inexpert users to browse and select codes
 Casebase
 disloc - produces surface displacements based on multiple arbitrary
dipping dislocations in an elastic half-space
 simplex - inverts surface geodetic displacements to produce fault
parameters
 VC - simulates interactions between vertical strike slip faults.
 Classification
 Initial effort – dummy cases created to classify the different codes
 A general approach is needed
CCBR CASE
Problem Solution
Feature
Feature
Feature
= <Question, Answer>
How does Case Ranking take place in CCBR?
 Retrieved cases are sorted based on their consistency with
the query case.
 As the questions are answered more cases are eliminated.
 A case is ruled out only if there is a conflict between the case
and the query case
 Consistency number for a case remains same if the case has
no answer for the question.
 Consistency number for a case gets incremented if the case
has the same answer to the question as the query case.
CCBR CASEBASE
Case
Feature 1
Feature 2
Feature 5
Case
= <Problem, Solution>
Feature 1
Feature 2
Feature 3
Feature 4
A Case from
CASEBASE
Query Case
IF ((A.Feature1.Solution = B.Feature1.Solution) &
(A.Feature2.Solution = B.Feature2.Solution))
THEN Consistency # = 2
A B
How does question ranking take place in CCBR?
 Questions can be ranked based on their frequency factor
 Questions can be ranked based on predefined inference rules
 Only distinguishing questions are to be ranked
 Questions can be YES/NO questions, multiple choice
questions or questions with numerical answers.
 W3C Semantic Web Activity Page. Available from
http://www.w3.org/2001/sw/.
 T. Berners-Lee, J. Hendler, and O. Lassila, “The Semantic Web.” Scientific
American, May 2001.
 Resource Description Framework (RDF)/W3C Semantic Web Activity Web
Site: http://www.w3.org/RDF/.
 D. Brickley and R. V. Guha (eds), “RDF Vocabulary Description Language
1.0: RDF Schema.” W3C Working Draft 23 January 2003.
 The DARPA Agent Markup Language Web Site: http://www.daml.org.
 OIL Project Web Site: http://www.ontoknowledge.org/oil
References

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RDFa Semantic Web

  • 1. Semantic Web & Cased Based Reasoning October 2015
  • 2.  Semantic Web Overview  Semantic Web  Motivations  Ontology Languages  Semantic Web and Cased Based Reasoning  Cased Based Reasoning Overview  Cased Based Reasoning  CBR Process  Conversational Cased Based Reasoning
  • 3. Semantic Web Overview  “The Semantic Web is a major research initiative of the World Wide Web Consortium (W3C) to create a metadata-rich Web of resources that can describe themselves not only by how they should be displayed (HTML) or syntactically (XML), but also by the meaning of the metadata.” From W3C Semantic Web Activity Page  “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-Lee, James Hendler, Ora Lassila, The Semantic Web, Scientific American, May 2001
  • 4. Motivations  Difficulties to find, present, access, or maintain available electronic information on the web  Need for a data representation to enable software products (agents) to provide intelligent access to heterogeneous and distributed information.
  • 5. The Semantic Stack and Ontology Languages XML, XML Schema RDF DAML, OIL, DAML+OIL OWL Lite RDF Schema OWL DL OWL Full From “The Semantic Web” technical report by PierceThe Semantic Language Layer for the Web A B A = Ontology languages based on XML syntax B = Ontology languages built on top of RDF and RDF Schema
  • 6. Resource Description Framework (RDF) - I  Resource Description Framework (RDF) is a framework for describing and interchanging metadata (data describing the web resources).  RDF provides machine understandable semantics for metadata. This leads,  better precision in resource discovery than full text search,  assisting applications as schemas evolve,  interoperability of metadata.
  • 7. Resource Description Framework (RDF)- II  RDF has following important concepts  Resource : The resources being described by RDF are anything that can be named via a URI.  Property : A property is also a resource that has a name, for instance Author or Title.  Statement : A statement consists of the combination of a Resource, a Property, and an associated value. Example: Alice is the creator of the resource http://www.cs.indiana.edu/~Alice.
  • 8. The Dublin Core Definition Standard  RDF is dependent on metadata conventions for definitions.  The Dublin Core is an example definition standard which defines a simple metadata elements for describing Web authoring.  It is named after 1995 Dublin (Ohio) Metadata Workshop.  Following list is the partial tag element list for Dublin Core standard.  Creator: the primary author of the content  Date: date of creation or other important life cycle events  Title: the name of the resource  Subject: the resource topic  Description: an account of the content  Type: the genre of the content  Language: the human language of the content.
  • 9. Example http://www.cs.indiana.edu/~Alice creator = http://purl.org/dc/elements/1.1/creator Alice is the creator of the resource http://www.cs.indiana.edu/~Alice. • Property “creator” refers to a specific definition. (in this example by Dublin Core Definition Standard). So, there is a structured URI for this property. This URI makes this property unique and globally known. • By providing structured URI, we also specified the property value Alice as following. “http://www.cs.indiana.edu/People/auto/b/Alice” Alice Resource Property Property Value Inspired from “The Semantic Web” technical report by Pierce
  • 10. Example Alice is the creator of the resource http://www.cs.indiana.edu/~Alice. Inspired from “The Semantic Web” technical report by Pierce <rdf:RDF xmlns:rdf=”http://www.w3c.org/1999/02/22-rdf-syntax-ns##” xmlns:dc=”http://purl.org/dc/elements/1.1” xmlns:cgl=”http://cgl.indiana.edu/people”> <rdf:Description about=” http://www.cs.indiana.edu/~Alice”> <dc:creator> <cgl:staff> Alice </cgl:staff> </dc:creator> </rdf:RDF> • Information in the graph can be modeled in diff. XML organizations. Human readers would infer the same structure, however, general purpose applications would not. •Given RDF model enables any general purpose application to infer the same structure. Why bother to use RDF instead of XML?
  • 11. RDF Schema (RDFS )  RDF Schema is an extension of Resource Description Framework.  RDF Schema provides a higher level of abstraction than RDF.  specific classes of resources ,  specific properties,  and the relationships between these properties and other resources can be described.  RDFS allows specific resources to be described as instances of more general classes.  RDFS provides mechanisms where custom RDF vocabulary can be developed.  Also, RDFS provides important semantic capabilities that are used by enhanced semantic languages like DAML, OIL and OWL. It resembles objected-oriented programming
  • 12.  No standard for expressing primitive data types such as integer, etc. All data types in RDF/RDFS are treated as strings.  No standard for expressing relations of properties (unique, transitive, inverse etc.)  No standard for expressing whether enumerations are closed.  No standard to express equivalence, disjointedness etc. among properties Limitations of RDF/RDFS
  • 13.  RDFRDFS define a framework, however they have limitations. There is a need for new semantic web languages with following requirements  They should be compatible with (XML, RDF/RDFS)  They should have enough expressive power to fill in the gaps in RDFS  They should provide automated reasoning support  Ontology Inference Layer (OIL) and DARPAAgent Markup Language (DAML) are two important efforts developed to fulfill these requirements.  Their combined efforts formed DAML+OIL declarative semantic language. DAML, OIL and DAML+OIL - I
  • 14.  DAML+OIL is built on top of RDFS.  It uses RDFS syntax.  It has richer ways to express primitive data types.  DAML+OIL allows other relationships (inverse and transitivity) to be directly expressed.  DAML+OIL provides well defined semantics, This provides followings:  Meaning of DAML+OIL statements can be formally specified.  Machine understanding and automated reasoning can be supported.  More expressive power can be provided. DAML, OIL and DAML + OIL - II
  • 15. Example: T. Rex is not herbivore and not a currently living species.  This statement can be expressed in DAML+OIL, but not in RDF/RDFS since RDF/RDFS cannot express disjointedness.  DAML+OIL provides automated reasoning by providing such expressive power.  For instance, a software agent can find out the “list of all the carnivores that won’t be any threat today” by processing the DAML+OIL data representation of the example above.  RDF/RDFS does not express “is not” relationships and exclusions. Example How is DAML+OIL is different than RDF/RDFS? From “The Semantic Web” technical report by Pierce
  • 16.  Web Ontology Language (OWL) is another effort developed by the OWL working group of the W3Consorsium.  OWL is an extension of DAML+OIL.  OWL is divided following sub languages.  OWL Lite  OWL (Description Logics) DL  OWL Full – limited cardinality  OWL Lite provides many of the facilities of DAML+OIL provides. In addition to RDF/RDFS tags, it also allows us to express equivalence, identity, difference, inverse, and transivity.  OWL Lite is a subset of OWL DL, which in turn is a subset of OWL Full. Web Ontology Language (OWL)
  • 17.  Developing new tools, applications and architectures on top of the Semantic Web is the real challenge.  AI techniques should be used to utilize the Semantic Web up to its potentials.  CBR is an AI technique based on reasoning on stored cases.  CBR technique can be applied to do intelligent retrieval on metadata of codes related Earthquake Science. From Semantic Web to Cased Based Reasoning
  • 18.  CBR is reasoning by remembering: It is a starting point for new reasoning  Problem-solving: CBR solves new problems by retrieving and adapting records from similar prior problems.  Interpretive/classification: CBR understands new situations by comparing and contrasting them to similar situations in the past  Case-based reasoning is a methodology of reasoning from specific experiences, which may be applied using various technologies (Watson 98) What is CBR? Overview of Case-Based Reasoning
  • 19. Everyday Examples of CBR  Remembering today’s route from the place you live to campus and taking the same route.  Diagnosing a computer problem based on a similar prior problem.  Predicting an opponent’s actions based on how they acted under similar past circumstances  Assessing a hiring candidate by comparing and contrasting to existing employees What is CBR?
  • 20. CBR Process  What is a Case?  Input cases are descriptions of a specific problem.  Stored cases encapsulate previous specific problem situations with solutions.  Another way to look at it:  Stored cases contain a lesson and a specific context where the lesson applied.  The context is used to determine when the lesson may apply again.
  • 21. CBR Process  When and how are cases used? Given a Problem Description (P.D.) to be solved, CBR follows a cyclical process.  REtrieve the most similar case(s)  REuse the case(s) to attempt to solve the problem  REvise the proposed solution if necessary  REtain the new solution as a part of new case.
  • 23.  CCBR is a method of CBR where user interacts with the system to retrieve the right cases.  System responds with ranked cases and questions at each step  Question-answer-ranking cycle continues until success or failure
  • 24.  CCBR facilities  Question management facility  Case management facility  GUI for user-system interaction  Facilities to display questions or cases
  • 25. A Prototype CCBR Application
  • 26.  Purpose  Intelligent retrieval on metadata describing codes written for earthquake science.  Guidance on how to run the codes to get reasonable results.  Guidance for inexpert users to browse and select codes  Casebase  disloc - produces surface displacements based on multiple arbitrary dipping dislocations in an elastic half-space  simplex - inverts surface geodetic displacements to produce fault parameters  VC - simulates interactions between vertical strike slip faults.
  • 27.  Classification  Initial effort – dummy cases created to classify the different codes  A general approach is needed
  • 29. How does Case Ranking take place in CCBR?  Retrieved cases are sorted based on their consistency with the query case.  As the questions are answered more cases are eliminated.  A case is ruled out only if there is a conflict between the case and the query case  Consistency number for a case remains same if the case has no answer for the question.  Consistency number for a case gets incremented if the case has the same answer to the question as the query case.
  • 30. CCBR CASEBASE Case Feature 1 Feature 2 Feature 5 Case = <Problem, Solution> Feature 1 Feature 2 Feature 3 Feature 4 A Case from CASEBASE Query Case IF ((A.Feature1.Solution = B.Feature1.Solution) & (A.Feature2.Solution = B.Feature2.Solution)) THEN Consistency # = 2 A B
  • 31. How does question ranking take place in CCBR?  Questions can be ranked based on their frequency factor  Questions can be ranked based on predefined inference rules  Only distinguishing questions are to be ranked  Questions can be YES/NO questions, multiple choice questions or questions with numerical answers.
  • 32.  W3C Semantic Web Activity Page. Available from http://www.w3.org/2001/sw/.  T. Berners-Lee, J. Hendler, and O. Lassila, “The Semantic Web.” Scientific American, May 2001.  Resource Description Framework (RDF)/W3C Semantic Web Activity Web Site: http://www.w3.org/RDF/.  D. Brickley and R. V. Guha (eds), “RDF Vocabulary Description Language 1.0: RDF Schema.” W3C Working Draft 23 January 2003.  The DARPA Agent Markup Language Web Site: http://www.daml.org.  OIL Project Web Site: http://www.ontoknowledge.org/oil References