This document provides an overview of the Semantic Web and Case-Based Reasoning (CBR). It defines the Semantic Web and its goals of making web resources machine-understandable. It describes languages used in the Semantic Web like RDF, RDF Schema, DAML+OIL, and OWL. It also provides an overview of CBR, the CBR process, and Conversational CBR. Finally, it proposes a prototype application that uses CBR techniques to intelligently retrieve metadata about earthquake simulation codes from the Semantic Web.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/owl-web-ontology-language.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=5Kr4JzqDO_w
The lecture covers:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions? -Property Restrictions
- Class Expressions? -Cardinality Restrictions
The OWL Web Ontology Language enables software engineers to define ontologies of domain knowledge which can be queried and reasoned over by software agents. OWL facilitates greater machine interpretability of content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
This presentation is about:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions -Property Restrictions
- Class Expressions -Cardinality Restrictions
WebSpa is a tool that allows the quick, intuitive (and even fun) interrogation of arbitrary SPARQL endpoints. WebSpa runs in the web browser and does not require the installation of any additional software. The tool manages a large variety of pre-defined SPARQL endpoints and allows the addition of new ones. An user account gives the possibility of saving both the interrogation and its results on the local computer, as well as further editing of the queries. The application is written in both Java and Flex. It uses Jena and ARQ application programming interface in order to perform the queries, and the results are processed and displayed using Flex.
The Semantic Web is a vision of information that is understandable by computers. Although there is great exploitable potential, we are still in "Generation Zero'' of the Semantic Web, since there are few real-world compelling applications. The heterogeneity, the volume of data and the lack of standards are problems that could be addressed through some nature inspired methods. The paper presents the most important aspects of the Semantic Web, as well as its biggest issues; it then describes some methods inspired from nature - genetic algorithms, artificial neural networks, swarm intelligence, and the way these techniques can be used to deal with Semantic Web problems.
These slides were presented as part of a W3C tutorial at the CSHALS 2010 conference (http://www.iscb.org/cshals2010). The slides are adapted from a longer introduction to the Semantic Web available at http://www.slideshare.net/LeeFeigenbaum/semantic-web-landscape-2009 .
A PDF version of the slides is available at http://thefigtrees.net/lee/sw/cshals/cshals-w3c-semantic-web-tutorial.pdf .
The Web is a universal medium for information, data and knowledge exchange. The Semantic Web is an extension of the World Wide Web, ``in which information is given well-defined meaning, better enabling computers and people to work in cooperation''\cite{semweb:lee}. RDF, together with SparQL, provide a powerful mechanism for describing and interchanging metadata on the web. This paper presents briefly the two concepts - RDF, SparQL - and three of the most popular frameworks (written in Java) that offer support for RDF: Jena, Sesame and JRDF.
This presentation was given at the Balisage 2017 conference, and provides an overview of three key RDF standards for constraint modeling, annotation and the use of data frames and cubes in RDF.
Although animals do not use language, they are capable of many of the same kinds of cognition as us; much of our experience is at a non-verbal level.
Semantics is the bridge between surface forms used in language and what we do and experience.
Language understanding depends on world knowledge (i.e. “the pig is in the pen” vs. “the ink is in the pen”)
We might not be ready for executives to specify policies themselves, but we can make the process from specification to behavior more automated, linked to precise vocabulary, and more traceable.
Advances such as SVBR and an English serialization for ISO Common Logic means that executives and line workers can understand why the system does certain things, or verify that policies and regulations are implemented
Explicit Semantics in Graph DBs Driving Digital Transformation With Neo4jConnected Data World
Dr. Jesús Barrasa's slides from his talk at Connected Data London. Jesús, who is a senior field engineer at Neo4j presented how semantic web principles can be used in a graph database.
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/owl-web-ontology-language.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=5Kr4JzqDO_w
The lecture covers:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions? -Property Restrictions
- Class Expressions? -Cardinality Restrictions
The OWL Web Ontology Language enables software engineers to define ontologies of domain knowledge which can be queried and reasoned over by software agents. OWL facilitates greater machine interpretability of content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
This presentation is about:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions -Property Restrictions
- Class Expressions -Cardinality Restrictions
WebSpa is a tool that allows the quick, intuitive (and even fun) interrogation of arbitrary SPARQL endpoints. WebSpa runs in the web browser and does not require the installation of any additional software. The tool manages a large variety of pre-defined SPARQL endpoints and allows the addition of new ones. An user account gives the possibility of saving both the interrogation and its results on the local computer, as well as further editing of the queries. The application is written in both Java and Flex. It uses Jena and ARQ application programming interface in order to perform the queries, and the results are processed and displayed using Flex.
The Semantic Web is a vision of information that is understandable by computers. Although there is great exploitable potential, we are still in "Generation Zero'' of the Semantic Web, since there are few real-world compelling applications. The heterogeneity, the volume of data and the lack of standards are problems that could be addressed through some nature inspired methods. The paper presents the most important aspects of the Semantic Web, as well as its biggest issues; it then describes some methods inspired from nature - genetic algorithms, artificial neural networks, swarm intelligence, and the way these techniques can be used to deal with Semantic Web problems.
These slides were presented as part of a W3C tutorial at the CSHALS 2010 conference (http://www.iscb.org/cshals2010). The slides are adapted from a longer introduction to the Semantic Web available at http://www.slideshare.net/LeeFeigenbaum/semantic-web-landscape-2009 .
A PDF version of the slides is available at http://thefigtrees.net/lee/sw/cshals/cshals-w3c-semantic-web-tutorial.pdf .
The Web is a universal medium for information, data and knowledge exchange. The Semantic Web is an extension of the World Wide Web, ``in which information is given well-defined meaning, better enabling computers and people to work in cooperation''\cite{semweb:lee}. RDF, together with SparQL, provide a powerful mechanism for describing and interchanging metadata on the web. This paper presents briefly the two concepts - RDF, SparQL - and three of the most popular frameworks (written in Java) that offer support for RDF: Jena, Sesame and JRDF.
This presentation was given at the Balisage 2017 conference, and provides an overview of three key RDF standards for constraint modeling, annotation and the use of data frames and cubes in RDF.
Although animals do not use language, they are capable of many of the same kinds of cognition as us; much of our experience is at a non-verbal level.
Semantics is the bridge between surface forms used in language and what we do and experience.
Language understanding depends on world knowledge (i.e. “the pig is in the pen” vs. “the ink is in the pen”)
We might not be ready for executives to specify policies themselves, but we can make the process from specification to behavior more automated, linked to precise vocabulary, and more traceable.
Advances such as SVBR and an English serialization for ISO Common Logic means that executives and line workers can understand why the system does certain things, or verify that policies and regulations are implemented
Explicit Semantics in Graph DBs Driving Digital Transformation With Neo4jConnected Data World
Dr. Jesús Barrasa's slides from his talk at Connected Data London. Jesús, who is a senior field engineer at Neo4j presented how semantic web principles can be used in a graph database.
MR^3: Meta-Model Management based on RDFs Revision ReflectionTakeshi Morita
We propose a tool to manage several sorts of relationships among RDF and RDFS. Our tool consists of three main functions: graphical editing of RDF contents, graphical editing of RDFS contents, and meta-model management facility. Metamodel management facility supports maintenance of relationship between RDF and RDFS contents. The above facilities are implemented based on plug-in system. We provide basic plug-in modules for consistency checking of RDFS classes and properties. The prototyping tool, called MR^3 (Meta-Model Management based on RDFs Revision Reflection), is implemented by Java language. Through the experiment of using MR^3, we show how MR^3 contributes the Semantic Web paradigm from the standpoint of RDFs contents management.
Semantic - Based Querying Using Ontology in Relational Database of Library Ma...dannyijwest
The traditional Web stores huge amount of data in the form of Relational Databases (RDB) as it is good at
storing objects and relationships between them. Relational Databases are dynamic in nature which allows
bringing tables together helping user to search for related material across multiple tables. RDB are
scalable to expand as the data grows. The RDB uses a Structured Query Language called SQL to access
the databases for several data retrieval purposes. As the world is moving today from the Syntactic form to
Semantic form and the Web is also taking its new form of Semantic Web. The Structured Query of the RDB
on web can be a Semantic Query on Semantic Web.
This tutorial explains the Data Web vision, some preliminary standards and technologies as well as some tools and technological building blocks developed by AKSW research group from Universität Leipzig.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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
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