Knowledge Modeling, use of information sources in the study of domains and interdomain relationships

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• Knowledge Modeling, use of information sources
  in the study of domains and inter-domain
  relationships
• - A Learning Paradigm
• by
• Sanjeev Thacker

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Introduction

• Introduction
• Background
• ADEPT
• Problems
• Contributions

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Background

• Web is an ever-increasing source of information
• Information of interest to user is distributed
  across multiple heterogeneous sources
• Need for integration to provide a one point
  access for querying

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ADEPT

• Besides querying, use the data sources to extract
  useful knowledge
• Provide an environment for studying domains
• Provide means to study and explore complex
  inter-domain relationships
• Ability to pose complex information requests
  across multiple domains

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Problems

• Diverse and distributed sources
• Web sources unlike database
• Unstructured or semi-structured
• Inconsistencies and information overlapping
• Heterogeneities
• Semantic
• Structural
• Syntactic

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Problems

• Representation of complex relationships
• Use of Knowledge Model for complex information
  request capability with embedded semantic
  information

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Contribution

• Knowledge Model
• Information Scape Model
• Learning Paradigm
• Visual Interfaces

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Outline

• Knowledge Modeling
• Information Scapes
• Learning Paradigm
• Visual Interfaces
• Related Work
• Future Work
• Demo

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Knowledge Modeling

• Approach to source modeling
• Global model and source model
• Source centric / query centric

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Source Centric

• Advantages
• Global model independent of source model
• Modeling a source is independent of other sources
• Dynamic addition, removal and modification of
  sources
• Global view remains unaffected
• No source mapping required during information
  integration
• More suitable for sources other than database
  sources ( web sources)

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Knowledge Base

• Comprises of
• Ontologies (Domain model)
• Resources
• Relationships
• Operations

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Domain Hierarchy
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Ontology

• Standardize meaning, description, representation
  of involved attributes
• Capture the semantics involved via domain
  characteristics
• Allow knowledge sharing and reuse
• Resolve resource model differences by mapping
  them to the global model of the ontology they
  represent
• Global interface

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Ontology

• Description includes
• Attributes
• Domain Rules
• Functional Dependencies

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Resource

• Desirable characteristics
• Add, modify and delete resources for an ontology
  dynamically without affecting the systems
  knowledge
• Specify the sources in a manner such that one can
  declaratively query them
• Since the number of resources is large there is a
  need to identify the exact usefulness of
  resources from the query viewpoint and prune the
  others

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Resource

• Description includes
• Attributes
• Binding Patterns
• Data Characteristics
• Local Completeness

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Relationships

• Simple relationships
• equals, less-than, like, is-a, is-part-of
• Are hierarchical or similarity based
• Complex relationships
• Earthquakes cause Tsunami, Nuclear explosions
  cause earthquakes, Air-pollution affects
  vegetation

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Relationships

• Characteristics
• Involves multiple ontologies
• Requires understanding the semantics involved in
  their interaction
• Cannot be expressed by simple relational and
  logical operators alone
• Involves use of complex operations like functions
  and simulations

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Relationship

• Example
• Nuclear explosion causes Earthquakes
• NuclearTest Causes Earthquake
• dateDifference(NuclearTest.eventDate,
  Earthquake.eventDate)lt30
• AND
• distance(NuclearTest.latitude,
• NuclearTest.longitude,
• Earthquake,latitude,
  Earthquake.longitude)lt10000

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Operations

• Functions, Simulations
• Functions
• user defined
• used to model the semantics involved in the
  relationships
• used in post processing of result data
• example distance, dateDifference
• Simulations
• independent programs
• used for post processing of result data
• example clarke urban growth model

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Information Scape(Iscape)

• Representation of an information request across
  multiple domains
• Can be deployed and executed
• Sources not explicitly specified like in a query
• System is aware of the sources and is able to
  identify the useful sources
• Semantic correlation across domains is embedded
  within the information request

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Information Scape

• Definition
• An IScape may be defined as information request
  over distributed heterogeneous sources of
  information involving multiple ontologies and the
  relationships between them that contains
  meta-information constructed to facilitate the
  bridging of semantic relationships between
  individual sources.

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Information Scape

• Ontologies
• Relationships
• Constraint
• Conjunctive boolean expression
• Runtime configurable constraint
• Conceptually different
• Grouping and group constraint
• Similar to having clause in SQL
• Projection list

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Learning Paradigm

• Study of domain
• Use IScapes to study the domain interaction by
  using relationships
• Relationships could lead to transitive findings
• Explore the hypothetical relationships to
  validate and establish them or invalidate them

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Learning Paradigm

• Data mining
• Age and breast cancer
• Relationships
• Nuclear Explosion causes Earthquakes
• Post processing
• Functions
• Simulations
• Charting tool

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Learning Paradigm

• Find the earliest recorded Nuclear test conducted
• Plot a graph of the average number of Earthquakes
  of magnitude greater than 5.8 per year starting
  from 1900
• Find the average number of Earthquakes of
  magnitude greater than 5.8 between 1900-1949 and
  between 1950-present

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Learning Paradigm

• Find the average number of Earthquakes of
  magnitude greater than 7 between 1900-1949 and
  between 1950-present
• Find pairs of Nuclear tests and Earthquakes that
  occurred with a certain radius and a certain time
  period of the explosion

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Visual Interfaces

• Knowledge Builder
• IScape Builder
• Web Interface
• IScape Processing Monitor

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Knowledge Builder

• GUI to build the knowledge base
• fast and easy to use
• Manually creating the knowledge could be arduous
  and error prone
• Knowledge is stored in the standard XML format
• Abstraction from the underlying format and other
  technical details

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Knowledge Builder

• Assists in the creation, deletion and
  modification of the knowledge base
• Automatically creates a knowledge tree that
  assists in relating the knowledge in a better
  manner

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Knowledge Builder
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Knowledge Hierarchy
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IScape Builder

• GUI to create, deploy and execute IScapes in a
  step by step manner
• IScape stored in XML format
• User abstraction to the underlying structure
• Validity checks implemented
• Integrated tools
• the charting tool to plot charts with the result
  data

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IScape Builder
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Web Interface

• Web accessible
• Knowledge Base
• Existing Iscapes
• Set the runtime configurable constraint
• Execute existing IScapes
• View the tabulated results
• Cannot create new IScapes

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Web InterfaceResult Screen
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IScape Processing Monitor

• Color coded log entries describing the IScape
  processing are generated
• Brief message along with agent name
• Time stamp
• detailed description and associated data, if any
• IScape plan for the existing sources
• Intermediate results
• High level debugging tool
• Understand execution, locate failures
• Not available with the web interface

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Monitor GUI
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Related Work

• State of the art
• SIMS, TSIMMIS, Information Manifold, Observer,
  Infosleuth
• Mainly focussed on one point access for querying
  of integrated data of a domain
• What makes ADEPT unique
• Relationships, IScapes, learning paradigm
  distinguishes our system from any prior work

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Future Work

• Support rules of type if-then and use of
  induction learning to speed up the processing
• Recursive query capability required
• IScape over Iscape support required
• Simulations currently supported as specialized
  function in our framework
• Statistical analysis tools like SAS for time
  series analysis, logistic regression