Ontology Cogito: Understanding what it is

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Ontology Cogito Understanding what it is?

• Muhammad Rafi

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Agenda

• What is Ontology?
• Ontology as Knowledge Representation
• Components of an Ontology
• Representation
• Languages
• Development method Life Cycle
• Example

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Knowledge?

• Knowledge all information and an understanding
  to carry out tasks and to infer new information
• Information -- data equipped with meaning
• Data -- un-interpreted signals that reach our
  senses
• Intelligence- play of knowledge

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Knowledge lt-gt Symbolism

• Newell
• A symbolic representation is enough to represent
  an intelligent activity. (Modern Computers)
• Allen Turing
• Turing Test to judge/measure Intelligence
• Algorithmic /Procedural/Actionable
• How?

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Knowledge Representation

• Procedural vs. Declarative
• For simple data access File vs. RDBMS
• For Semantic data access slots-Filler
  structures, Frame, Topic map, Ontology

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Approaches Knowledge representation

• Representational Adequacy
• Inferential Adequacy
• Inferential Efficiency
• Acquisitional Efficiency

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Definition Ontology

• Conceptualization of a domain of interest
• Concepts, relations, attributes, constraints,
  objects, values
• An ontology is a specification of a
  conceptualization
• Formal notation
• Documentation
• A variety of forms, but includes
• A vocabulary of terms
• Some specification of the meaning of the terms

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Ontology-Key Aspect

• Focus on semantics!
• Not file formats
• Not number of bytes
• Accurately model the information content of a
  complex domain
• Capture semantic nuances
• Rigorously define what each field in a database
  means, Adhere to those definitions!
• Ontologies should be self-documenting in two
  senses
• Allow people to browse the ontology and learn it
• Encode the definition of a concept such that the
  computer understands its meaning

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What does an Ontology do?

• Captures knowledge
• Creates a shared understanding between humans
  and for computers
• Makes knowledge machine consumable
• Makes meaning explicit by definition and
  context

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Components of Ontology

• Concepts Class of individuals The concept
  Protein
• Relationships between concepts
• Is a kind of relationship forms a taxonomy
• Other relationships give further structure is a
  part of
• Axioms Disjointness, covering, equivalence,

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Ontology Representation

• Expressiveness
• The range of constructs that can be used to
  formally, flexibly, explicitly and accurately
  describe the ontology
• Ease of use
• Computational complexity
• Is the language computable in real time?
• Rigour -- Satisfiability and consistency of the
  representation
• Systematic enforcement mechanisms
• Unambiguous, clear and well defined semantics

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Ontology Languages

• Vocabularies using natural language
• Hand crafted, flexible but difficult to evolve,
  maintain and keep consistent, with weak semantics
• Gene Ontology
• Object-based KR frames
• Extensively used, good structuring, intuitive.
  Semantics defined by OKBC standard
• EcoCyc (uses Ocelot) and RiboWeb (uses
  Ontolingua)
• Logic-based Description Logics
• Very expressive, model is a set of theories, well
  defined semantics
• Automatic derived classification taxonomies
• Concepts are defined and primitive

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Ontology Development Lifecycle

• Two kinds of complementary methodologies emerged
• Stage-based, e.g. TOVE Uschold96
• Iterative evolving prototypes, e.g. MethOntology
  Gomez Perez94.
• Most have TWO stages
• Informal stage
• ontology is sketched out using either natural
  language descriptions or some diagram technique
• Formal stage
• ontology is encoded in a formal knowledge
  representation language, that is machine
  computable
• the informal representation helps the former
• the formal representation helps the latter

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The V-model Methodology
Ontology in Use
Evaluation coverage, verification, granularity
Identify purpose and scope
Knowledge acquisition
User Model
Conceptualisation Principles commitment,
conciseness, clarity, extensibility, coherency
Conceptualisation
Integrating existing ontologies
Conceptualisation Model
Encoding/Representation principles encoding
bias, consistency, house styles and standards,
reasoning system exploitation
Encoding
Representation
Implementation Model
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Ontology Building Life Cycle
Identify purpose and scope
Consistency Checking
Knowledge acquisition
Building
Language and representation
Conceptualisation
Integrating existing ontologies
Available development tools
Encoding
Ontology Learning
Evaluation
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Example

• Cue sport game
• Snooker
• Billiards
• Pool
• Entities
• Table
• Ball
• Player

• Rules
• Potting rules
• Wining rules
• Progress rules

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Questions????