CROC

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CROC a Representational Ontology for Concepts
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Contents

• Introduction
• Semantic Web
• Conceptuology
• Language
• CROC a Representational Ontology for Concepts

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Semantic Web

• Making Web content understandable for intelligent
  agents
• RDF/RDFS/OWL ontologies (state of art) that
  define classes
• The interoperability problem how to merge
  different world-views?

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Classification

• Different classifications world-views
• Classification needs identification

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Communication (I)

• Communication
• (1) expresses using symbols
• (2) reads what is expressed
• Interoperability problem when one doesnt know
  the symbols

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CYC a shared classification?

• CYC.com developing one big classification
• One world-view
• not soon or never complete
• agents have own interests and pick up other ideas
  (autonomy)
• conceptions may be different from agent to agent

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Mapping world-views?

• Should we map classifications to solve the
  interoperability problem?
• Rather think about the identification mechanism
  (for a Semantic Web!).

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Communication (II)

• Communication
• (1) represents
• (2) identifies and classifies
• Problem when the receiving agent cannot identify
  the representation

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Identification conceptuology

• A concept
• (fuzzy / partial) definition?
• prototyping?
• an ability to reidentify for a purpose
  1Millikan, On Clear and Confused Ideas An
  Essay on Substance Concepts
• Most concepts are not classes

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Concept for dogs
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Common sense

• Computers usually dont have much common sense
  they are deaf, blind, tasteless, touchless, etc.
• Do they need it for having concepts?

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Language

• Same concepts, different conceptions
• Having concepts entirely through language

It is common to have a substance concept
entirely through the medium of language. It is
possible to have it, that is, while lacking any
ability to recognize the substance in the flesh.
1, Ch. 6
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CROC a Representational Ontology for Concepts
(I)

• Lexical representations for concepts
• Concepts have names (so can be shared by
  language)
• Where the name fails, CROC uses induction or
  deduction using the related knowledge to the
  concept
• Representation, using other concepts
• Descriptions instead of definitions

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Examples (I)

• A Swans are white.
• OWL B (OK, Ill take that into the class
  definition.)
• CROC B (OK, nice to know.)
• A There is a black swan.
• CROC B (OK, nice to know.)
• OWL B (Error in 1, or unalignable classes for
  swan.)

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CROC a Representational Ontology for Concepts
(II)

• Concepts for every unit of representation
• Subjects, subdivided in Kinds (like a dog),
  Individuals (like Oscar), and Stuffs (like
  gold)
• Substances
• Properties (like colour)
• Happenings (events, situations)
• Predicates (like poor, eager)
• Relations (like of, in, at)

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CROC a Representational Ontology for Concepts
(III)

• Abilities to gather, store and query
  representational information for reidentification
• Storage of statements (happenings) about concepts
• Subject templates to gather information
• Semantical tableaux for reasoning about statements

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Examples (II)

• A I like Ciceros De Oratore.
• B (I dont know that word.) Cicero??
• A (I will answer what I know is relevant for
  humans.) Cicero is a human. He was born in
  Arpinum.
• B (I have other relevant questions about
  humans.) Where did he live?
• A In Rome.

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Examples (III)

• (continued)
• B (I see someone matches all inductive
  properties.) Cicero is Marcus Tullius?
• A Yes.
• B (I will merge the two concepts.)

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CROC a Representational Ontology for Concepts
(IV)

• Our goal is not primarily knowledge
  representation, but agent communication and
  understanding
• Agents have their own conceptuology
• No need for division of linguistic labour (where
  only experts own the concept)
• Private concepts and conceptions are welcome
  (autonomy)
• Easy learning of new concepts

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Conclusions

• Identification by name will be able to solve the
  interoperability problem (for a great deal)
• concepts for every part of the representation
• agents can have own conceptuologies
• Concepts may be grounded entirely in lexical
  representations

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Future work

• Higher-order reasoning about what other agents
  believe, etc.
• A temporal logic for reasoning with statements
• Integrating classification systems (efficient
  knowledge representation)
• The language-thought partnership Millikan,
  Language A Biological Model, Ch. 5

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Thank you for your attention
http//sourceforge.net/projects/croc