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CROC — a Representational Ontology for Concepts

CROC — a Representational Ontology for Concepts. Contents. Introduction Semantic Web Conceptuology Language CROC — a Representational Ontology for Concepts. Semantic Web. Making Web content understandable for intelligent agents RDF/RDFS/OWL ontologies (state of art) that define classes

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CROC — a Representational Ontology for Concepts

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  1. CROC — a Representational Ontology for Concepts

  2. Contents • Introduction • Semantic Web • Conceptuology • Language • CROC — a Representational Ontology for Concepts

  3. 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?

  4. Classification • Different classifications: “world-views” • Classification needs identification

  5. Communication (I) • Communication: • (1) expresses using symbols • (2) reads what is expressed • Interoperability problem when one doesn’t know the symbols

  6. 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

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

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

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

  10. 1 http://en.wikipedia.org/wiki/Image:Pupppppy.jpg Concept for dogs 2

  11. Common sense • Computers usually don’t have much common sense: they are deaf, blind, tasteless, touchless, etc. • Do they need it for having concepts?

  12. 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]

  13. 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

  14. Examples (I) A: “Swans are white.” OWL B: (OK, I’ll 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”.)

  15. 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’)

  16. 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

  17. Examples (II) A: “I like Cicero’s De Oratore.” B: (I don’t 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.”

  18. Examples (III) (continued) B: (I see someone matches all inductive properties.) “Cicero is Marcus Tullius?” A: “Yes.” B: (I will merge the two concepts.)

  19. 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

  20. 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

  21. 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]

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

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