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  The interoperability problem: how to merge different world-views?

Classification  Different classifications: “world-views”  Classification needs identification

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

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

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

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

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

Concept for dogs 1 2

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

Language  Same concepts, different conceptions  Having concepts entirely through language [1, Ch. 6] “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]

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

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”.)

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’)

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

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.”

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

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

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

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]

Thank you for your attention