1. Artificial Intelligence – CS364 Knowledge Representation Lectures on Artificial Intelligence – CS364 Conceptual Dependency 20 th September 2005 Dr Bogdan L. Vrusias b.vrusias@surrey.ac.uk

2. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 20052 Contents Definition of Conceptual Dependency Grammar Building blocks Advantages and disadvantages Exercises

3. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 20053 Concepts and Representation A number of authors in AI have addressed the question of the 'concept'-based organisation of knowledge and we use two examples to illustrate this: –Firstly, we consider a verb-oriented organisation of knowledge proposed by Schank: Conceptual Dependency Grammar. –Then we go on to discuss a highly nominalised system proposed by Sowa: Conceptual Graphs.

4. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 20054 Conceptual Dependency Conceptual dependency (or CD) is a theory of how to represent the meaning of natural language sentences in a way that: –First, facilitates for drawing inferences from the sentences. –Second, the representation (CD) is independent of the language in which the sentences were originally stated.

5. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 20055 Conceptual Dependency Theory Schank's (1975) Conceptual Dependency Theory was developed as part of a natural language comprehension project. Schank's claim was that sentences can be translated into basic concepts expressed as a small set of semantic primitives. Conceptual dependency allows these primitives, which signify meanings, to be combined to represent more complex meanings. Schank calls the meaning propositions underlying language "conceptualisations".

6. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 20056 Conceptual Dependency Theory Schank’s project is the ‘representation of meaning in an unambiguous language-free manner’ (1973:187). ‘Any two utterances that can be said to mean the same thing, whether they are in the same or different languages, should be characterised in only one way by the conceptual structure’ (1973:191) Towards a representation ‘in terms that are as interlingual and as neutral as possible’ (ibid.)

7. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 20057 CD Building Blocks CD theorists argue that –"the CD representation of a sentence is built not out of primitives corresponding to the words used in the sentence, but rather out of conceptual primitives that can be combined to form the meanings of words in any particular language" Building Blocks –Primitive conceptualizations (conceptual categories) –Conceptual dependencies (diagrammatic conventions) –Conceptual cases –Primitive acts –Conceptual tenses

8. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 20058 Primitive Conceptualizations Schank emphasises analysis of a sentence/utterance at the conceptual level or to analyse conceptualisation. Conceptual dependency theory of four primitive conceptualizations: –actions (ACT: actions) –objects (PP: picture producers) –modifiers of actions (AA: action aiders) –modifiers of objects (PA picture aiders)

9. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 20059 Concept can be An abstract or concrete object that invokes an image –"cars" are concrete objects –"gravity" is an abstract concept An object (nominal) produces a picture (PP) Something an animate object does. –"running" is an action A modifier that modifies an object or an action. A modifier that specifies an action or a nominal. –"blue" is a PA modifier (e.g. A blue car) –"quickly" is a AA modifier (e.g. He quickly run)

10. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200510 Conceptual Dependencies Conceptual categories (PP, ACT, PA and AA) relate to each other in specified ways. These relations are called dependencies by Schank. In a dependency relation, one partner or item is dependent and the other dominant or governing. A governor  dependent is a partially ordered relationship –A dependent must have a governor and is understood in terms of the governor –A governor may or may not have dependent(s) and has an independent existence –A governor can be a dependent PP and ACT are inherently governing categories. PA and AA are inherently dependent.

11. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200511 Conceptual Dependencies For a conceptualisation to exist, there must be at least two governors: –E.g. Sally stroked her fat cat PP: Sally, cat, her [Sally] ACT: stroke PA: fat Governors:Sally, stroke, cat Dependent:PP (cat) on ACT (stroke) PA (fat) on PP (cat) PP (cat) on PP (her[Sally])

12. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200512 Building CD graphs E.g. Sally stroked her fat cat –Sally and stroking are necessary for conceptualisation: there is a two-way dependency between each other: Sally  stroke –Sally’s cat cannot be conceptualised without the ACT stroke  it has an objective dependency on stroke Sally  stroke cat.

13. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200513 Building CD graphs E.g. Sally stroked her fat cat –The concept ‘cat’ is the governor for the modifier ‘fat’: Sally  stroke cat  fat –The concept PP(cat) is also governed by the concept PP(Sally) through a prepositional dependency: Sally  stroke cat  POSS-BY fat Sally[her]

14. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200514 I

15. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200515 Conceptual Cases Dependents that are required by an ACT are called Conceptual Cases: There are four main conceptual cases: –Objective Case (O) –Recipient Case (R) –Instrumental Case (I) –Directive Case Relation (D)

16. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200516 Conceptual Cases –Objective Case (O): "John took the book" o PP [John] PP [book] o ACT [took]

17. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200517 Conceptual Cases –Recipient Case (R): "John took the book from Mary" PP [John] PP [book] PP [Mary] o R ACT [took]

18. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200518 Conceptual Cases –Instrumental Case (I): "John ate the ice cream with a spoon" I PP [John] ACT [eat] PP [ice cream] PP [spoon] o o ACT [do]

19. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200519 Conceptual Cases –Directive Case Relation (D) "John drove his car to London from Guildford" PP [John]ACT [do] ACT [drove]PP [car] PP [London] PP [Guildford] D POSS-BY PP [John]

20. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200520 Prepositional Dependency Consider the following sentences: Possession e.g. "This is Sally’s cat":Cat  POSS-BY Sally Location e.g. "Sally is in London":London  LOC Sally Containment e.g. "The glass contains water":Water  CONT Glass

21. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200521 Primitive ACTs Primitive ActElaboration ATRANSTransfer of an abstract relationship such as possession ownership or control (give) PTRANSTransfer of the physical location of an object (go) PROPELApplication of a physical force to an object (push) MOVEMovement of a body part of an animal by that animal (kick) GRASPGrasping of an object by an actor (grasp) INGESTTaking in of an object by an animal to the inside of that animal (eat) EXPELExpulsion of an object from the object of an animal into the physical world (cry) MTRANSTransfer of mental information between animals or within an animal (tell) MBUILDConstruction by an animal of new information of old information (decide) CONCConceptualise or think about an idea (think) SPEAKActions of producing sounds (say) ATTENDAction of attending or focusing a sense organ towards a stimulus (listen)

22. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200522 Primitive ACTs e.g. I gave a book to Sally PP [I] PP [Sally] PP [book] PP [I] o R ACT [gave] I Sally book I o R ATRANS

23. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200523 Conceptual Tenses Any conceptualisation can be modified as a whole by a conceptual tense. John took the book (John  took) can be denoted by looking at the lemma take (from which the past tense took was derived): John ATRANS p

24. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200524 Conceptual Tenses SymbolElaboration p Past f Future t Transition tsts Start Transition tftf Finished Transition k Continuing ? Interrogative / Negative nil Present delta Timeless c Conditional "John will be taking the book": or "John is taking the book": or John taking John ATRANS k John taking John ATRANS f

25. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200525 Summarising CD Building Blocks E.g. I took a book from Sally Primitive conceptualizations (conceptual categories): –Objects (Picture Producers: PP): Sally, I, book Conceptual dependencies (diagrammatic conventions): –Arrows indicate the direction of dependency –Double arrow indicates two way link between actor and action Conceptual cases: –"O" indicates object case relation –"R" indicates recipient case relation Primitive acts: –ATRANS indicates transfer (of possession) Conceptual tenses: –"p" indicates that the action was performed in the past I I book Sally o R ATRANS p

26. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200526 Semantic Nets Vs CD Semantic Nets only provide a structure into which nodes representing information can be placed. Conceptual Dependency representation, on the other hand, provides both a structure and a specific set of primitives out of which representations of particular pieces of information can be constructed.

27. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200527 Advantages of CD The organisation of knowledge in terms of the primitives (or 'primitive acts') leads to a fewer inference rules. Many inferences are already contained in the representation itself. The initial structure that is built to represent the information contained in one sentence will have holes in it that have to be filled in: –holes which will serve as attention focusers for subsequent sentences.

28. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200528 Disadvantages of CD CD requires all knowledge to be broken down into 12 primitives: sometimes inefficient and sometimes impossible. CD is essentially a theory of the representation of events: though it is possible to have an event-centred view of knowledge but not a practical proposition for storing and retrieving knowledge. May be difficult or impossible to design a program that will reduce sentences to canonical form. (Probably not possible for monoids, which are simpler than natural language). Computationally expensive to reduce all sentences to the 12 primitives.

29. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200529 Exercises Please create the conceptual dependency representation of the following sentences: –John ran –John is a Doctor –John’s Dog –John pushed the cart –Bill shot Bob –John ate the egg –John prevented Mary from giving a book to Bill

30. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200530 Solution 1 "John ran" (Schank and Colby 1973) JohnPTRANS p

31. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200531 Solution 2 "John is a doctor" (Schank and Colby 1973) Johndoctor

32. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200532 Solution 3 "John’s Dog" (Schank and Colby 1973) dog John POSS-BY

33. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200533 Solution 4 "John pushed the cart" (Schank and Colby 1973) o John cart o PROPEL p

34. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200534 Solution 5 "Bill shot Bob" (Schank and Colby 1973) o Rob gun R Bill bullet o PROPEL p p Bob health(-10)

35. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200535 Solution 6 "John ate the egg" (Schank and Rieger 1974). o INSIDE MOUTH John D egg o INGEST p

36. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200536 Solution 7 "John prevented Mary from giving a book to Bill" (Schank and Rieger 1974). Mary p JohnDO Bill book Mary o R c/ p ATRANS

37. Artificial Intelligence – CS364 Knowledge Representation 20 th September 2005Bogdan L. Vrusias © 200537 Closing Questions??? Remarks??? Comments!!! Evaluation!