Artificial Intelligence CS 364 Knowledge Representation Lectures on

Artificial Intelligence – CS 364 Knowledge Representation Lectures on Artificial Intelligence – CS 364 Conceptual Dependency 20 th September 2005 Dr Bogdan L. Vrusias b. vrusias@surrey. ac. uk 20 th September 2005 Bogdan L. Vrusias © 2005

Artificial Intelligence – CS 364 Knowledge Representation Contents • • Definition of Conceptual Dependency Grammar Building blocks Advantages and disadvantages Exercises 20 th September 2005 Bogdan L. Vrusias © 2005 2

Artificial Intelligence – CS 364 Knowledge Representation 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. 20 th September 2005 Bogdan L. Vrusias © 2005 3

Artificial Intelligence – CS 364 Knowledge Representation 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. 20 th September 2005 Bogdan L. Vrusias © 2005 4

Artificial Intelligence – CS 364 Knowledge Representation 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". 20 th September 2005 Bogdan L. Vrusias © 2005 5

Artificial Intelligence – CS 364 Knowledge Representation 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. ) 20 th September 2005 Bogdan L. Vrusias © 2005 6

Artificial Intelligence – CS 364 Knowledge Representation 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 20 th September 2005 Bogdan L. Vrusias © 2005 7

Artificial Intelligence – CS 364 Knowledge Representation 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) 20 th September 2005 Bogdan L. Vrusias © 2005 8

Artificial Intelligence – CS 364 Knowledge Representation 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) 20 th September 2005 Bogdan L. Vrusias © 2005 9

Artificial Intelligence – CS 364 Knowledge Representation 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 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. 20 th September 2005 Bogdan L. Vrusias © 2005 10

Artificial Intelligence – CS 364 Knowledge Representation Conceptual Dependencies • For a conceptualisation to exist, there must be at least two governors: – E. g. Sally stroked her fat cat PP: ACT: PA: Sally, cat, her [Sally] stroke fat Governors: Dependent: Sally, stroke, cat PP (cat) on ACT (stroke) PA (fat) on PP (cat) on PP (her[Sally]) 20 th September 2005 Bogdan L. Vrusias © 2005 11

Artificial Intelligence – CS 364 Knowledge Representation 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 20 th September 2005 cat. Bogdan L. Vrusias © 2005 12

Artificial Intelligence – CS 364 Knowledge Representation 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 fat 20 th September 2005 cat POSS-BY Sally[her] Bogdan L. Vrusias © 2005 13

Artificial Intelligence – CS 364 Knowledge Representation I 20 th September 2005 Bogdan L. Vrusias © 2005 14

Artificial Intelligence – CS 364 Knowledge Representation 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) 20 th September 2005 Bogdan L. Vrusias © 2005 15

Artificial Intelligence – CS 364 Knowledge Representation Conceptual Cases – Objective Case (O): "John took the book" PP [John] 20 th September 2005 ACT [took] o Bogdan L. Vrusias © 2005 PP [book] 16

Artificial Intelligence – CS 364 Knowledge Representation Conceptual Cases – Recipient Case (R): "John took the book from Mary" PP [John] ACT [took] R o PP [book] PP [John] PP [Mary] 20 th September 2005 Bogdan L. Vrusias © 2005 17

Artificial Intelligence – CS 364 Knowledge Representation Conceptual Cases – Instrumental Case (I): "John ate the ice cream with a spoon" PP [John] ACT [eat] PP [John] I ACT [do] o o PP [ice cream] PP [spoon] 20 th September 2005 Bogdan L. Vrusias © 2005 18

Artificial Intelligence – CS 364 Knowledge Representation Conceptual Cases – Directive Case Relation (D) "John drove his car to London from Guildford" PP [John] PP [car] ACT [do] ACT [drove] D PP [London] PP [Guildford] POSS-BY PP [John] 20 th September 2005 Bogdan L. Vrusias © 2005 19

Artificial Intelligence – CS 364 Knowledge Representation 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": 20 th September 2005 Water CONT Glass Bogdan L. Vrusias © 2005 20

Artificial Intelligence – CS 364 Knowledge Representation Primitive ACTs Primitive Act Elaboration ATRANS Transfer of an abstract relationship such as possession ownership or control (give) PTRANS Transfer of the physical location of an object (go) PROPEL Application of a physical force to an object (push) MOVE Movement of a body part of an animal by that animal (kick) GRASP Grasping of an object by an actor (grasp) INGEST Taking in of an object by an animal to the inside of that animal (eat) EXPEL Expulsion of an object from the object of an animal into the physical world (cry) MTRANS Transfer of mental information between animals or within an animal (tell) MBUILD Construction by an animal of new information of old information (decide) CONC Conceptualise or think about an idea (think) SPEAK Actions of producing sounds (say) ATTEND Action of attending or focusing a sense organ towards a stimulus (listen) 20 th September 2005 Bogdan L. Vrusias © 2005 21

Artificial Intelligence – CS 364 Knowledge Representation Primitive ACTs e. g. I gave a book to Sally PP [I] ACT [gave] R PP [Sally] PP [I] o PP [book] I ATRANS R o Sally I book 20 th September 2005 Bogdan L. Vrusias © 2005 22

Artificial Intelligence – CS 364 Knowledge Representation 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): p John 20 th September 2005 ATRANS Bogdan L. Vrusias © 2005 23

Artificial Intelligence – CS 364 Knowledge Representation Conceptual Tenses Symbol Elaboration "John will be taking the book": p Past f Future t Transition ts Start Transition tf Finished Transition k Continuing ? Interrogative / Negative nil Present delta Timeless c Conditional 20 th September 2005 taking John or f ATRANS John "John is taking the book": taking John or k John Bogdan L. Vrusias © 2005 ATRANS 24

Artificial Intelligence – CS 364 Knowledge Representation Summarising CD Building Blocks E. g. I took a book from Sally I p ATRANS o R I Sally book • 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 20 th September 2005 Bogdan L. Vrusias © 2005 25

Artificial Intelligence – CS 364 Knowledge Representation 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. 20 th September 2005 Bogdan L. Vrusias © 2005 26

Artificial Intelligence – CS 364 Knowledge Representation 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. 20 th September 2005 Bogdan L. Vrusias © 2005 27

Artificial Intelligence – CS 364 Knowledge Representation 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. 20 th September 2005 Bogdan L. Vrusias © 2005 28

Artificial Intelligence – CS 364 Knowledge Representation 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 20 th September 2005 Bogdan L. Vrusias © 2005 29

Artificial Intelligence – CS 364 Knowledge Representation Solution 1 • "John ran" (Schank and Colby 1973) p John 20 th September 2005 PTRANS Bogdan L. Vrusias © 2005 30

Artificial Intelligence – CS 364 Knowledge Representation Solution 2 • "John is a doctor" (Schank and Colby 1973) John 20 th September 2005 doctor Bogdan L. Vrusias © 2005 31

Artificial Intelligence – CS 364 Knowledge Representation Solution 3 • "John’s Dog" (Schank and Colby 1973) dog POSS-BY John 20 th September 2005 Bogdan L. Vrusias © 2005 32

Artificial Intelligence – CS 364 Knowledge Representation Solution 4 • "John pushed the cart" (Schank and Colby 1973) p John 20 th September 2005 PROPEL o cart Bogdan L. Vrusias © 2005 33

Artificial Intelligence – CS 364 Knowledge Representation Solution 5 • "Bill shot Bob" (Schank and Colby 1973) p Bill PROPEL o bullet health(-10) R Rob gun Bob p 20 th September 2005 Bogdan L. Vrusias © 2005 34

Artificial Intelligence – CS 364 Knowledge Representation Solution 6 • "John ate the egg" (Schank and Rieger 1974). p John o INGEST D 20 th September 2005 egg INSIDE John MOUTH John Bogdan L. Vrusias © 2005 35

Artificial Intelligence – CS 364 Knowledge Representation Solution 7 • "John prevented Mary from giving a book to Bill" (Schank and Rieger 1974). p John DO c/ Mary ATRANS o book p R Bill Mary 20 th September 2005 Bogdan L. Vrusias © 2005 36

Artificial Intelligence – CS 364 Knowledge Representation Closing • • Questions? ? ? Remarks? ? ? Comments!!! Evaluation! 20 th September 2005 Bogdan L. Vrusias © 2005 37