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Meaning-Based Knowledge Representations. 2004 년 4 월 13 일 홍 승 권. 강의 목표. 강의목표 인간의 지식표현방식 중에 Meaning-Based Knowledge Representation 에 대한 지식의 습득 . MBKR 의 두 가지 표현 방식 Propositional structure : Encode the significant information about a particular event
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Meaning-Based Knowledge Representations 2004년 4월 13일 홍 승 권
강의 목표 • 강의목표 • 인간의 지식표현방식 중에 Meaning-Based Knowledge Representation에 대한 지식의 습득. • MBKR의 두 가지 표현 방식 • Propositional structure : Encode the significant information about a particular event • Schemas : Represent categories of events and objects in terms of their typical properties
목차 • Memory for Meaning Interpretations of Events • Memory of Verbal Information • Memory of Visual Information • Retention of Detail versus Meaning • Implications of Good Memory for Meaning • Propositional Representations • Propositional Networks • Conceptual Knowledge • Semamtic Networks • Schemas • Psychological Reality of Schemas • Degree of Category Membership • Event Concepts • Abstraction versus Instance Theories • Learning Schemas in a Neural Network • Categories in the Brain
1. Memory for Meaning Interpretations of Events Memory of Verbal Information Memory of Visual Information Retention of Detail versus Meaning Implications of Good Memory for Meaning
Memory for Verbal/Visual Information • Verbal Information • After processing a linguistic message, people normally remember just its meaning and not its exact wording • Wanner’s experiment (1968): Fig. 5.1 • 실험조건: 문장을 기억하도록 한 경우와 그렇지 않은 경우 • Meaning이 같은 문장과 Style만 같은 문장을 제시하고 구분토록 함
Memory for Verbal/Visual Information • Visual Information • When see a picture, they tend to remember a meaningful interpretation of the picture • Mandler and Ritchey’s experiment (1977): Fig. 5.2 • 실험조건: Classroom을 표현하는 그림에서, 옷 색상의 변화와 지도의 변화 • 지도가 바뀌었을 때, 더 민감하게 반응(meaning significant change)
Retention of Detail versus Meaning • Memory for detail is available initially but is forgotten rapidly, whereas memory of meaning is retained. • Memory for orientation of a picture is one of the visual details that appears to decay rapidly (Gernsbacher (1985), Fig. 5.4) • Anderson (1974) • Question: The missionary shot the painter • Choices: 1. The missionary shot the painter 2. The painter was shot by the missionary 3. The painter shot the missionary 4. The missionary was shot by the painter (56%) • 1 과 3에 대해 : 잠시 후 98%, 한참 후 (96%) • 1 과 2에 대해 : 잠시 후 99%, 한참 후 (56%)
Implications of good memory for meaning • It is easier to remember more meaning material, if it is converted into more meaningful material • 기억술(Mnemonic technique)에 활용 • Shopping list, names for faces, telephone numbers, vocabulary items in a foreign language. • DAX-GIB : Like dad, the first part of gibberish
2. Propositional Representations Propositional Networks
Concept of a Proposition • Borrowed from logic and linguistics • A proposition is the smallest unit of knowledge that can stand as a separate assertion • That is, the smallest unit about which it makes sense to make the judgment true or false
Proposition의 예 • “Lincoln, who was president of the USA during a bitter war, freed the slaves”를 간단한 문장으로 바꾸면, A. Lincoln was president of the USA during war B. The war was bitter C. Lincoln freed the slaves. • Kintsch(1974) • As a list containing a relation followed by an ordered list of arguments • A’ (president-of, Lincoln, USA, War) • B’ (Bitter, war) • C’ (Free, Lincoln, slaves) • president-of takes 3, free takes 2, and bitter takes 1
Propositional Networks • Propositional information can be represented in networks that display the relations among concepts • Each proposition: an ellipse which is connected by labeled arrows to its relation and arguments • Fig. 5.5 • In the case of propositional representation, the abstraction involved deletion of many of the perceptual details and retention of the important relationships among the elements
3. Conceptual Knowledge Semamtic Networks Schemas Psychological Reality of Schemas Degree of Category Membership Event Concepts Abstraction versus Instance Theories Learning Schemas in a Neural Network Categories in the Brain
Conceptual Knowledge • Because of the ability to predict, categories gives us great economy in representation and communication • For Instance, • We can tell someone “I was licked by a dog”, and the person can predict the number of legs on the creature, approximate size and so on. • Semantic network and Schemas
Semantic Network • When a property is not stored directly with a concept, people can retrieve it from a higher-order concept • Our categorical knowledge were structured like Fig. 5.8 • Both the strength of the connections between facts and concepts and the distance between them in the semantic network have effects on retrieval time
Schema • Semantic networks which just store properties with concepts, cannot capture the approximate nature of our knowledge about a house, such as its typical size or shape • Schemas represent categorical knowledge according to a slot structure, where slots specify values that members of a category have on various attribute • House • Isa: Building • Parts: rooms • Materials: wood, brick, stone • Function: human dwelling • Shape: retilinear, triangular • Size: 100-100,000 square feet
Psychological reality of schemas • People will infer that an object has the default values for its category, unless they explicitly notice otherwise • Brewer and Treyens (1981)’s experiment • office내에 있는 물건을 기억하기: 일반적으로 office에 있는 물건은 잘 기억하였고, 실제 없었던 책을 있다고 하기도 했음.
Degree of category membership • Different instances are judged to be member of a category to different degrees, and more central members of a category have an advantage in processing • Cup To Bowl (Fig. 5.10 and 5.11) • the percentage of cup response gradually decreased with increasing width, but there is no clear-cut point where subjects stopped using cup • Classification behavior varies not only with the properties of an object but also with the context in which the object is imagined or presented
Event concepts(Script) • Script : Versions of event schemas. • Script of going to a restaurant (Bower, Black and Turner, 1979) • The stereotypic sequence was ‘sit down’, ‘look at menu’, ‘order’, ‘eat’, ‘pay bill’, and ‘leave’. • New events are encoded with respect to these general schemas. • Script are event schemas that people use to reason about prototypical events.
Abstraction versus Instance Theories • Abstraction Theory : Schemas • Instance Theory : Semantic network The End