2002.09.05 - SLIDE 1IS 202 - Fall 2002

Lecture 04: Knowledge Representation

Prof. Ray Larson & Prof. Marc Davis

UC Berkeley SIMS

Tuesday and Thursday 10:30 am - 12:00 am

Fall 2002

SIMS 202:

Information Organization

and Retrieval

Credits to Warren Sack for some of the slides in this lecture

2002.09.05 - SLIDE 2IS 202 - Fall 2002

Today

• Review of Categorization

• From Cognitive Science to AI

• The Vocabulary Problem

• Artificial Intelligence, Knowledge Representation,and Commonsense

• Photo Project Assignment 2 Check-In

2002.09.05 - SLIDE 3IS 202 - Fall 2002

Categorization

• Processes of categorization are fundamental to human cognition

• Categorization is messier than our computer systems would like

• Human categorization is characterized by– Family resemblances– Prototypes– Basic-level categories

• Considering how human categorization functions is important in the design of information organization and retrieval systems

2002.09.05 - SLIDE 4IS 202 - Fall 2002

Categorization

• Classical categorization– Necessary and sufficient conditions for

membership– Generic-to-specific monohierarchical structure

• Modern categorization– Characteristic features (family resemblances)– Centrality/typicality (prototypes)– Basic-level categories

2002.09.05 - SLIDE 5IS 202 - Fall 2002

Properties of Categorization

• Family Resemblance– Members of a category may be related to one

another without all members having any property in common

• Prototypes– Some members of a category may be “better

examples” than others, i.e., “prototypical” members

2002.09.05 - SLIDE 6IS 202 - Fall 2002

Basic-Level Categorization

• Perception– Overall perceived shape– Single mental image– Fast identification

• Function– General motor program

• Communication– Shortest, most commonly used and contextually neutral words– First learned by children

• Knowledge Organization– Most attributes of category members stored at this level

2002.09.05 - SLIDE 7IS 202 - Fall 2002

Information Hierarchy

Wisdom

Knowledge

Information

Data

2002.09.05 - SLIDE 8IS 202 - Fall 2002

Information Hierarchy

Knowledge

Information

Wisdom

Data

2002.09.05 - SLIDE 9IS 202 - Fall 2002

Today’s Thinkers/Tinkerers

George Furnas

http://www.si.umich.edu/~furnas/

Marvin Minsky

http://web.media.mit.edu/~minsky/

Doug Lenat

http://www.cyc.com/staff.html

2002.09.05 - SLIDE 10IS 202 - Fall 2002

Psychology Methodology

Theorizing Experimenting

2002.09.05 - SLIDE 11IS 202 - Fall 2002

Computer Science Methodology

Theorizing

System Building

2002.09.05 - SLIDE 12IS 202 - Fall 2002

Cognitive Science Methodology

Theorizing Experimenting

System Building

2002.09.05 - SLIDE 13IS 202 - Fall 2002

What is Cognitive Science?

• A definition from Howard Gardner (1986) The Mind’s New Science; the five symptoms of cognitive science; the first two are central, the next three are strategic– (1) Mental representations– (2) Computers– (3) Emphasis– (4) Epistemology– (5) Interdisciplinarity

2002.09.05 - SLIDE 14IS 202 - Fall 2002

Symptom 1 of Cognitive Science: Mental Representations

• To study human cognition it is necessary to posit mental representations and examine those representations separately from the “low level” biological or neurological, on one hand, and also separately from the “high level” social or cultural, on the other hand.

(adapted from Gardner, 1986)

2002.09.05 - SLIDE 15IS 202 - Fall 2002

Symptom 2 of Cognitive Science: Computers

• Computers are central to any understanding of the human mind. They are essential both as tools, but also as models of how the mind works.

(adapted from Gardner, 1986)

2002.09.05 - SLIDE 16IS 202 - Fall 2002

Symptom 3 of Cognitive Science:Emphasis

• Cognitive scientists deliberately de-emphasize certain factors which may be important for cognitive functioning but whose inclusion would unnecessarily complicate the cognitive-scientific enterprise. These de-emphasized factors include emotional affect, historical, cultural, and other types of context (e.g., issues of embodiment and the senses).(adapted from Gardner, 1986)

2002.09.05 - SLIDE 17IS 202 - Fall 2002

Symptom 4 of Cognitive Science: Epistemology

• Cognitive science is concerned with an area that has historically been a part of philosophy, namely the domain of epistemology.

(adapted from Gardner, 1986)

2002.09.05 - SLIDE 18IS 202 - Fall 2002

Symptom 5 of Cognitive Science: Interdisciplinarity

• Cognitive science is an interdisciplinary enterprise.

(adapted from Gardner, 1986)

2002.09.05 - SLIDE 19IS 202 - Fall 2002

Disciplines of Cognitive Science

• Philosophy

• Psychology

• Artificial Intelligence

• Linguistics

• Anthropology

• Neuroscience

2002.09.05 - SLIDE 20IS 202 - Fall 2002

The Birth of Cognitive Science

• Symposium on Information Theory, MIT, 10-12 September 1956– Allen Newell & Herbert Simon, “Logic Theory

Machine”– Noam Chomsky, “Three Models of Language”– George Miller, “The Magical Number Seven”

2002.09.05 - SLIDE 21IS 202 - Fall 2002

The Birth of AI

• Rockefeller-sponsored Institute at Dartmouth College, Summer 1956– John McCarthy, Dartmouth (->MIT->Stanford)– Marvin Minsky, MIT (geometry)– Herbert Simon, CMU (logic)– Allen Newell, CMU (logic)– Arthur Samuel, IBM (checkers)– Alex Bernstein, IBM (chess)– Nathan Rochester, IBM (neural networks)– Etc.

2002.09.05 - SLIDE 22IS 202 - Fall 2002

Definition of AI

“... artificial intelligence [AI] is the science of making machines do things that would require intelligence if done by [humans]” (Minsky, 1963)

2002.09.05 - SLIDE 23IS 202 - Fall 2002

The Goals of AI Are Not New

• Ancient Greece– Daedalus’ automata

• Judaism’s myth of the Golem• 18th century automata

– Singing, dancing, playing chess?

• Mechanical metaphors for mind– Clock– Telegraph/telephone network– Computer

2002.09.05 - SLIDE 24IS 202 - Fall 2002

Some Areas of AI

• Knowledge Representation• Programming Languages• Natural Language Understanding• Speech Understanding• Vision• Robotics• Planning• Machine Learning• Expert Systems• Qualitative Simulation

2002.09.05 - SLIDE 25IS 202 - Fall 2002

Furnas: The Vocabulary Problem

• People use different words to describe the same things– “If one person assigns the name of an item,

other untutored people will fail to access it on 80 to 90 percent of their attempts.”

– “Simply stated, the data tell us there is no one good access term for most objects.”

2002.09.05 - SLIDE 26IS 202 - Fall 2002

The Vocabulary Problem

• How is it that we come to understand each other?– Shared context– Dialogue

• How can machines come to understand what we say?– Shared context?– Dialogue?

2002.09.05 - SLIDE 27IS 202 - Fall 2002

Vocabulary Problem Solutions?

• Furnas et al.– Make the user memorize precise system

meanings– Have the user and system interact to identify

the precise referent

• Minsky and Lenat– Give the system “commonsense” so it can

understand what the user’s words can mean

2002.09.05 - SLIDE 28IS 202 - Fall 2002

Lenat on the Vocabulary Problem

• “The important point is that users will be able to find information without having to be familiar with the precise way the information is stored, either through field names or by knowing which databases exist, and can be tapped.”

2002.09.05 - SLIDE 29IS 202 - Fall 2002

Minsky on the Vocabulary Problem

• “To make our computers easier to use, we must make them more sensitive to our needs. That is, make them understand what we mean when we try to tell them what we want. […] If we want our computers to understand us, we’ll need to equip them with adequate knowledge.”

2002.09.05 - SLIDE 30IS 202 - Fall 2002

Commonsense

• Commonsense is background knowledge that enables us to understand, act, and communicate

• Things that most children know

• Minsky on commonsense:– “Much of our commonsense knowledge

information has never been recorded at all because it has always seemed so obvious we never thought of describing it.”

2002.09.05 - SLIDE 31IS 202 - Fall 2002

Commonsense Example

• “I want to get inexpensive dog food.”

• The food is not made out of dogs.• The food is not for me to eat.• Dogs cannot buy their own food.• I am not asking to be given dog food.• I am not saying that I want to understand

why some dog food is inexpensive.• The dog food is not more than $5 per can.

2002.09.05 - SLIDE 32IS 202 - Fall 2002

Engineering Commonsense

• Use multiple ways to represent knowledge

• Acquire huge amounts of that knowledge

• Find commonsense ways to reason with it (“knowledge about how to think”)

2002.09.05 - SLIDE 33IS 202 - Fall 2002

CYC

• Decades long effort to build commonsense knowledge-base

• Storied past

• 100,000 basic concepts

• 1,000,000 assertions about the world

• The validity of Cyc’s assertions are context-dependent (default reasoning)

2002.09.05 - SLIDE 34IS 202 - Fall 2002

Cyc’s Top-Level Ontology

• Fundamentals • Top Level • Time and Dates • Types of Predicates • Spatial Relations • Quantities • Mathematics • Contexts • Groups • "Doing" • Transformations • Changes Of State • Transfer Of

Possession • Movement • Parts of Objects

• Professions

• Composition of Substances

• Agents • Organizations • Actors • Roles • Emotion • Propositional

Attitudes • Social • Biology • Chemistry • Physiology • General Medicine

http://www.cyc.com/cyc-2-1/toc.html

• Materials• Waves • Devices • Construction

• Financial • Food • Clothing • Weather • Geography • Transportation • Information • Perception • Agreements • Linguistic Terms • Documentation

2002.09.05 - SLIDE 35IS 202 - Fall 2002

OpenCYC

• Cyc’s knowledge-base is now coming online– http://www.opencyc.org/

• How could Cyc’s knowledge-base affect the design of information organization and retrieval systems?

2002.09.05 - SLIDE 36IS 202 - Fall 2002

Multiple Representations

• Minksy– “I think this is what brains do instead: Find several

ways to represent each problem and to represent the required knowledge. Then when one method fails to solve a problem, you can quickly switch to another description.”

• Furnas– “But regardless of the number of commands or

objects in a system and whatever the choice of their ‘official’ names, the designer must make many, many alternative verbal access routes to each.”

2002.09.05 - SLIDE 37IS 202 - Fall 2002

AI or IA?

• Artificial Intelligence (AI)– Make machines as smart as (or smarter than)

people

• Intelligence Amplification (IA)– Use machines to make people smarter

2002.09.05 - SLIDE 38IS 202 - Fall 2002

Assignment 0 Check-In

• Deliverables– Personal web page– Assignments page– Email address– Focus statement– Online Questionnaire

• Feedback– Spell-check and grammar-check– Simple vs. skeletal

2002.09.05 - SLIDE 39IS 202 - Fall 2002

Assignment 2 Check-In

• Deliverables– Persona description (brief)– Scenario description (brief)– Annotated user experience storyboard– Group web site– Work distribution table on your group web site– Photos for your application idea

• Feedback– Questions, comments, problems?

2002.09.05 - SLIDE 40IS 202 - Fall 2002

Homework (!)

• Read – Chapters 3 and 5 in The Organization of

Information (OI)

• Assignment 2: Photo Use Scenario– Due by Thursday, September 12

2002.09.05 - SLIDE 41IS 202 - Fall 2002

Next Time

• Metadata Introduction (RRL)