Lecture 04: Knowledge Representation

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Lecture 04 Knowledge Representation
SIMS 202 Information Organization and Retrieval

• Prof. Ray Larson Prof. Marc Davis
• UC Berkeley SIMS
• Tuesday and Thursday 1030 am - 1200 am
• Fall 2003

Credits to Warren Sack for some of the slides in
this lecture
2
Today

• Review of Categorization
• Knowledge Representation
• The Vocabulary Problem
• Commonsense
• Cyc
• Discussion Questions
• Phone Project Overview and Assignment 2
• Action Items for Next Time

3
Today

• Review of Categorization
• Knowledge Representation
• The Vocabulary Problem
• Commonsense
• Cyc
• Discussion Questions
• Phone Project Overview and Assignment 2
• Action Items for Next Time

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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

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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

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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

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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
• Tends to be in the middle of a classification
  hierarchy

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Today

• Review of Categorization
• Knowledge Representation
• The Vocabulary Problem
• Commonsense
• Cyc
• Discussion Questions
• Phone Project Overview and Assignment 2
• Action Items for Next Time

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Information Hierarchy
Wisdom
Knowledge
Information
Data
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Information Hierarchy
Wisdom
Knowledge
Information
Data
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Todays 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
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The Birth of AI

• Rockefeller-sponsored Institute at Dartmouth
  College, Summer 1956
• John McCarthy, Dartmouth (-gtMIT-gtStanford)
• 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.

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Definition of AI

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

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The Goals of AI Are Not New

• Ancient Greece
• Daedalus automata
• Judaisms myth of the Golem
• 18th century automata
• Singing, dancing, playing chess?
• Mechanical metaphors for mind
• Clock
• Telegraph/telephone network
• Computer

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Some Areas of AI

• Knowledge representation
• Programming languages
• Natural language understanding
• Speech understanding
• Vision
• Robotics
• Planning
• Machine learning
• Expert systems
• Qualitative simulation

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AI or IA?

• Artificial Intelligence (AI)
• Make machines as smart as (or smarter than)
  people
• Intelligence Amplification (IA)
• Use machines to make people smarter

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Today

• Review of Categorization
• Knowledge Representation
• The Vocabulary Problem
• Commonsense
• Cyc
• Discussion Questions
• Phone Project Overview and Assignment 2
• Action Items for Next Time

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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.

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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?

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Vocabulary Problem Solutions?

• Furnas et al.
• Make the user memorize precise system meanings
• Have the user and system interact to identify the
  precise referent
• Provide infinite aliases to objects
• Minsky and Lenat
• Give the system commonsense so it can
  understand what the users words can mean

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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.

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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, well need to equip
  them with adequate knowledge.

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Today

• Review of Categorization
• Knowledge Representation
• The Vocabulary Problem
• Commonsense
• Cyc
• Discussion Questions
• Phone Project Overview and Assignment 2
• Action Items for Next Time

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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.

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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.

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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)

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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.

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Today

• Review of Categorization
• Knowledge Representation
• The Vocabulary Problem
• Commonsense
• Cyc
• Discussion Questions
• Phone Project Overview and Assignment 2
• Action Items for Next Time

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CYC

• Decades long effort to build a commonsense
  knowledge-base
• Storied past
• 100,000 basic concepts
• 1,000,000 assertions about the world
• The validity of Cycs assertions are
  context-dependent (default reasoning)

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Cyc Examples

• Cyc can find the match between a user's query for
  "pictures of strong, adventurous people" and an
  image whose caption reads simply "a man climbing
  a cliff"
• Cyc can notice if an annual salary and an hourly
  salary are inadvertently being added together in
  a spreadsheet
• Cyc can combine information from multiple
  databases to guess which physicians in practice
  together had been classmates in medical school
• When someone searches for "Bolivia" on the Web,
  Cyc knows not to offer a follow-up question like
  "Where can I get free Bolivia online?"

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Cyc Applications

• Applications currently available or in
  development
• Integration of Heterogeneous Databases
• Knowledge-Enhanced Retrieval of Captioned
  Information
• Guided Integration of Structured Terminology
  (GIST)
• Distributed AI
• WWW Information Retrieval
• Potential applications
• Online brokering of goods and services
• "Smart" interfaces
• Intelligent character simulation for games
• Enhanced virtual reality
• Improved machine translation
• Improved speech recognition
• Sophisticated user modeling
• Semantic data mining

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Cycs 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

• Composition of Substances
• Agents
• Organizations
• Actors
• Roles
• Professions
• Emotion
• Propositional Attitudes
• Social
• Biology
• Chemistry
• Physiology
• General Medicine

• Materials
• Waves
• Devices
• Construction
• Financial
• Food
• Clothing
• Weather
• Geography
• Transportation
• Information
• Perception
• Agreements
• Linguistic Terms
• Documentation

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

• Cycs knowledge-base is now coming online
• http//www.opencyc.org/
• How could Cycs knowledge-base affect the design
  of information organization and retrieval
  systems?

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Today

• Review of Categorization
• Knowledge Representation
• The Vocabulary Problem
• Commonsense
• Cyc
• Discussion Questions
• Phone Project Overview and Assignment 2
• Action Items for Next Time

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Discussion Questions (Furnas)

• Alison Billings Vijay Viswanathan on Furnas
• Are unlimited alias indexes an effective design
  solution to the problem of precision in "term
  based" searches? Is it possible to implement
  such a system that could maintain an accurate
  relation (category) to the designers armchair
  term with the existence of polysemy? Would the
  adaptive nature of this solution propagate an all
  inclusive alias category which could include all
  accessible information in a particular index?

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Discussion Questions (Furnas)

• Alison Billings Vijay Viswanathan on Furnas
• Since the publishing of this article in 1987 the
  technological advances in information retrieval
  in the past 16 years have been profound. Is the
  Vocabulary-Problem still a major issue in
  Human-System Communication? Furnas, et al.,
  provide some solutions to the Vocabulary Problem
  such as unlimited aliasing, keyword
  harvesting, and adaptive indices. But now
  there are WYSIWYG interfaces such as Windows that
  may reduce the need for command line word
  choices, search engines that harvest the content
  from web pages, or services like Google that put
  out Did you mean xxxxx? when search results are
  sparse. Has the Vocabulary Problem been solved?

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Discussion Questions (Minsky)

• Joseph Hall on Minsky
• Minsky talks a lot about commonsense. How would
  you define what is within the commonsense? Do
  you think that commonsense would be easy or
  difficult to teach to a computer? Why? Is
  commonsense a cross-cultural, basic-level
  category in the sense of what Lakoff described?
  Or is it more culturally specific (like "Don't
  step in front of moving traffic.") and thus
  harder to define? How would culturally-dependent
  definitions of "commonsense" complicate Minsky's
  theory?
• Are machines that learn such a good thing? For
  example, I would like my computer to learn
  certain things (like how to fix common errors)
  but not others (like how to play the stock market
  with my bank account). Are ethics (cyber and
  otherwise) to be programmed into learning
  computers?

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Discussion Questions (Minsky)

• Joseph Hall on Minsky
• What Minsky describes is all fine and dandy...
  but there seems to be a rather large gap between
  the machines of today and the machines he is
  postulating. To learn, machines would not only
  have to be able to note (and take action) when
  they are deviating from "operational parameter
  space" (malfunctioning, blue screen of death,
  etc.) but be able to decide on and implement a
  solution to the problem at hand from a different
  direction and/or using a different technique,
  quickly.

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Discussion Questions (Minsky)

• Joseph Hall on Minsky
• Do you think that building such a
  commonsense-aware machine is possible today?
  (That is, is Minsky's model of a
  commonsense-based machine a reasonable goal or
  just an ideal?) If not, what are some of the
  impediments to the realization of one of Minsky's
  machines?
• Do user expectations (reasonable or not) of what
  a computer should be doing factor into this at
  all?

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Discussion Questions (Lenat)

• Rebecca Shapley on Lenat
• What does this article imply for best-practices
  in information organization retrieval? How
  would you articulate the potential for a
  commonsense knowledgebase to revolutionize
  information retrieval? Does the premise of a
  commonsense-base feeding efforts at machine
  learning or natural language understanding make
  sense to you? Which potential applications Lenat
  mentions are compelling to you?
• This article is from 1995 - do we hear anything
  more about this CYC? Did it revolutionize things?
  Why does Minsky call for a huge commonsense
  knowledgebase in 2000 when CYC was nearly
  complete in 1995?

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Discussion Questions (Lenat)

• Rebecca Shapley on Lenat
• How would you apply the conduit metaphor
  toolmaker's paradigms to describe, or perhaps
  critique, the CYC project?
• If CYC is 'automating the whitespace in
  documents' - capturing the context for
  information, how would you describe the context
  it is capturing? How would you describe where the
  captured context is no longer applicable? How do
  you feel about the notion that 10 people in Palo
  Alto CA were able to describe your context? Do
  you trust them with that task? Do you consider it
  necessary that some shared automated context be
  created? What challenges do you see for their
  ostensible goal, or limitations do you see to
  their approach?

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Discussion Questions (Lenat)

• Rebecca Shapley on Lenat
• Anything in particular you can imagine yourself
  unwilling to have represented a particular way in
  the commonsensebase? Let's say you believe in
  reincarnation but the assertions in the
  commonsensebase don't leave any room for this
  idea, and how to interpret what you might say to
  a bereaved friend. How do you feel about the
  ability to 'automatically' interpret your
  expression being left out? Does it make you feel
  invisible, relieved, angry? What would be
  necessary to have it be culturally sensitive, and
  would that be encodable?

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Discussion Questions (Lenat)

• Rebecca Shapley on Lenat
• What can you piece together about how CYC is
  implemented, how it makes decisions? What
  questions do you still have about how it works?
• Do you think the tone of the article was
  influenced by the fact that Lenat was writing as
  President of Cycorp?
• So, can this common-sense-base 'think'? Is it
  intelligent? Why and why not?

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Today

• Review of Categorization
• Knowledge Representation
• The Vocabulary Problem
• Commonsense
• Cyc
• Discussion Questions
• Phone Project Overview and Assignment 2
• Action Items for Next Time

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Assignment 0 Check-In

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

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Phone Project Overview

• In this project we will be creating, sharing, and
  reusing mobile media and metadata
• You and your Project Group will design
  application use scenarios and develop and refine
  metadata frameworks for your photos
• Some of you may even choose to develop retrieval
  applications for the photo database in the second
  half of the course
• We will be using the Nokia 3650 mobile media
  phone and software developed by Garage Cinema
  Research

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Phone Project Overview

• In the SIMS 202 Phone Project you and your
  Project Group will
• Experience the actual process of information
  organization and retrieval (especially as regards
  metadata creation and use)
• Work in small, focused teams performing a variety
  of tasks in image acquisition, description, and
  application design
• Develop an ongoing resource for SIMS (an
  annotated photo database) that can be used for
  internal research and teaching, as well as for
  external promotional and informational purposes

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Phone Project Requirements

• Create engaging and useful application scenarios
  and photos for use by your team and the entire
  class
• The photos you take and the applications you will
  design to use them should be interesting and
  useful to you and your colleagues
• Create a shared, reusable resource of annotated
  photos
• Design your metadata such that all photos are
  accessible not only for the needs of your
  particular application, but also for the
  reusability of your photos and metadata by other
  applications

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Phone Project Assignments

• Photo Use Scenario Application Idea (Assignment
  2)
• You will brainstorm and storyboard an application
  for a mobile media device that accesses a server
  and facilitates the creation, sharing, and reuse
  of media and metadata. You will develop user
  personas and scenarios of how the application
  works and how the user experiences it.
• Photo Capture and Annotation (Assignment 3)
• With the goals of your application and the
  overall goals of the class project in mind, each
  group member is required to take at least 5
  pictures relevant to the scenario you specified
  in the prior assignment. You will also get
  hands-on experience in annotating photos using
  the Mobile Media Metadata (MMM) framework, an
  application available on the mobile phones. You
  will also identify strengths and weaknesses of
  MMM framework.

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Phone Project Assignments

• Photo Metadata Design (Assignment 4)
• Having your application and the overall project
  goals in mind, you will design a suitable
  metadata framework to annotate the photos in the
  collection. You will also annotate more photos
  using your metadata framework.

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Phone Project Assignments

• Project Presentations (Assignment 6)
• In a special class session, your group will
  present your application ideas, metadata
  frameworks, and annotated photos to your fellow
  students using the Flamenco browser. Each group
  will have about 10 minutes to present their
  innovative work.
• Metadata Consolidation (Assignment 8)
• You will consolidate your classification scheme
  with those belonging to other groups. The entire
  class will collaborate to create one overall
  metadata framework which will be used to for
  Phase II of the project.

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Phone Project Assignments

• Phone Project Phase II Application Selection
  (Assignment 10)
• The entire class will decide on an application to
  implement from among the application ideas
  presented by the various project groups as well
  as from among any ideas you or your Project group
  have come up with.
• Phone Project Phase II Specification Design
  (Assignment 13)
• A group of class volunteers will draft
  specifications and designs for the application
  selected in the previous assignment.
• Phone Project Phase II Implementation Testing
  (Assignment 14)
• A group of class volunteers will implement and
  test the application selected in the previous
  assignment.

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Assignment 2 Process

• Brainstorm application ideas
• Evaluate your ideas and agree on one to pursue
• Come up with a persona and scenario for your
  application idea
• Write a description of your application idea
  involving one persona and one scenario
• Draw a storyboard with explanatory text
• Document the results of your brainstorming
• Create your group website

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Assignment 2 Deliverables

• Brief description of the application idea you
  selected
• Persona description
• Scenario description
• Annotated storyboard
• Work distribution table
• List all brainstorming ideas and reasons for
  selecting or rejecting each

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Assignment 2 Turning It In

• Submit an email to is202-ta_at_sims.berkeley.edu
  with the following information (due September 16,
  before class)
• Group name
• URL of your group website
• URL to description (application, persona,
  scenario), storyboard, brainstorming results,
  work distribution table
• Time it took you to complete the assignment
• Any comments on assignment (optional)

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Today

• Review of Categorization
• Knowledge Representation
• The Vocabulary Problem
• Commonsense
• Cyc
• Discussion Questions
• Phone Project Overview and Assignment 2
• Action Items for Next Time

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Homework (!)

• Read
• Word Association Norms, Mutual Information, and
  Lexicography (Church, Kenneth and Hanks, Patrick)
• Wordnet An Electronic Lexical Database --
  Introduction Ch. 1 (C. Fellbaum, G.A. Miller)
  (handout)
• Assignment 2 Photo Use Scenario
• Due by Tuesday, September 16

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Next Time

• Lexical Relations and WordNet (RRL)