2004.10.21 - slide 1is 202 - fall 2004 lecture 16: knowledge representation prof. ray larson &...
Post on 19-Dec-2015
216 views
TRANSCRIPT
![Page 1: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/1.jpg)
2004.10.21 - SLIDE 1IS 202 - FALL 2004
Lecture 16: Knowledge Representation
Prof. Ray Larson & Prof. Marc Davis
UC Berkeley SIMS
Tuesday and Thursday 10:30 am - 12:00 pm
Fall 2004
SIMS 202:
Information Organization
and Retrieval
Credits to Marti Hearst and Warren Sack for some of the slides in this lecture
![Page 2: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/2.jpg)
2004.10.21 - SLIDE 2IS 202 - FALL 2004
Agenda
• Review of Last Time
• Knowledge Representation
– The Vocabulary Problem
– Commonsense
– CYC
• Discussion Questions
• Action Items for Next Time
![Page 3: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/3.jpg)
2004.10.21 - SLIDE 3IS 202 - FALL 2004
Agenda
• Review of Last Time
• Knowledge Representation
– The Vocabulary Problem
– Commonsense
– CYC
• Discussion Questions
• Action Items for Next Time
![Page 4: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/4.jpg)
2004.10.21 - SLIDE 4IS 202 - FALL 2004
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
![Page 5: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/5.jpg)
2004.10.21 - SLIDE 5IS 202 - FALL 2004
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
![Page 6: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/6.jpg)
2004.10.21 - SLIDE 6IS 202 - FALL 2004
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
![Page 7: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/7.jpg)
2004.10.21 - SLIDE 7IS 202 - FALL 2004
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
![Page 8: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/8.jpg)
2004.10.21 - SLIDE 8IS 202 - FALL 2004
Agenda
• Review of Last Time
• Knowledge Representation
– The Vocabulary Problem
– Commonsense
– CYC
• Discussion Questions
• Action Items for Next Time
![Page 9: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/9.jpg)
2004.10.21 - SLIDE 9IS 202 - FALL 2004
Information Hierarchy
Wisdom
Knowledge
Information
Data
![Page 10: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/10.jpg)
2004.10.21 - SLIDE 10IS 202 - FALL 2004
Information Hierarchy
Knowledge
Information
Wisdom
Data
![Page 11: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/11.jpg)
2004.10.21 - SLIDE 11IS 202 - FALL 2004
Today’s Thinkers/Tinkerers
George Furnashttp://www.si.umich.edu/~furnas/
Marvin Minskyhttp://web.media.mit.edu/~minsky/
Doug Lenathttp://www.cyc.com/staff.html
![Page 12: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/12.jpg)
2004.10.21 - SLIDE 12IS 202 - FALL 2004
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.
![Page 13: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/13.jpg)
2004.10.21 - SLIDE 13IS 202 - FALL 2004
Definition of AI
“... artificial intelligence [AI] is the science of making machines do things that would require intelligence if done by [humans]” (Minsky, 1963)
![Page 14: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/14.jpg)
2004.10.21 - SLIDE 14IS 202 - FALL 2004
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
![Page 15: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/15.jpg)
2004.10.21 - SLIDE 15IS 202 - FALL 2004
Some Areas of AI
• Knowledge representation• Programming languages• Natural language understanding• Speech understanding• Vision• Robotics• Planning• Machine learning• Expert systems• Qualitative simulation
![Page 16: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/16.jpg)
2004.10.21 - SLIDE 16IS 202 - FALL 2004
AI or IA?
• Artificial Intelligence (AI)– Make machines as smart as (or smarter than)
people
• Intelligence Amplification (IA)– Use machines to make people smarter
![Page 17: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/17.jpg)
2004.10.21 - SLIDE 17IS 202 - FALL 2004
Agenda
• Review of Last Time
• Knowledge Representation
– The Vocabulary Problem
– Commonsense
– CYC
• Discussion Questions
• Action Items for Next Time
![Page 18: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/18.jpg)
2004.10.21 - SLIDE 18IS 202 - FALL 2004
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.”
![Page 19: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/19.jpg)
2004.10.21 - SLIDE 19IS 202 - FALL 2004
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?
![Page 20: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/20.jpg)
2004.10.21 - SLIDE 20IS 202 - FALL 2004
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 user’s words can mean
![Page 21: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/21.jpg)
2004.10.21 - SLIDE 21IS 202 - FALL 2004
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.”
![Page 22: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/22.jpg)
2004.10.21 - SLIDE 22IS 202 - FALL 2004
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.”
![Page 23: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/23.jpg)
2004.10.21 - SLIDE 23IS 202 - FALL 2004
Agenda
• Review of Last Time
• Knowledge Representation
– The Vocabulary Problem
– Commonsense
– CYC
• Discussion Questions
• Action Items for Next Time
![Page 24: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/24.jpg)
2004.10.21 - SLIDE 24IS 202 - FALL 2004
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.”
![Page 25: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/25.jpg)
2004.10.21 - SLIDE 25IS 202 - FALL 2004
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.
![Page 26: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/26.jpg)
2004.10.21 - SLIDE 26IS 202 - FALL 2004
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”)
![Page 27: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/27.jpg)
2004.10.21 - SLIDE 27IS 202 - FALL 2004
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.”
![Page 28: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/28.jpg)
2004.10.21 - SLIDE 28IS 202 - FALL 2004
Agenda
• Review of Last Time
• Knowledge Representation
– The Vocabulary Problem
– Commonsense
– CYC
• Discussion Questions
• Action Items for Next Time
![Page 29: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/29.jpg)
2004.10.21 - SLIDE 29IS 202 - FALL 2004
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 Cyc’s assertions are context-dependent (default reasoning)
![Page 30: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/30.jpg)
2004.10.21 - SLIDE 30IS 202 - FALL 2004
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?"
![Page 31: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/31.jpg)
2004.10.21 - SLIDE 31IS 202 - FALL 2004
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
![Page 32: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/32.jpg)
2004.10.21 - SLIDE 32IS 202 - FALL 2004
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
• Composition of Substances
• Agents • Organizations • Actors • Roles • Professions
• 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
![Page 33: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/33.jpg)
2004.10.21 - SLIDE 33IS 202 - FALL 2004
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?
![Page 34: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/34.jpg)
2004.10.21 - SLIDE 34IS 202 - FALL 2004
Web KR Resources
• OpenCYC– http://www.opencyc.org/
• OpenMind– http://commonsense.media.mit.edu
• beingmeta– http://www.beingmeta.com/technology.fdxml
• Semantic Web– http://www.w3.org/2001/sw/
![Page 35: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/35.jpg)
2004.10.21 - SLIDE 35IS 202 - FALL 2004
Agenda
• Review of Last Time
– The Vocabulary Problem
– Commonsense
– CYC
• Knowledge Representation
• Discussion Questions
• Action Items for Next Time
![Page 36: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/36.jpg)
2004.10.21 - SLIDE 36IS 202 - FALL 2004
Discussion Questions (Furnas)
• Steve Chan on Furnas– The Furnas results indicating the problems of
word selection would seem to be related to the motivations behind IR systems that support relevance feedback, as well as IR systems that support search term synonyms; namely, user's search terms may not clearly identify the desired objects. Of the two IR approaches, which one seems closer to the approach suggested by Furnas?
![Page 37: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/37.jpg)
2004.10.21 - SLIDE 37IS 202 - FALL 2004
Discussion Questions (Furnas)
• Steve Chan on Furnas– The Furnas experiments used only a small
number of target objects, but allowed a large number of aliases. We saw in classical IR systems that search methods that worked well on small collections, would often have problems on larger collections. Do you believe the aliasing would work well for larger collections of target objects? What kinds of applications might you want to use unlimited aliasing for, and how do they differ from the typical IR document retrieval system?
![Page 38: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/38.jpg)
2004.10.21 - SLIDE 38IS 202 - FALL 2004
Discussion Questions (Lenat)
• Rupa Patel on Lenat– Can common-sense databases like CYC help
solve Furnas's problem of vocabulary usage in systems design?
– How can common-sense knowledge bases lend insight into natural language ambiguities?
![Page 39: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/39.jpg)
2004.10.21 - SLIDE 39IS 202 - FALL 2004
Discussion Questions (Lenat)
• Rupa Patel on Lenat– In CYC, human “knowledge enterers” are
responsible for adding and editing atomic terms, assertions of reason, and contexts. The assertions can be related to one another, and each holds true only in certain contexts.
– Based on your understanding of CYC, which categorization effects are utilized in the construction of the contexts: prototype effects, classical categorization theory, polysemy.
![Page 40: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/40.jpg)
2004.10.21 - SLIDE 40IS 202 - FALL 2004
Discussion Questions (Minsky)
• Andrew Fiore on Minsky– Minsky's claims about how the mind works
are not supported by cognitive psychology. In what other useful ways might we view his theories? As philosophy? Merely as history?
![Page 41: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/41.jpg)
2004.10.21 - SLIDE 41IS 202 - FALL 2004
Discussion Questions (Minsky)
• Andrew Fiore on Minsky– Humans clearly use a great deal of common-
sense information, and although upon demand we can express some of this knowledge in terms of rules, we do not move through the world logically applying one rule after another. (The cognitive burden would overwhelm.) Why, then, represent a common-sense knowledge base in terms of rules?
![Page 42: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/42.jpg)
2004.10.21 - SLIDE 42IS 202 - FALL 2004
Discussion Questions (Minsky)
• Andrew Fiore on Minsky– What are the benefits and deficits of this
approach compared with a connectionist or associative model of the mind? (Efficiency, effectiveness, model legibility, external validity...)
![Page 43: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/43.jpg)
2004.10.21 - SLIDE 43IS 202 - FALL 2004
Agenda
• Review of Last Time
• Knowledge Representation
– The Vocabulary Problem
– Commonsense
– CYC
• Discussion Questions
• Action Items for Next Time
![Page 44: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/44.jpg)
2004.10.21 - SLIDE 44IS 202 - FALL 2004
Assignment 0 Check-In
• Suggested deliverables– SIMS email address– Focus statement– SIMS web site– SIMS coursework page
![Page 45: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/45.jpg)
2004.10.21 - SLIDE 45IS 202 - FALL 2004
Next Time
• Lexical Relations and WordNet (RRL)
![Page 46: 2004.10.21 - SLIDE 1IS 202 - FALL 2004 Lecture 16: Knowledge Representation Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30](https://reader035.vdocuments.site/reader035/viewer/2022062516/56649d375503460f94a108ce/html5/thumbnails/46.jpg)
2004.10.21 - SLIDE 46IS 202 - FALL 2004
Homework (!)
• Course Reader – 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)