1. Patrick McCauley CMSC 691S - Semantic Web Spring 2009
Cyc
Patrick McCauley
CMSC 691S - Semantic Web
Spring 2009
Semantic Web in miniature

2. Why do we need Cyc? Evolution of rule-based expert systems
MYCIN - diagnosis of blood infections
DENDRAL - chemical analysis
Rule-based systems are brittle
Cannot detect typos
Only work within a specific domain
Brittleness due to lack of “common sense”
Rule-based systems were big in the 1970’s
Brittle examples
Typos - apply for a credit loan - been at a job for 20 years, but the applicant is only 18 years old
Expert system can diagnose “rust spots” on a car as measles

3. What is Cyc? All apps can benefit from common sense
Began in 1984 by Doug Lenat
Initial goals:
KB and Ontology Building (pump priming)
NL Understanding / Interactive Dialog
Whether designing an online credit loan form or recording a patient’s medical history, all apps can benefit from common sense
Lenat realized there was “no free lunch” - took a brute force approach to encoding basic human knowledge

4. Vocab 101 Knowledge - underlying heuristics that allow us to reason
Data - Facts or statements about specific items in the world
Knowledge must be hand-crafted and entered into Cyc
Data can be gleaned or even referenced from external sources

5. Where to start?
How much does a system need to know in order to be useful?
What kinds of knowledge are necessary?
How should this knowledge be represented?
Three basic questions that guide design of Cyc

6. Priming the Pump
Need to encode basic, common sense knowledge “representing human consensus reality” - insulting to state these facts to another person
E.g. “You have ten fingers.”
Assumes ten is a number and that a person has a specific number of fingers.
E.g. “Cardinals are red.”
Assumes that cardinals are a type of bird and that birds have feathers which, in this case, are red. Also assumes “red” is a color.
Answer to first question is tough to answer - depends on what the questions are.
Answer to second question: There is a basic core understanding that is needed by ANY system.
Includes pieces of data that another person would be insulted if you asserted this fact to them.

7. As data grows, so do inconsistencies
Too much data gives rise to inconsistencies
Microtheory
Internally consistent data module
Explicitly represented logical context
Cyc knows or is told which MTs should be used to solve a problem
Dracula example
“Who was Dracula?”
“A vampire” - MICROTHEORY = Fictional Literature
“Are vampires real?”
“No” - MICROTHEORY = the real world
Subdividing microtheories adds context and helps performance

8. Cyc KB Topic Map

9. How to represent knowledge?
CycL - augmented FOPC
Each assertion in the KB carries a “truth value”
Monotonically false
Default false
Unknown
Default true
Monotonically true
Answer to third question: use CycL
FOPC - First Order Predicate Calculus
If a conflict in truth arises, Monotonically wins out (e.g. if something is Default false and Monotonically true, it is true)
Most assertions are Monotonically or default true

10. What about external data?
SKSI - Semantic Knowledge Source Integration
CycL used to describe external DB columns
SKSI is a tool kit developed by Cycorp
Helps keep KB small - just access data that exists elsewhere
Also works well for data that rapidly changes (e.g. stock prices, news headlines)
In effect, CycL can be used to create and execute SQL statements

11. Cyc I/O
Communications between applications, external data sources is easy - well defined interfaces that can be implemented by computers
Communication between users/authors is hard - need to define new knowledge without being technically savvy
E.g. An ancient Roman historian knows a lot about Tarquinius Superbus but nothing about a regular expression

12. Natural Language Processing
Extremely difficult since human speech/language is ridiculously complex
Written text often violates proper grammar, but its meaning is understood by humans
Fred saw the plan flying over Zurich.
Fred saw the mountains flying over Zurich.

13. CycNL to the rescue!
Lexicon - “contains syntactic and semantic information about English words”
Relationships between English words and Cyc constants are stored
CycNL is divided into three parts - first part is the Lexicon

14. CycNL - Syntactic Parser
Uses a phrase-structure grammar, context free rules
Builds multiple tree structures for each phrase/sentence
However, some trees do not make “syntactic” sense
Syntactic Parser is the second part
Some trees do not make sense…this is where the third part comes in
E.g. the phrase “with a telescope” can be interpreted in two ways:
John used a telescope to see the light
John saw the light which had a telescope

15. CycNL - Semantic Interpreter
Transforms results into CycL formulas
Result is “pure” CycL
Pure CycL can be used to make queries against the KB or extract data from external sources
KB is consulted to see whether telescopes are used as instruments in seeing or if lights are things that usually have telescopes. Since the second statement causes a constradiction in the KB, it is rejected.

16. How is this useful to Humans?
Ambient Research Assistant
flexibility and ease of communication are key
Must be capable of “learning”
Deciding what facts to learn
Learning those facts
Learning of rules
Generalizing rules
Testing and revision
We now have vast quantities of data and complex relationships, so how do we find what we really want?
Need to create an Assistant (Agent) who is capable of learning and anticipating needs
1. Deciding what facts to learn. An assistant system must reason about what knowledge gaps would be most cost-effective to fill in any given context. If a researcher is considering submitting a paper to an upcoming conference, finding submission dates and contact information is likely to be more useful than organizing older work, and should be a higher priority task.
2. Learning those facts. The factual gaps should be filled, from available documentation, online sources, and/or communication with the scientist being assisted. In the aforementioned example, the system should set out to learn any missing facts by appropriately querying all its available sources, both online ones and people, starting with the conference web site or call-for-papers and progressing to information that requires some knowledge of the research in question. The submission information may depend on the track to which the paper is being submitted, which requires knowledge of the research topic.
3. Learning of rules. Once knowledge is acquired, it is possible to hypothesize general rules. If several conferences have been identified, an assistant might correlate information about each of them and conclude that conferences in some broad field (e.g. machine learning) are often of interest, or that knowing submission dates is often useful. Such a rule can then guide the selection and prioritization of tasks.
4. Generalizing rules. Carrying this example through, an effective assistant might learn from one or more identified rules that, for some particular user or researcher, learning and then tracking dates by which some particular action must occur is valuable.
5. Testing and revision. The rules, especially the generalized rules, will need to be tested independently of how they were produced. For example, when a general rule about tracking dates is hypothesized, a system might discover after experimentation that it is less helpful to track and remind a user of recurring dates, such as a weekly report that must be made to an overview body. This discovery would force revision (tightening) of the generalized rule.

17. Benefits of Assistant Capable of searching much faster than humans
Availability - supercedes the 9-to-5

18. “Truly Intelligent” Assistant
Plan Recognition
Learning NL

19. Acknowledgements CYC Website http://www.cyc.com/
CYC: A Large-Scale Investment in Knowledge Infrastructure
Mapping Ontologies into Cyc
Common Sense Reasoning – From Cyc to Intelligent Assistant

24. CycL is Cyc’s language
"Bill Clinton belongs to the collection of U.S. presidents" and (#$isa #$BillClinton #$UnitedStatesPresident) "All trees are plants". (#$genls #$Tree-ThePlant #$Plant) "Paris is the capital of France." (#$capitalCity #$France #$Paris) "a fact about sets" (#$implies (#$and (#$isa ?OBJ ?SUBSET) (#$genls ?SUBSET ?SUPERSET)) (#$isa ?OBJ ?SUPERSET))