Copyright © 2002 Cycorp Why use logic? CycL Syntax Collections and Individuals (#$isa and #$genls) Microtheories Foundations of Knowledge Representation.

Slides:



Advertisements
Similar presentations
Computer Science CPSC 322 Lecture 25 Top Down Proof Procedure (Ch 5.2.2)
Advertisements

Copyright © 2002 Cycorp Introduction Fundamental Expression Types Top Level Collections Time and Dates Spatial Properties and Relations Event Types Information.
The Logic of Quantified Statements
Truth Maintenance Systems. Outline What is a TMS? Basic TMS model Justification-based TMS.
Knowledge Representation
CPSC 322, Lecture 23Slide 1 Logic: TD as search, Datalog (variables) Computer Science cpsc322, Lecture 23 (Textbook Chpt 5.2 & some basic concepts from.
A Probabilistic Framework for Information Integration and Retrieval on the Semantic Web by Livia Predoiu, Heiner Stuckenschmidt Institute of Computer Science,
Copyright © 2002 Cycorp Inference in Cyc Logical Aspects of Inference Incompleteness in Searching Incompleteness from Resource Bounds and Continuable Searches.
Dynamic Ontologies on the Web Jeff Heflin, James Hendler.
CPSC 322, Lecture 23Slide 1 Logic: TD as search, Datalog (variables) Computer Science cpsc322, Lecture 23 (Textbook Chpt 5.2 & some basic concepts from.
Copyright © 2002 Cycorp Why use logic? CycL Syntax Collections and Individuals (#$isa and #$genls) Microtheories Foundations of Knowledge Representation.
Overview of Cyc Tom O'Hara NMSU AI Seminar 18 February 2002.
Copyright © 2002 Cycorp Logical Aspects of Inference Incompleteness in Searching Incompleteness from Resource Bounds and Continuable Searches Inference.
Copyright © 2002 Cycorp Events in Cyc Roles and Event Predicates Actor Slots Sub-events OE Example: Events and Roles.
Copyright © 2002 Cycorp Why use logic? CycL Syntax Collections and Individuals (#$isa and #$genls) Microtheories Foundations of Knowledge Representation.
Copyright © 2002 Cycorp A Bundle of Assertions Think of a microtheory (mt) as a set of assertions. Each microtheory bundles assertions based on –a shared.
Cyc Seven Differences between Cyc and Frames Systems.
Copyright © 2002 Cycorp Writing Efficient CycL: Part 2 Part One: General Suggestions –Simpler is Better –Use Rule Macro Predicates –Create Simplifying.
Copyright © 2002 Cycorp Writing Efficient CycL Part 1 Part One –Simpler is Better –Use Rule Macro Predicates –Create Simplifying Vocabulary Part Two –Factor.
Artificial Intelligence Chapter 17 Knowledge-Based Systems Biointelligence Lab School of Computer Sci. & Eng. Seoul National University.
Knowledge Representation II Praveen Paritosh CogSci 207: Fall 2003: Week 2 Tue, Oct 5, 2004.
Ontology Mapping with Cyc doug foxvog 14 July 2004
Semantic Web Technologies Lecture # 2 Faculty of Computer Science, IBA.
Reasoning with context in the Semantic Web … or contextualizing ontologies Fausto Giunchiglia July 23, 2004.
Notes for Chapter 12 Logic Programming The AI War Basic Concepts of Logic Programming Prolog Review questions.
1 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications Stuart Aitken Artificial Intelligence Applications.
Protege OWL Plugin Short Tutorial. OWL Usage The world wide web is a natural application area of ontologies, because ontologies could be used to describe.
Chapter 9 Integrity. Copyright © 2004 Pearson Addison-Wesley. All rights reserved.9-2 Topics in this Chapter Predicates and Propositions Internal vs.
Author: William Tunstall-Pedoe Presenter: Bahareh Sarrafzadeh CS 886 Spring 2015.
Artificial intelligence project
Easy-to-Understand Tables RIT Standards Key Ideas and Details #1 KindergartenGrade 1Grade 2 With prompting and support, ask and answer questions about.
Computer Science 101 Database Concepts. Database Collection of related data Models real world “universe” Reflects changes Specific purposes and audience.
LDK R Logics for Data and Knowledge Representation Modeling First version by Alessandro Agostini and Fausto Giunchiglia Second version by Fausto Giunchiglia.
Declarative vs Procedural Programming  Procedural programming requires that – the programmer tell the computer what to do. That is, how to get the output.
Metadata. Generally speaking, metadata are data and information that describe and model data and information For example, a database schema is the metadata.
Slide 1 Propositional Definite Clause Logic: Syntax, Semantics and Bottom-up Proofs Jim Little UBC CS 322 – CSP October 20, 2014.
General Knowledge Ontological background for everything.
EEL 5937 Ontologies EEL 5937 Multi Agent Systems Lecture 5, Jan 23 th, 2003 Lotzi Bölöni.
QUIRK:Project Progress Report December Cycorp IBM.
Simultaneously Learning and Filtering Juan F. Mancilla-Caceres CS498EA - Fall 2011 Some slides from Connecting Learning and Logic, Eyal Amir 2006.
Artificial Intelligence 2004 Ontology
Pad++: A Zooming Graphical Interface for Exploring Alternate Interface Physics Presented By: Daniel Loewus-Deitch.
EEL 5937 Ontologies EEL 5937 Multi Agent Systems Lecture 5, Jan 23 th, 2003 Lotzi Bölöni.
Some Thoughts to Consider 8 How difficult is it to get a group of people, or a group of companies, or a group of nations to agree on a particular ontology?
Computer Science CPSC 322 Lecture 22 Logical Consequences, Proof Procedures (Ch 5.2.2)
Approach to building ontologies A high-level view Chris Wroe.
Context sensitivity for networked ontologies Igor Mozetič, Marko Grobelnik, Damjan Bojadžijev Jozef Stefan Institute Slovenia.
Copy right 2004 Adam Pease permission to copy granted so long as slides and this notice are not altered Ontology Overview Introduction.
First-Order Logic Semantics Reading: Chapter 8, , FOL Syntax and Semantics read: FOL Knowledge Engineering read: FOL.
Chapter One The Science of Biology
Cyc Jaehui Park Summarized by Jaehui Park, IDS Lab., Seoul National University Presented by Jaehui Park, IDS Lab., Seoul National University.
Assumption-based Truth Maintenance Systems: Motivation n Problem solvers need to explore multiple contexts at the same time, instead of a single one (the.
Object Design More Design Patterns Object Constraint Language Object Design Specifying Interfaces Review Exam 2 CEN 4010 Class 18 – 11/03.
KNOWLEDGE REPRESENTATION Ziyang Lin March 17, 2011.
MDD-Kurs / MDA Cortex Brainware Consulting & Training GmbH Copyright © 2007 Cortex Brainware GmbH Bild 1Ver.: 1.0 How does intelligent functionality implemented.
Announcements  Upcoming due dates  Thursday 10/1 in class Midterm  Coverage: everything in lecture and readings except first-order logic; NOT probability.
ACT Science ACT Test Prep Goals – 1. Become familiar with many of the concepts that are tested on the official test 2. Be able to target the item-types.
Semantic Web Project Status
Formal Modeling Concepts
Artificial Intelligence Chapter 17 Knowledge-Based Systems
Knowledge-Based Systems Chapter 17.
Artificial Intelligence Chapter 17 Knowledge-Based Systems
Survey of Knowledge Base Content
Ontology.
Artificial Intelligence Chapter 17. Knowledge-Based Systems
CS 188: Artificial Intelligence
Artificial Intelligence Chapter 17 Knowledge-Based Systems
Representations & Reasoning Systems (RRS) (2.2)
Presentation transcript:

Copyright © 2002 Cycorp Why use logic? CycL Syntax Collections and Individuals (#$isa and #$genls) Microtheories Foundations of Knowledge Representation in Cyc

Copyright © 2002 Cycorp A Bundle of Assertions Think of a microtheory (mt) as a set of assertions. Each microtheory bundles assertions based on –a shared set of assumptions on which the truth of the assertions depends, or –a shared topic (world geography, brain tumors, pro football), or –a shared source: (CIA World Fact Book 1997, FM101-5, USA Today) the Cyc KB, as a sea of assertions

Copyright © 2002 Cycorp Avoiding Inconsistencies The assertions within a microtheory must be mutually consistent –no monotonic contradictions allowed within a single microtheory Assertions in different microtheories may be inconsistent the Cyc KB, as a sea of assertions in MT1: tables, etc., are solid in MT2: tables are mostly space in MT1: Mandela is an elder statesman in MT2: Mandela is President of South Africa in MT3: Mandela is a political prisoner

Copyright © 2002 Cycorp Every Assertion is in a Microtheory Every assertion falls within at least one microtheory Currently, every microtheory is a reified (named) term, such as #$HumanActivitiesMt or #$OrganizationMt Mts are one way of indexing all the assertions in Cyc

Copyright © 2002 Cycorp Better/faster/more scalable knowledge base building Better/faster/more scalable inferencing, too. To focus development of the Cyc knowledge base To enable shorter and simpler assertions Mandela is president vs. Mandela is president throughout 1995 in South Africa Tables are solid vs. At granularity usually considered by humans, tables are solid To cope with global inconsistency in the KB, inevitable at this scale Each mt is locally consistent (content in unrelated mts is not visible) Good for handling divergence (different points of view, scientific theories, changes over time) Why Have Microtheories?

Copyright © 2002 Cycorp Better/faster/more scalable knowledge base building Better/faster/more scalable inferencing, too. To focus development of the Cyc knowledge base To enable shorter and simpler assertions Mandela is president vs. Mandela is president throughout 1995 in South Africa Tables are solid vs. At granularity usually considered by humans, tables are solid To cope with global inconsistency in the KB, inevitable at this scale Each mt is locally consistent (content in unrelated mts is not visible) Good for handling divergence (different points of view, scientific theories, changes over time) Why Have microtheories? (cont.)

Copyright © 2002 Cycorp #$VocabularyMicrotheory -- each instance contains definitions of general concepts used in a knowledge domain (e.g., #$TransportationVocabMt, #$ComputerSoftwareVocabMt) #$TheoryMicrotheory -- each instance contains general assertions in a knowledge domain (e.g., #$TransportationMt,#$ComputerSoftwareMt). #$DataMicrotheory -- each instance contains assertions about specific individuals (e.g., #$TransportationDataMt, #$ComputerSoftwareDataMt) Some types of microtheories #$Microtheory #$TheoryMicrotheory #$DataMicrotheory #$CounterfactualContext #$PropositionalInformationThing genls #$VocabularyMicrotheory genls

Copyright © 2002 Cycorp Some types of microtheories #$Microtheory #$TheoryMicrotheory #$DataMicrotheory #$CounterfactualContext #$PropositionalInformationThing genls #$VocabularyMicrotheory genls #$PropositionalInformationThing --each instance of this collection contains assertions representing the propositional content of some #$InformationBearingThing (such as a picture, text, or database table). #$CounterfactualContext -- each instance of this collection contains at least one assertion which is not generally taken to be true in the real world (e.g., #$TheSimpsonsMt, #$SQ77bMt)

Copyright © 2002 Cycorp Explicitly relates a microtheory to a formula that is true in that microtheory. (#$ist MT FORMLA) means that the Cyc formula FORMLA is true in the microtheory MT. Microtheory predicates: #$ist (#$ist #$CyclistsMt (#$isa #$Lenat #$Person)) (#$ist #$NaiveStateChangeMt (#$implies (#$and (#$isa ?FREEZE #$Freezing) (#$outputsCreated ?FREEZE ?OBJ)) (#$stateOfMatter ?OBJ #$SolidStateOfMatter)))

Copyright © 2002 Cycorp Relates two microtheories such that one of them inherits the assertions in the other; i.e., the first microtheory has access to the assertions in the second microtheory. (#$genlMt MT-1 MT-2) means that every assertion which is true in MT-2 is also true in MT-1. #$genlMt is transitive. Microtheory predicates: #$genlMt (#$genlMt #$TransportationMt #$NaivePhysicsMt) (#$genlMt #$ModernMilitaryTacticsMt #$ModernMilitaryVehiclesMt) (#$genlMt #$EconomyMt #$TransportationMt)

Copyright © 2002 Cycorp #$genlMt Microtheory predicates, cont’d. #$BaseKB #$NaiveSpatialMt #$NaivePhysicsMt#$NaturalGeographyMt genlMt #$MovementMt genlMt #$TransportationMt genlMt

Copyright © 2002 Cycorp Finding the right microtheory Microtheory placement is important in both making assertions and asking queries. An assertion is visible in all and only the mts that inherit from the mt in which it is placed. A query is answered using all and only assertions in mts visible from the mt in which it is asked. Well-formedness requires visibility of definitional information (#$isa, #$genls, #$arity, #$arg1Isa...) for all the terms used. Good placement of assertions makes them visible in the microtheories in which they are needed. That is, good placement is not too specific. Good placement of assertions makes them invisible in microtheories in which they are not needed; otherwise, search space for inference in the lower microtheories is needlessly increased. That is, good placement is not too general.

Copyright © 2002 Cycorp Finding the right microtheory 3607 instances of #$Microtheory as of 02/05/ instances of #$GeneralMicrotheory For now, no substitute for familiarity To determine the function of some microtheory: read the #$comment examine the assertions examine the place in the mt hierarchy in which it fits if still unclear, consult the #$myCreator (or someone who has made many assertions in that microtheory)

Copyright © 2002 Cycorp Forthcoming Changes/Improvements For more efficient ontology building and inference, we want: Dynamic generation of microtheories Software “power tools” that suggest best microtheory placement for an assertion or a query More targeted, smaller contexts, i.e., re-place each assertion These, in turn, require: More explicit representation of context features e.g., topic, level of granularity, time period in which it holds,… More explicit representation of the relationships that hold between contexts (besides just #$genlMt) These improvements are part of : * RKF tools * Context overhaul

Copyright © 2002 Cycorp What is a microtheory? Why have microtheories? Some types of microtheories Microtheory predicates Finding the right microtheory Summary