SNePS 3 for Ontologies Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science University at Buffalo,

Slides:

Advertisements

Similar presentations

School of Computer Science & Information Technology G6DICP - Lecture 16 Inheritance.

Advertisements

An F-Measure for Context-Based Information Retrieval Michael Kandefer and Stuart C. Shapiro University at Buffalo Department of Computer Science and Engineering.

SPECIFYING MODALITIES IN THE MGLAIR ARCHITECTURE Stuart C. Shapiro and Jonathan P. Bona Department of Computer Science and Engineering And Center for Cognitive.

S.C. Shapiro Knowledge Representation and Reasoning Stuart C. Shapiro Professor, CSE Director, SNePS Research Group Member, Center for Cognitive.

Listen and choose monkey bird frog horse fish Listen and choose 6.

Research in Knowledge Representation and Reasoning Stuart C. Shapiro Department of Computer Science & Engineering Center for MultiSource Information.

Set-Oriented Logical Connectives: Syntax and Semantics Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive Science.

The GLAIR Architecture for Cognitive Robotics Stuart C. Shapiro Department of Computer Science & Engineering and Center for Cognitive Science.

The GLAIR Cognitive Architecture and Prospects for Consciousness Stuart C. Shapiro Department of Computer Science & Engineering and Center.

A Categorization of Contextual Constraints Michael Kandefer and Stuart C. Shapiro University at Buffalo Department of Computer Science and Engineering.

The GLAIR Cognitive Architecture Stuart C. Shapiro and Jonathan P. Bona Department of Computer Science & Engineering Center for Cognitive Science.

The GLAIR Architecture for Cognitive Robots Stuart C. Shapiro Department of Computer Science & Engineering and Center for Cognitive Science.

Knowledge Representation for Self-Aware Computer Systems Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive.

S.C. Shapiro Development of a Cognitive Agent Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive Science.

Combining Numeric and Symbolic Reasoning in SNePS Stuart C. Shapiro Department of Computer Science & Engineering Center for MultiSource Information.

Inconsistency Tolerance in SNePS Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science University.

S.C. Shapiro Endowing Agents with a Personal Sense of Time Haythem O. Ismail & Stuart C. Shapiro Department of Computer Science and Engineering.

Symbolic Representation and Reasoning an Overview Stuart C. Shapiro Department of Computer Science and Engineering, Center for Multisource.

S.C. Shapiro Knowledge Representation for Natural Language Competence Stuart C. Shapiro Department of Computer Science and Engineering and.

Cassie as a Self-Aware SNePS/GLAIR Agent Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science.

Natural Language Processing and Knowledge Representation دانشگاه صنعتی اميرکبير (پلي تکنيک تهران) دانشكده مهندسي كامپيوتر آزمايشگاه سيستم‌هاي هوشمند

Research in Knowledge Representation, Reasoning, and Acting Stuart C. Shapiro Professor, CSE Affiliated Professor, Linguistics, Philosophy.

S.C. Shapiro Knowledge Representation and Reasoning Stuart C. Shapiro Professor, CSE Director, SNePS Research Group Member, Center for Cognitive.

Human Cognition in Mechanical Engineering Design Roots are back in the 1970’s at the Roots are back in the 1970’s at the Engineering Design Laboratory.

S.C. Shapiro An Intelligent Interface to a GIS Stuart C. Shapiro Professor, CSE Director, SNePS Research Group Member, Center for Cognitive.

Semantics of a Propositional Network Stuart C. Shapiro Department of Computer Science & Engineering Center for MultiSource Information Fusion.

S.C. Shapiro Symbol-Anchoring in Cassie Stuart C. Shapiro and Haythem O. Ismail Department of Computer Science and Engineering and Center for.

A Logic of Arbitrary and Indefinite Objects Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science.

Clarke, R. J (2001) L951-08: 1 Critical Issues in Information Systems BUSS 951 Seminar 8 Arguments.

S.C. Shapiro Symbol Anchoring in a Grounded Layered Architecture with Integrated Reasoning Stuart C. Shapiro Department of Computer Science.

Improving Recovery for Belief Bases Frances L. Johnson & Stuart C. Shapiro Department of Computer Science and Engineering, Center for Multisource.

A Logic of Arbitrary and Indefinite Objects Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science.

Research in Knowledge Representation, Reasoning, and Acting Stuart C. Shapiro Professor, CSE Affiliated Professor, Linguistics, Philosophy.

S.C. Shapiro An Introduction to SNePS 3 Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive Science State.

Dr. Dona Warren, Department of Philosophy The University of Wisconsin-Stevens Point Argument Structure IV) Lesson 3a: Inference Erasers: “and” (with Dependent.

The Semantic Web Is it “The Web guy’s shortcut to A.I.”? Or more? Or less?

Smithsonian Museum Of Natural History(SMNH) Lesson Plans For K-12 And Class Field Trip.

6 < > 99 8 = 8 Click mouse for next screen. Lesson 2.

Inference Graphs: A Roadmap Daniel R. Schlegel and Stuart C. Department of Computer Science and Engineering L A – Logic of Arbitrary.

CSE PredLogic 1 Knowledge Representation with Logic: First Order Predicate Calculus Outline –Introduction to First Order Predicate Calculus (FOPC)

NATURAL LANGUAGE UNDERSTANDING FOR SOFT INFORMATION FUSION Stuart C. Shapiro and Daniel R. Schlegel Department of Computer Science and Engineering Center.

Concurrent Inference Graphs Daniel R. Schlegel Department of Computer Science and Engineering Problem Summary Inference graphs 2 in their current form.

1 Berendt: Advanced databases, first semester 2011, 1 Advanced databases – Inferring new knowledge.

Concurrent Reasoning with Inference Graphs Daniel R. Schlegel and Stuart C. Shapiro Department of Computer Science and Engineering University at Buffalo,

The different rock layers of Earth’s crust.. The 3 layers The Paleozoic, Mesozoic and Cenozoic eras are the 3 most recent layers of Earth’s crust. They.

STRATEGY Process, Content, Context

Ontology-Based Computing Kenneth Baclawski Northeastern University and Jarg.

Aerospace Engineering Laboratory I

At the end of this lesson, you will be able to Explain what dependent reasons are Use the “and” family of inference erasers to communicate and analyze.

Knowledge Representation and Reasoning in SNePS for Bioinformatics Stuart C. Shapiro Department of Computer Science and Engineering, and Center.

IB Computer Science – Logic

Fish Classification Chordates 7 Vertebrates 1. Jawless fish (lampreys)4. Amphibians7. Mammals 2. Cartilaginous fish (sharks, rays)5. Reptiles 3. *Bony.

STEM Fair Information Night September 15, 2011 Agenda What is a STEM Fair? What does a STEM Fair Project look like? Where do I Get Ideas? Allowable Topics.

Charting the Potential of Description Logic for the Generation of Referring Expression SELLC, Guangzhou, Dec Yuan Ren, Kees van Deemter and Jeff.

Computer Science: A Structured Programming Approach Using C1 5-5 Incremental Development Part II In Chapter 4, we introduced the concept of incremental.

1 Ontological Foundations For SysML Henson Graves September 2010.

Subject Area(s): Science adybugs (Facts/Lifecyle) Jessica Coleman.

Science Test Review.

a valid argument with true premises.

Reading Lab Adult Education Center.

Let’s connect ideas logically!

What do cats eat ? They eat … 2A : Unit 8.

Implementation Details of the Text Classification Project

Ernest Davis Automating Commonsense Reasoning for Science

What is the difference between a Vertebrate and an Invertebrate?

MGLAIR Modal Grounded Layered Architecture with Integrated Reasoning

Mystery Picture ????????????.

COMPUTER HISTORY, PRESENT & FUTURE. What is a Computer? A computer is a machine that can be instructed to carry out sequences of arithmetic or logical.

Vertebrate Invertebrate

Presentation transcript:

SNePS 3 for Ontologies Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science University at Buffalo, The State University of New York 201 Bell Hall, Buffalo, NY

September, 2004S. C. Shapiro2 What Is SNePS? A logic-based And network-based KRR system To support NL understanding & generation And commonsense reasoning Including ontological reasoning.

September, 2004S. C. Shapiro3 Logic-Based As expressive as higher-order logic Broad set of logical rules of inference.

September, 2004S. C. Shapiro4 Network-Based Efficient, path-based reasoning Restricted set of logical rules of inference Useful for ontological reasoning.

September, 2004S. C. Shapiro5 SNePS 2 Implemented Long history of use.

September, 2004S. C. Shapiro6 Ontological Reasoning in SNePS 2 DisjointSubclass({Urochordate, Cephalachordate, Vertebrate}, Chordate)! DisjointSubclass({Mammal, Bird, Fish}, Vertebrate)! DisjointSubclass({Dog, Cat}, Mammal)! all(x)(Subclass(x, Vertebrate) Have(x, spine)). : Subclass(Dog, Chordate)? Subclass(Dog,Chordate) : Subclass(Cat, Cephalachordate)? ~Subclass(Cat,Cephalachordate) : Have(Dog, spine)? Have(Dog,spine) : Have(Cephalachordate, spine)? ~Have(Cephalachordate,spine)

September, 2004S. C. Shapiro7 SNePS 3 Partially implemented In development.

September, 2004S. C. Shapiro8 Basic Ideas of SNePS 3 Arbitrary Terms (any x R (x)) Indefinite Terms (some x (y 1 … y n ) R (x)) Type checking of arguments

September, 2004S. C. Shapiro9 Ontology in SNePS 3 Isa({(any Arb1 Isa(Arb1, Urochordate)), (any Arb2 Isa(Arb2, Cephalachordate)), (any Arb3 Isa(Arb3, Vertebrate))}, Chordate) Isa({(any Arb5 Isa(Arb5, Mammal)), (any Arb6 Isa(Arb6, Bird)), (any Arb7 Isa(Arb7, Fish))}, Vertebrate) Isa({(any Arb8 Isa(Arb8, Dog)), (any Arb9 Isa(Arb9, Cat)), (any Arb10 Isa(Arb10, Monkey)), (any Arb11 Isa(Arb11, Ape))}, Mammal) Isa({(any Arb12 Isa(Arb12, Gorilla)), (any Arb13 Isa(Arb13, Chimp)), (any Arb14 Isa(Arb14, Orangutan))}, Ape) Isa(J Fred, Chimp) Property((any Arb3 Isa(Arb3, Vertebrate)), furry)

September, 2004S. C. Shapiro10 Inferences Isa((any Arb8 Isa(Arb8, Dog)), Chordate) Property((any Arb13 Isa(Arb13, Chimp)), furry) Isa(J Fred, Chordate) Property(J Fred, furry)

September, 2004S. C. Shapiro11 Collaboration Ad Looking to collaborate with content providers Or research on design of ontologies

September, 2004S. C. Shapiro12 More Information on SNePS and The SNePS Research Group