siks december 2008 history of knowledge representation

1 SIKS Course - Knowledge Modelling

Rinke Hoekstra

History of Knowledge Representation

10-12-2008

http://www.lri.jur.uva.nl/new/index.html


SIKS Course - Knowledge Modelling2

Caveat Emptor

About me… Knowledge Engineering Ontologies Web Ontology Language (OWL 2)

DissertationOntology Representation: Design Patterns and Ontologies that Make Sense (to be published spring 2009, I hope)

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Overview

In the beginning… (400 BC – 1900s)

Scruffies vs. Neats (1970-ies) The Dark Ages (1980-ies) Engineering Revival (1990-ies) The ‘O’ Word (1995 onwards)

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IN THE BEGINNING…400BC – 1900s

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Aristotle (384 BC – 322 BC)

Dialectics reductio ad absurdum

Deduction premises conclusion (Plato)

Syllogisms Standard logic until the 19th century

Categories

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Syllogisms

ExampleMajor premise All mortal things dieMinor premise All men are mortal thingsConclusion All men die

Forms

Names Barbara (AAA), Celarent (EAE), …

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code quantifier subj. copula pred. type

A All S are P universal affirmatives

E No S are P universal negatives

I Some S are P particular affirmatives

O Some S are not P particular negatives



Aristotle’s Categories

Substance primary vs. secondary

Quantity extension

Quality nature

Relation

Place position relative to

environment

Time pos. relative to events

Position condition of rest (action)

State condition of rest (affection)

Action production of change

Affection reception of change

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Porphyry of Tyre (233–c. 309)

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Ramon Llull (1232 – 1315)

Mechanical aids to reasoning

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Scientific Revolution (17th and 18th century)

Dualism René Descartes (1596 – 1650)

Body as machine <-> Mind Empiricism

John Locke (1632 – 1704)

Royal Society Mercantilism Engineering

Christiaan Huygens (1629 – 1695)

Blaise Pascal (1623 – 1662)10-12-2008



John Wilkins (1614 – 1672)

Universal Character Replace latin (Metric system)

Tree with 3 layers

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Layer Code Meaning

Genus (40) Zi Beast (mammal)

Difference t Rapacious beast of the dog kind

Species a Dog



Gottfried Wilhelm Leibniz (1646 – 1716)

Characteristica Universalis“Once the characteristic numbers of most notions are determined, the human race will have a new kind of tool, a tool that will increase the power of the mind much more than optical lenses helped our eyes, a tool that will be as far superior to microscopes or telescopes as reason is to vision.”

(Leibniz, Philosophical Essays)

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Calculators

Pascaline Addition Substraction

Stepped Reckoner Multiplication Division Binary System

… but Leibniz wanted more Calculus Ratiocinator

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Another Leibniz Quote

"If controversies were to arise, there would be no more need of disputation between two philosophers than between two accountants. For it would suffice to take their pencils in their hands, and say to each other: Let us calculate.”

Leibniz, Dissertio de Arte Combinatoria, 1666

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Linnaeus (1707-1778) – Systema Naturae

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… so, what’s new?

Syllogisms Rules of valid reasoning

Reasoning as Calculation Symbol Manipulation

Categories Top-down categories of thought

Universal Character/Systema Naturae Bottom-up inventory of phenomena in reality

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Gottlob Frege (1884 – 1924)

Logic Study of correct reasoning

Arithmetics and Mathematics

Begriffschrift Formal Language (of Meaning) Axiomatic Predicate Logic Variables, Functions, Quantifiers

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Computers

Algorithms Alan Turing (1912 – 1954)

Processor/Memory Architecture Neumann János Lajos (1903 – 1957)

Automatic Theorem Proving Resolution

Artificial Intelligence! But…

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Theorem Proving

``… great theorem proving controversy of the late sixties …’’ (Newell, 1982)

Problematic No human scale No organisation No procedures

Small, theoretically hard problems

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SCRUFFIES VS. NEATS1970ies

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Two Schools (1970ies and onwards)

Philosophy (Neats) Clean, uniform language Knowledge derives from small set of ‘elegant’

first principles Theoretical understanding of reality

Cognitive Psychology (Scruffies) Cognitively plausible language Knowledge is what’s in our heads Human intelligence and behaviour

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Artificial Intelligence

“. . . an entity is intelligent if it has an adequate model of the world […], if it is clever enough to answer a wide variety of questions on the basis of this model, if it can get additional information from the external world when required, and can perform such tasks in the external world as its goals demand and its physical abilities permit.”

(McCarthy and Hayes, 1969, p.4)

Frame Problem!

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Epistemic and Heuristic adequacy

McCarthy & Hayes: Representation vs. Mechanism

Epistemic Adequacy Correct representation

Heuristic Adequacy Correct reasoning

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Heuristic vs. Epistemic views in Psychology

Knowledge is about the how Problem Solving Production Systems

Knowledge is about the what Natural Language Memory Semantic Networks

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Information Processing System (IPS)

Computer as metaphor of the mind“the human operates as an information processing machine’’

Newell & Simon, 1972

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Production Systems (1)

Processor Interpreter Elementary Information Processes (EIP) Sequence of EIPs a function of symbols in

memory Production Rules (Emil Post, 1943)

if … then … Rule ‘fires’ if interpreter finds a match

between condition and symbols in memory Sequential ≠ material implication

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Production Systems (2)

Adequacy? Correspondence to human reasoning Not ‘clean’ or ‘logical’

Escape limitations of theorem provers Local, rational control of problem solving Easily modifiable

Drawback: Natural language?

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Semantic Networks (1)

Natural Language Ground lexical terms in a model of reality

Semantic Memory M. Ross Quillian (1966) Associative Memory

Semantic Networks Graph Based Nodes, planes and pointers

subclass, modification, disjunction, conjunction, subject/object

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Adequacy? Correspondence to human memory

Response time Property inheritance

Extensions Named Attributes (type/token) Concepts vs. Examples (instances)

Jaime Carbonell, 1970

Sprawl of variants

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Frames (1)

Criticism from Cognitive Science Frames, Marvin Minsky (1975) Scripts, Roger Schank (1975)

Frames Larger `chunks’ of thought Situations (akin to planes)

Default values10-12-2008

… that which is always true …

terminals, `slots’• simple assignment• complex condition (relations)



Frames (2)

Frame system Related frames that share the same terminals … different viewpoints on the same situation Knowledge Reuse

Information Retrieval Network Standard matching procedure

Fixed perspective: situations, objects, processes (object-oriented design)

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Technical problems Weak inference (inheritance) Unclear semantics

“What’s in a link?”, Bill Woods (1975) “What IS-A is and isn’t”, Ron Brachman (1983)

Consider the semantics of the representation itself

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Frame (like) Languages

Emphasis Interrelated, internally structured concepts

Knowledge Representation Language (KRL) Bobrow and Winograd (1976)

Structured InheritanceNetworks Ron Brachman (1979)

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Knowledge Representation Language (KRL)

Known entity: prototype Description by reusable descriptors Descriptions by comparison to prototype +

extension Modes of description:

membership, relationship, role (object/event) Reasoning:

Process of recognition, procedural attachments Inference mechanism determines meaning

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SI Networks

KL-ONE (Brachman, 1979; Brachman & Schmolze, 1985)

Descriptions Role/Filler Descriptions Structural Descriptions Interpretive Attachments

Role modality types: inherent, derivable, obligatory

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SI-Network of an Arch

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Epistemological Status

Cognitive plausibility Epist. Status Relation to reality?

Relation to representation language?

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The Knowledge Level (Allen Newell, 1982)

“… the crux for AI is that no one has been able to formulate in a reasonable way the problem of finding the good representation, so that it can be tackled by an AI system”

(Newell, 1982, p.3)

Computer System Level Medium System Processing Components Composition Guidelines Behavior

Independent, but reducible to lower level

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The Knowledge Level (2)

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“There exists a distinct computer systems level, lying immediately above the symbol level, which is characterised by knowledge as the medium and the principle of rationality as the law of behaviour”

(Newell, 1982, p. 99)

Level Description

Knowledge Level Knowledge and Rationality

Symbol/Program Level Symbols and Operations

Logic Level Boolean logic switches (AND/OR/XOR)

Circuit Level Circuits, connections, currents

Device Level Physical description



The Knowledge Level (3)

Not a stance viz. the intentional stance (Dennett, 1987)

No representation at knowledge level (concepts, tasks, goals) Knowledge level = knowledge itself! Representation always at the symbol level

Knowledge representation Representation of knowledge, not reality

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Brachman’s Triangle Extended (Hoekstra, 2009)

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Representation and Language

Brachman’s levels in Semantic Nets

Primitives of KR languages Requirements

neutrality, adequacy, well-defined semantics

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Level Description

Implementational Graphs, atoms, pointers

Logical Propositions, predicates, operators

Conceptual Semantic or conceptual relations (cases), primitive objects, actions

Linguistic Arbitrary concepts, words, expressions



Epistemological Level

Missing level Knowledge-structuring primitives

“The formal structure of conceptual units and their interrelationships as conceptual units (independent of any knowledge expressed therein) forms what could be called an epistemology.”

(Brachman, 1979, p.30)

Two interpretations Adequacy of Language for some level Representation at a level

e.g. Logical primitives as concepts

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Optimism

Modern Knowledge Representation Representation of expert knowledge Performance over Plausibility

Modern Languages Defined semantics Clear epistemological status

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THE DARK AGES1980ies

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Practical Applications (1980s)

Expert Systems Production Rules Rules of thumb

Relatively clear status Memory in PSI of secondary importance

Severe problems Scalability Reusability

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MYCIN and GUIDON (William Clancey, 1983)

MYCIN: medical diagnosis GUIDON: medical tutoring

“transfer back” expert knowledge Problematic

No information about how the rule-base was structured: design knowledge

“Compiled Knowledge”

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Role of Knowledge in Problem Solving

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Knowledge Types

Order of rules: problem solving strategy Structure in knowledge

Common causes before unusual ones Justification: domain theory

Identification rules Causal rules World fact rules Domain fact rules

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Convergence?

Heuristic vs. Epistemological Adequacy Two approaches

Different formalisms Same types of knowledge

Two solutions Components (Clancey) Knowledge Structuring (Brachman)

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Problems

Knowledge Acquisition Bottleneck (Feigenbaum, 1980) The difficulty to correctly extract

relevant knowledge from an expert into a knowledge base

Interaction Problem (Bylander and Chandrasekaran, 1987) Different types of knowledge cannot be

cleanly separated Problem for reuse

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ENGINEERING REVIVAL1990s

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Knowledge Acquisition

Ensure Quality Reuse

Ad hoc Methodologies Engineering Knowledge modelling vs. extraction

Implementation guided by Specification

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CommonKADS (Wielinga et al., 1992, van Heijst et al., 1997)

Knowledge Level Model Independent of KR language Solution to the KA Bottleneck?

Limited Interaction Hypothesis Solution to the Interaction Problem?

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Reuse

Role limiting Direct reuse Index symbol level representations Detailed blueprints

Skeletal Models Reuse of `understanding’ Knowledge-level ‘sketches’ Library of reusable knowledge components

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Knowledge Types (1)

Control Knowledge Task Knowledge Inference Knowledge

Problem Solving Methods (Breuker & van de Velde, 1994)

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Knowledge Types (2)

Domain Knowledge Index PSM’s for reuse Epistemology Generic domain theory

What an expert system ‘knows’ about:

ONTOLOGY

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Functional Perspective (Hector Levesque, 1984)

Descend to the Symbol Level?

Knowledge Base Abstract datatype Competencies

Set of TELL/ASK queries

Capabilities of KB Function of queries/answers,

assertions

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Knowledge Based Systems

Architecture Specialised KR languages Specialised Services

Performance guarantees Domain Theory

Identification, Classification KL-ONE like languages…

Control Knowledge Rules…

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The return of logic (Levesque & Brachman, 1987)

Classification as logical inference Exact semantics

Trade-off Expressive power Computational efficiency

Restricted Language Thesis“… general purpose knowledge representation systems should restrict their languages by omitting constructs which require non-polynomial (or otherwise unacceptably long) worst-case response times for correct classification of concepts.” (Doyle & Patil, 1991)

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Description Logics (Baader & Hollunder, 1991)

KL-One, NIKL, KL-Two, LOOM, FL, KANDOR, KRYPTON, CLASSIC …

Quest Expressive Sound & Complete

Decidable

KRIS, SHIQ, SHOIN, SROIQ, …

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… and the rest?

Domain Theory Causal Knowledge Naïve Physics Qualitative Reasoning

(J. de Kleer, K.D. Forbus, B. Bredeweg, …)

Strategic Knowledge Logic-based approaches

Prolog, Datalog, etc.. … no principled effort.

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THE ‘O’ WORD1995 and onwards

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Oh no! Not that again!



Pop Quiz

What is an ontology?

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Ontology

“Ontology or the science of something and of nothing, of being and not-being of the thing and the mode of the thing, of substance and accident”

G.W. Leibniz

“… ontology, the science, namely, which is concerned with the more general properties of all things.”

Immanuel Kant

The nature of being Aristotle’s categories

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Knowledge Representation (Davis, Shrobe, Szolovits, 1993)

Surrogate Set of ontological commitments

through language and domain theory Fragmentary theory of intelligent reasoning

sanctions heuristic adequacy Medium for pragm. efficient computation

way of formulating problems (Newell) Medium of human expression

``Universal Character’’(Wilkins, Leibniz, … and Stefik, 1986)

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Ontology Definitions

Knowledge Management An explicit (knowledge level) specification of a

conceptualization (a.o. Gruber, 1994)

Knowledge Representation An explicit (symbol level) specification of a

conceptualisation

Philosophy A formal specification of an ontological theory

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THE END

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siks december 2008 history of knowledge representation

Education

gottfried wilhelm leibniz

leibniz quoteif controversies

new kind of tool

mortal thingsconclusionall

whats new

iesengineering revival

design patterns

formsnamesbarbara aaa