What is Knowledge Representation and Reasoning? Tommie Meyer - - PowerPoint PPT Presentation

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What is Knowledge Representation and Reasoning? Tommie Meyer - - PowerPoint PPT Presentation

Dec 10, 2022 956 likes •1.42k views

What is Knowledge Representation and Reasoning? Tommie Meyer Department of Computer Science University of Cape Town, and Centre for Artificial Intelligence Research (CAIR) ICTAC 2018 tmeyer@cs.uct.ac.za http://www.cs.uct.ac.za/tmeyer 1 /

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What is Knowledge Representation and Reasoning?

Tommie Meyer

Department of Computer Science University of Cape Town, and Centre for Artificial Intelligence Research (CAIR) ICTAC 2018 tmeyer@cs.uct.ac.za http://www.cs.uct.ac.za/˜tmeyer

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Artificial Intelligence

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Knowledge Representation and Reasoning

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Knowledge Representation and Reasoning

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Knowledge Representation and Reasoning

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Knowledge Representation and Reasoning

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Outline of my talk

◮ Knowledge Representation and Reasoning ◮ An example of a KRR system ◮ Description logics ◮ Ontology engineering ◮ KRR and Deep Learning ◮ Conclusion

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Knowledge Representation and Reasoning

Knowledge Representation and Reasoning

◮ Finding appropriate ways of representing knowledge about a

given domain of interest

◮ Developing methods to derive implicit consequences from

explicitly represented information. The dominant approach is logic-based

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Implicit consequences

Italian English Person Lazy LatinLover

{disjoint,covering}

Gentleman Hooligan

{disjoint}

Example due to Enrico Franconi

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Implicit consequences

Simple reasoning example

Italian English Person Lazy LatinLover

{disjoint,covering}

Gentleman Hooligan

{disjoint}

There aren’t any Latin lovers Therefore all Italians are lazy

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Knowledge Representation and Reasoning

All these sub-topics involve logic-based reasoning

◮ Even semantic networks

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Example: Open domain question answering

IBM’s Watson

◮ “An application of advanced Natural Language Processing,

Information Retrieval, Knowledge Representation and Reasoning, and Machine Learning technologies to the field of

  • pen domain question answering”

◮ It comprehensively defeated the all-time best players in the

American TV quiz show Jeopardy!

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Example: IBM’s Watson

Ken Jennings, famous for winning 74 games in a row on the TV quiz show, borrowing a line from a “Simpsons” episode: “I, for one, welcome our new computer overlords.”

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Description Logics

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Description Logics

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Description Logics

A logic-based formalisation of ontologies

◮ Decidable fragments of first-order logic ◮ Concepts: unary predicates; Roles: binary predicates ◮ A formal model-theoretic semantics

Reasoning Services

◮ Instance checking ◮ Concept subsumption and equivalence ◮ Concept satisfiability and knowledge base consistency ◮ Worst case complexity terrible but performs well in practice

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A partial description of the DL ALC

Concept construction

◮ ¬C; C ⊓ D; C ⊔ D; (∀R.C); (∃R.C)

TBox

◮ Subsumption: C ⊑ D; Equivalence C ≡ D

ABox

◮ C(a) and R(a, b)

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A partial description of the DL ALC

TBox

◮ Person ⊑ Animal ⊓ Biped; Person ≡ Man ⊔ Woman ◮ Mother ≡ Woman ⊓ (∃ParentOf.Person) ◮ MotherWithDaughters ≡ Mother ⊓ (∀ ParentOf.Woman)

ABox

◮ MotherWithDaughters(Mary); ParentOf(Mary,Sam)

Implicit consequence: Instance checking

◮ Woman(Sam)

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A partial description of the DL ALC

TBox

◮ Person ⊑ Animal ⊓ Biped; Person ≡ Man ⊔ Woman ◮ Mother ≡ Woman ⊓ (∃ParentOf.Person) ◮ MotherWithDaughters ≡ Mother ⊓ (∀ ParentOf.Woman)

ABox

◮ MotherWithDaughters(Mary); ParentOf(Mary,Sam)

Implicit consequence: Subsumption checking

◮ Man ⊑ Person

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A partial description of the DL ALC

TBox

◮ Human ⊑ ∃hasHeart.∃hasPosition.Left ◮ Human⊓SitusInversus ⊑ ∃hasHeart.∃hasPosition.Right ◮ ∃hasHeart.∃hasPosition.Left ⊓

∃hasHeart.∃hasPosition.Right⊑ ⊥ Implicit consequences: Concept unsatisfiability

◮ Human ⊓ SitusInversus ⊑ ⊥ ◮ Add Human(Sam) and SitusInversus(Sam): Inconsistent

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Ontology Engineering

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Ontology Engineering

A formal way to conceptualise an application domain

◮ Concepts of interest in an application domain ◮ Properties describing concept features and attributes ◮ Perhaps some restrictions on the attributes and features

Ontology Engineering

◮ The construction and development of ontologies

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A visual representation of an ontology

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Prot´ eg´ e: An Ontology Editing Tool

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A reasoner at the backend of Prot´ eg´ e

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Ontology Engineering

◮ Tools such as Prot´

eg´ e and the reasoners underlying it are possible because of the Web Ontology Language OWL 2

◮ OWL 2 is the official ontology language endorsed by the World

Wide Web Consortium (W3C)

◮ OWL 2 is based on the description logic known as SROIQ

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OWL 2

OWL 2 Profiles

RDFS OWL 2 QL OWL 2 EL OWL 2 RL OWL 2 DL

Picture courtesy of Roman Kontchakov

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OWL 2 QL and Ontology Based Data Access

  • ntology-based data integration

sources

q

sources sources

  • ntology

conceptual layer data layer

Picture courtesy of Riccardo Rosati

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OWL 2 QL and Ontology Based Data Access

Reasoning

Result

Query

Data Source

Logical Schema Schema / Ontology

Picture courtesy of Riccardo Rosati

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OWL 2 QL and Ontology Based Data Access

Query

Result

Reasoning

Data Source

Logical Schema Schema / Ontology

Picture courtesy of Riccardo Rosati

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OWL 2 QL and Ontology Based Data Access

Reasoning Rewritten Query Query

Result

Reasoning

Data Source

Logical Schema Schema / Ontology

Picture courtesy of Riccardo Rosati

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Artificial Intelligence

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AI vs Consciousness

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Doomsday AI

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Deep Learning

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The Success of Deep Learning

◮ Sophisticated techniques for function fitting coupled with Big

Data

◮ Successful identification of a class of practical applications ◮ We’ve shifted the goal posts a bit

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The Real Surprise Underlying Deep Learning

Some tasks, typically associated with perception or cognition can be approximated to a reasonable extent by fitting functions to data.

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Deep Learning: Successful but Brittle

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AI and the Game of Go

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AI and Poker

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Today, machines lack contextual reasoning capabilities, and their training must cover every eventuality, which is not only costly, but ultimately impossible. We want to explore how machines can acquire human-like communication and reasoning capabilities, with the ability to recognize new situations and environments and adapt to them.

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Conclusion

◮ Deep Learning is function-fitting ◮ KRR is model-fitting ◮ The question is not whether to use Deep Learning, or KRR, or

some other AI technique, but how to combine these techniques

◮ The best way to do so is for AI researchers to be exposed to all

these techniques

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Acknowledgement

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