openhpi 5.2 - dl inference and reasoning

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Dr. Harald Sack

Hasso Plattner Institute for IT Systems Engineering

University of Potsdam

Spring 2013

Semantic Web Technologies

Lecture 5: Knowledge Representations II02: DL Inference and Reasoning

Semantic Web Technologies , Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam

Lecture 5: Knowledge Representations II

Open HPI - Course: Semantic Web Technologies

02 DL Inference and ReasoningOpen HPI - Course: Semantic Web Technologies - Lecture 5: Knowledge Representations II

Lecture: Semantic Web Technologies, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam, WS 2012/13

Open World Assumption - OWA

•When we have an empty DL ontology, everything is possible

•We then constrain an ontology iteratively, making it more restrictive as we go

•We state what is not possible, what is forbidden or excluded

Sheep ⊑ Animal ⊓ ∀hasLimbs.Leg

• Can Sheep fly?

• No idea, but probably yes (according to our knowledge base)

Sheep ⊑ Animal ⊓ ∀hasLimbs.Leg

• In the OWA, unless we have a statement (or we can infer) “sheep can/cannot fly” we return “don’t know”

• In the real world, we are used to deal with incomplete information

• In the Semantic Web we expect people to extend our own models (but we don‘t worry in advance how)

• The OWA assumes incomplete information by default

• Therefore, we can intentionally underspecify our models and allow others to reuse and extend

further ext

ension possi

Sheep ⊑ Animal ⊓ ∀hasLimbs.Leg ⊓ canFly

Closed World Assumption - CWA

• Closed World Systems require a place to put everything

• You can’t say anything until there’s somewhere to say it, as e.g. a slot on a frame, field on an OO class, column in a DB

• In Close World Systems, we state what is possible and have to specify all knowledge

• the CWA holds that anything that cannot be shown to be true is false; no explicit declaration of falsehood is needed.

Sheep can‘t fly

Open World vs. Closed World Assumption

• OWA: Open World AssumptionThe existence of further individuals is possible, if they are not explicitly excluded.

• CWA: Closed World AssumptionIt is assumed that the knowledge base contains all individuals.

if we assume that we know everything about Bill then all of his children are male

child(Bill,Bob)Man(Bob)

are all childrenof Bill male?

? ⊨ ∀child.Man(Bill)

no idea sincewe do not knowall children of Bill

DL answersdon‘t know

PROLOG answersyes

if we assume that we know everything about Bill then all of his children are male

child(Bill,Bob)Man(Bob)

are all childrenof Bill male?

? ⊨ ∀child.Man(Bill)

no idea sincewe do not knowall children of Bill

DL answersdon‘t know

PROLOG answersyes

≤1 child.⊤(Bill) ? ⊨ ∀child.Man(Bill) yesnow we knoweverything aboutBill‘s children

Problems of Inference (1)

• Global (In)Consistency of the knowledge base

• Does the knowledge base make sense? KB ⊨ !?

• Class(in)consistency C ≡ ! ?• Must class C be empty?

• Class inclusion (Subsumption) C ⊑ D?

• Structuring the knowledge base

• Class equivalency C ≡ D?

• Are two classes the same?

• Class disjointness C ⊓ D = !?• Are two classes disjunctive?

• Class membership C(a)?• Is individual a contained in class C?

• Instance generation (Retrieval) „find all x with C(x)“

• Find all (known!) Individuals of class C.

Problems of Inference (2)

11 • Decidability: for each inference problem there always exists an algorithm that terminates in finite time

• DLs are fragments of FOL, therefore (in principle) FOL inference algorithms (Resolution, Tableaux) can be applied.

• But FOL algorithms do not always terminate!

• Problem: Find algorithms that always terminate!

• There might be no „naive“ solutions!

Description Logics and Inference

Decidability and DL

• FOL inference algorithms (Tableaux algorithm and Resolution) must be adapted for DLs

• (for the lecture we will restrict to ALC tableaux algorithm)

• Tableaux algorithm and resolution show unsatisfiability of a theory (knowledge base)

• Adaption of entailment problems to the detection of contradictions in the knowledge base, i.e. proof of the unsatisfiability of the knowledge base!

Reduction to Unsatisfiability (1)

• Class (in)consistency C ≡ !• iff KB⋃{C(a)} unsatisfiable (a new)

• Class inclusion (Subsumption) C ⊑ D

• iff KB⋃{(C⊓¬D)(a)} unsatisfiable (a new)

• Class equivalency C ≡ D

• Class equivalency C ≡ D• iff C ⊑ D and D ⊑ C

• Class disjointness C ⊓ D = !• iff KB⋃{(C⊓D)(a)} unsatisfiable (a new)

• Class membership C(a)

• iff KB⋃{¬C(a)} unsatisfiable

• Check class membership for all individuals

• but: efficiency...?

03 Tableaux Algorithm for ALCOpen HPI - Course: Semantic Web Technologies - Lecture 5: Knowledge Representations II

openhpi 5.2 - dl inference and reasoning

web technologies lecture

real world

open world assumption

open world assumption

open world assumption

closed world assumption

closed world assumption8

close world systems

Documents

openhpi 4.5 - foundations of logic

openhpi - workshop ec-tel graz 2014

openhpi 1.5 - the vision of the semantic web

openhpi 1.7 - applications in the web of data

openhpi 6.1 - ontological engineering

openhpi - parallel programming concepts - week 1

openhpi 2.2 - how to represent facts - rdf

openhpi 1.3 - the importance of meaning

openhpi 4.7 - resolution

openhpi 4.4 - ontology types

openhpi 4.1 - ontologies as central concept in philosophy

openhpi 1.4 - the limits of the web (continued)

openhpi 6.3 - ontology design 101

openhpi 5.9 - rules and the semantic web

openhpi 1.6 - the vision of the semantic web - part 2

openhpi 6.2 - ontology design

openhpi - parallel programming concepts - week 4

openhpi 2.6 - how to model classes and relations - rdfs

openhpi 1.1 - the web and how we got there

openhpi 5.7 - web ontology language (part 4)