Logics for Data and Knowledge Representation

Slides:



Advertisements
Similar presentations
Query Answering based on Standard and Extended Modal Logic Evgeny Zolin The University of Manchester
Advertisements

Modal Logic with Variable Modalities & its Applications to Querying Knowledge Bases Evgeny Zolin The University of Manchester
CS848: Topics in Databases: Foundations of Query Optimization Topics covered  Introduction to description logic: Single column QL  The ALC family of.
OWL - DL. DL System A knowledge base (KB) comprises two components, the TBox and the ABox The TBox introduces the terminology, i.e., the vocabulary of.
An Introduction to Description Logics
1 A Description Logic with Concrete Domains CS848 presentation Presenter: Yongjuan Zou.
LDK R Logics for Data and Knowledge Representation Modal Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
FiRE Fuzzy Reasoning Engine Nikolaos Simou National Technical University of Athens.
LDK R Logics for Data and Knowledge Representation Description Logics as query language.
An Introduction to Description Logics. What Are Description Logics? A family of logic based Knowledge Representation formalisms –Descendants of semantic.
Tableau Algorithm.
Ming Fang 6/12/2009. Outlines  Classical logics  Introduction to DL  Syntax of DL  Semantics of DL  KR in DL  Reasoning in DL  Applications.
Logics for Data and Knowledge Representation Exercises: Modeling Fausto Giunchiglia, Rui Zhang and Vincenzo Maltese.
LDK R Logics for Data and Knowledge Representation ClassL (part 3): Reasoning with an ABox 1.
Presented by:- Somya Gupta( ) Akshat Malu ( ) Swapnil Ghuge ( ) Franz Baader, Ian Horrocks, and Ulrike Sattler.
Topics in artificial intelligence 1/1 Dr hab. inż. Joanna Józefowska, prof. PP Reasoning and search techniques.
An Introduction to Description Logics (chapter 2 of DLHB)
Logics for Data and Knowledge Representation Exercises: DL.
LDK R Logics for Data and Knowledge Representation Modal Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
1 How to decide Query Containment under Constraints using a Description Logic Ian Horrocks, Ulrike Sattler, Sergio Tessaris, and Stephan Tobies presented.
LDK R Logics for Data and Knowledge Representation PL of Classes.
LDK R Logics for Data and Knowledge Representation Description Logics (ALC)
LDK R Logics for Data and Knowledge Representation Description Logics.
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
DL Overview Second Pass Ming Fang 06/19/2009. Outlines  Description Languages  Knowledge Representation in DL  Logical Inference in DL.
LDK R Logics for Data and Knowledge Representation ClassL (Propositional Description Logic with Individuals) 1.
LDK R Logics for Data and Knowledge Representation First Order Logics (FOL) Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto.
Logics for Data and Knowledge Representation Exercises: ClassL Fausto Giunchiglia, Rui Zhang and Vincenzo Maltese.
LDK R Logics for Data and Knowledge Representation ClassL (part 2): Reasoning with a TBox 1.
ece 627 intelligent web: ontology and beyond
LDK R Logics for Data and Knowledge Representation Propositional Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
Knowledge Repn. & Reasoning Lec #11+13: Frame Systems and Description Logics UIUC CS 498: Section EA Professor: Eyal Amir Fall Semester 2004.
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
1 Instance Store Database Support for Reasoning over Individuals S Bechhofer, I Horrocks, D Turi. Instance Store - Database Support for Reasoning over.
Knowledge Representation and Reasoning University "Politehnica" of Bucharest Department of Computer Science Fall 2011 Adina Magda Florea
Of 29 lecture 15: description logic - introduction.
Logics for Data and Knowledge Representation Exercises: DL.
LDK R Logics for Data and Knowledge Representation Description Logics: family of languages.
Ontology Technology applied to Catalogues Paul Kopp.
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
LDK R Logics for Data and Knowledge Representation Description Logics.
Logical Agents. Outline Knowledge-based agents Logic in general - models and entailment Propositional (Boolean) logic Equivalence, validity, satisfiability.
1 Representing and Reasoning on XML Documents: A Description Logic Approach D. Calvanese, G. D. Giacomo, M. Lenzerini Presented by Daisy Yutao Guo University.
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
ece 720 intelligent web: ontology and beyond
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Description logics (book, page 456)
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Logics for Data and Knowledge Representation
Presentation transcript:

Logics for Data and Knowledge Representation ClassL (part 3): Reasoning with an ABox

Outline World descriptions, assertions (ABox) Reasoning with the ABox Satisfiability/Consistency Instance checking Instance retrieval Concept realization Eliminating the ABox: Reducing to DPLL reasoning Closed and open world assumptions 2

ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS ABox, syntax ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS The second component of the knowledge base is the world description, the ABox. In an ABox one introduces individuals, by giving them names, and one asserts properties about them. We denote individual names as a, b, c,… An assertion with concept C is called concept assertion (or simply assertion) in the form: C(a), C(b), C(c), … Student(paul) Professor(fausto) To be read: paul belongs to (is in) Student fausto belongs to (is in) Professor

ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS ABox, semantics ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS We give semantics to ABoxes by extending interpretations to individual names An interpretation I: L  ∆I not only maps atomic concepts to sets, but in addition it maps each individual name a to an element aI ∈ ∆I, namely I(a) = aI ∈ ∆I Unique name assumption (UNA). We assume that distinct individual names denote distinct objects in the domain NOTE: ∆I denotes the domain of interpretation, a denotes the symbol used for the individual (the name), while aI is the actual individual of the domain.

ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS ABox, semantics ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS ∆I = {Fausto, Jack, Paul, Mary} We mean that: I(paul) ∈ I(Student) I(fausto) ∈ I(Professor) I(paul) = Paul I(fausto) = Fausto I (Professor) = {Fausto} I (Student) = {Jack, Paul, Mary} A Student(paul) Professor(fausto) 5

Individuals in the TBox ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Sometimes, it is convenient to allow individual names (also called nominals) not only in the ABox, but also in the TBox They are used to construct concepts. The most basic one is the “set” constructor, written: {a1,…,an} It defines a concept, without giving it a name, by enumerating its elements, with the semantics: {a1,…,an}I = {a1I,…,anI} Student ≡ {Jack, Paul, …, Mary} (the name is optional)

ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Reasoning Services ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Given an ABox A, we can reason (w.r.t. a TBox T) about the following: Satisfiability/Consistency: An ABox A is consistent with respect to T if there is an interpretation I which is a model of both A and T. Instance checking: checking whether an assertion C(a) is entailed by an ABox, i.e. checking whether a belongs to C. A ⊨ C(a) if every interpretation that satisfies A also satisfies C(a). Instance retrieval: given a concept C, retrieve all the instances a which satisfy C. Concept realization: given a set of concepts and an individual a find the most specific concept(s) C (w.r.t. subsumption ordering) such that A ⊨ C(a). 7

Reasoning via expansion of the ABox ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Reasoning services over an ABox w.r.t. an acyclic TBox can be reduced to checking an expanded ABox. We define the expansion of an ABox A with respect to T as the ABox A’ that is obtained from A by replacing each concept assertion C(a) with the assertion C’(a), with C’ the expansion of C with respect to T. A is consistent with respect to T iff its expansion A’ is consistent A is consistent iff A is satisfiable (*), i.e. non contradictory. (*) in PL, under the usual translation, with C(a) considered as a proposition different from C(b) 8

Reasoning via expansion ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS T Undergraduate ⊑  Teach Bachelor ≡ Student ⊓ Undergraduate Master ≡ Student ⊓  Undergraduate PhD ≡ Master ⊓ Research Assistant ≡ PhD ⊓ Teach A Master(Chen) PhD(Enzo) Assistant(Rui) The expansion of A w.r.t. T: Master(Chen) Student(Chen) Undergraduate(Chen) PhD(Enzo) Master(Enzo) Research(Enzo) Student(Enzo) Undergraduate(Enzo) Assistant(Rui) PhD(Rui) Teach(Rui) Master(Rui) Research(Rui) Student(Rui) Undergraduate(Rui) 9

ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Consistency ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Satisfiability/Consistency: An ABox A is consistent with respect to T if there is an interpretation I which is a model of both A and T. We simply say that A is consistent if it is consistent with respect to the empty TBox T A Parent ≡ Mother ⊔ Father Mother(Mary) Father ≡ Male ⊓ hasChild Father(Mary) Mother ≡ Female ⊓ hasChild Male ≡ Person ⊓  Female A is not consistent w.r.t. T: In fact, from the expansion of T we get that Mother and Father are disjoint. A is consistent (w.r.t. the empty TBox, no constraints)

ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Instance checking ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Instance checking: checking whether an assertion C(a) is entailed by an ABox, i.e. checking whether a belongs to C. A ⊨ C(a) if every interpretation that satisfies A also satisfies C(a). A ⊨ C(a) iff A ⋃ { C(a)} is inconsistent Consider T and A from the previous example. Is Phd(Rui) entailed? YES! The assertion is in the expansion of A.

ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Instance retrieval ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Instance retrieval: given a concept C, retrieve all the instances a which satisfy C. Implementation: A trivial, but not optimixed implementation consists in doing instance checking for all instances. Consider T and A from the previous example. Find all the instances of Undergraduate Looking at the expansion of A we have {Chen, Enzo, Rui}

ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Concept realization ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Concept realization: given a set of concepts and an individual a find the most specific concept(s) C (w.r.t. subsumption ordering) such that A ⊨ C(a). Dual problem of Instance retrieval Implementation: A trivial, but not optimixed implementation consists in doing instance checking for all concepts.

ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Concept realization ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Consider T and A from the previous example. Given the instance Rui, and the concept set {Student, PhD, Assistant} find the most specific concept C such that A ⊨ C(Rui) Rui is in the extension of all the concepts above. The following chain of subsumptions holds: Assistant ⊑ PhD ⊑ Student Therefore, the most specific concept is Assistant. T Undergraduate ⊑  Teach Bachelor ≡ Student ⊓ Undergraduate Master ≡ Student ⊓  Undergraduate PhD ≡ Master ⊓ Research Assistant ≡ PhD ⊓ Teach A Master(Chen) PhD(Enzo) Assistant(Rui) 14

ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS RECALL: ABoxes contain assertions of the form C(a). To eliminate the ABox we need to create a corresponding concept for each assertion, e.g. of the form C-a and a new axiom C-a ⊑ C. This causes an exponential blow up. A = {Master(Chen), Master(Paul), PhD(Enzo), PhD(Ronald), Assistant(Rui)} New concepts: Master-Chen, Master-Paul, PhD-Enzo, PhD-Ronald, Assistant-Rui Their interpretation is the singleton set containing the individual. T is extended with: {Master-Chen ⊑ Master, PhD-Enzo ⊑ PhD, Assistant-Rui ⊑ Assistant} 15

Eliminating the ABox: the algorithm ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS It is always possible to reduce reasoning problems w.r.t. an acyclic TBox T and an ABox A to problems without them. See for instance the algorithm for subsumption (all the others can be reduced to it). Input: a TBox T, an ABox A, the two concepts C and D Output: true if C ⊑ D holds or false otherwise boolean function IsSubsumedBy(T, A, C, D) { A’ = Expand(A, T); T’ = ConvertAssertions(T, A’); return IsSubsumedBy(T’, C, D) ; } ABox expansion ABox elimination DPLL Reasoning by eliminating T’. (see previous lesson) 16

Closed and Open world assumptions ABOX :: REASONING WITH AN ABOX :: ELIMINATING THE ABOX :: ASSUMPTIONS Closed world Assumption CWA (in Databases): anything which is not explicitly asserted is false Open World Assumption OWA (in Abox): anything which is not explicitly asserted (positive or negative) is unknown NOTE: a Database has/is one model. Query answering is model checking. NOTE: an ABox has a set of models. Query answering is satisfiability.