Charting the Potential of Description Logic for the Generation of Referring Expression SELLC, Guangzhou, Dec. 2010 Yuan Ren, Kees van Deemter and Jeff.

Slides:



Advertisements
Similar presentations
Artificial Intelligence
Advertisements

Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002.
Charting the Potential of Description Logic for the Generation of Referring Expression SELLC, Guangzhou, Dec Yuan Ren, Kees van Deemter and Jeff.
Ontologies and Databases Ian Horrocks Information Systems Group Oxford University Computing Laboratory.
Query Answering based on Standard and Extended Modal Logic 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.
Knowledge Representation and Reasoning using Description Logic Presenter Shamima Mithun.
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
What if...? Chris Mellish, Jeff Pan, Kees van Deemter, Yuan Ren, Artemis Parvizi, University of Aberdeen Robert Stevens, Caroline Jay, Markel Vigo, University.
Knowledge & Reasoning Logical Reasoning: to have a computer automatically perform deduction or prove theorems Knowledge Representations: modern ways of.
Ontological Logic Programming by Murat Sensoy, Geeth de Mel, Wamberto Vasconcelos and Timothy J. Norman Computing Science, University of Aberdeen, UK 1.
Artificial Intelligence Inference in first-order logic Fall 2008 professor: Luigi Ceccaroni.
1 A Description Logic with Concrete Domains CS848 presentation Presenter: Yongjuan Zou.
Of 27 lecture 7: owl - introduction. of 27 ece 627, winter ‘132 OWL a glimpse OWL – Web Ontology Language describes classes, properties and relations.
High-level Data Access Based on Query Rewritings Ekaterina Stepalina Higher School of Economics.
INTRODUCTION TO ARTIFICIAL INTELLIGENCE Massimo Poesio LECTURE 4: Semantic Networks and Description Logics.
Combining the strengths of UMIST and The Victoria University of Manchester A Tableaux Decision Procedure for SHOIQ Ian Horrocks and Ulrike Sattler University.
Inference in Probabilistic Ontologies with Attributive Concept Descriptions and Nominals Rodrigo Bellizia Polastro and Fabio Gagliardi Cozman.
Description Logics. Outline Knowledge Representation Knowledge Representation Ontology Language Ontology Language Description Logics Description Logics.
Chapter 7 Reasoning about Knowledge by Neha Saxena Id: 13 CS 267.
DL systems DL and the Web Ilie Savga
Part 6: Description Logics. Languages for Ontologies In early days of Artificial Intelligence, ontologies were represented resorting to non-logic-based.
ANHAI DOAN ALON HALEVY ZACHARY IVES Chapter 12: Ontologies and Knowledge Representation PRINCIPLES OF DATA INTEGRATION.
Reasoning the FMA Ontologies with TrOWL Jeff Z. Pan, Yuan Ren, Nophadol Jekjantuk, and Jhonatan Garcia University of Aberdeen, UK ORE2013.
An Introduction to Description Logics. What Are Description Logics? A family of logic based Knowledge Representation formalisms –Descendants of semantic.
Applying Belief Change to Ontology Evolution PhD Student Computer Science Department University of Crete Giorgos Flouris Research Assistant.
A Logic for Decidable Reasoning about Services Yilan Gu Dept. of Computer Science University of Toronto Mikhail Soutchanski Dept. of Computer Science Ryerson.
Ming Fang 6/12/2009. Outlines  Classical logics  Introduction to DL  Syntax of DL  Semantics of DL  KR in DL  Reasoning in DL  Applications.
Query Answering Based on the Modal Correspondence Theory Evgeny Zolin University of Manchester Manchester, UK
Building an Ontology of Semantic Web Techniques Utilizing RDF Schema and OWL 2.0 in Protégé 4.0 Presented by: Naveed Javed Nimat Umar Syed.
DECIDABILITY OF PRESBURGER ARITHMETIC USING FINITE AUTOMATA Presented by : Shubha Jain Reference : Paper by Alexandre Boudet and Hubert Comon.
Chapter 1, Part II: Predicate Logic With Question/Answer Animations.
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.
1 Knowledge Representation. 2 Definitions Knowledge Base Knowledge Base A set of representations of facts about the world. A set of representations of.
An Introduction to Description Logics (chapter 2 of DLHB)
Semantic web course – Computer Engineering Department – Sharif Univ. of Technology – Fall Description Logics: Logic foundation of Semantic Web Semantic.
Chapter 1, Part II: Predicate Logic With Question/Answer Animations.
LDK R Logics for Data and Knowledge Representation Description Logics (ALC)
More on Description Logic(s) Frederick Maier. Note Added 10/27/03 So, there are a few errors that will be obvious to some: So, there are a few errors.
Predicate Logic for software Engineers Sagnik Bhattacharya Siddharth Dalal.
DL Overview Second Pass Ming Fang 06/19/2009. Outlines  Description Languages  Knowledge Representation in DL  Logical Inference in DL.
Artificial Intelligence “Introduction to Formal Logic” Jennifer J. Burg Department of Mathematics and Computer Science.
LDK R Logics for Data and Knowledge Representation ClassL (Propositional Description Logic with Individuals) 1.
Description Logics Dr. Alexandra I. Cristea. Description Logics Description Logics allow formal concept definitions that can be reasoned about to be expressed.
ece 627 intelligent web: ontology and beyond
Knowledge Repn. & Reasoning Lec. #5: First-Order Logic UIUC CS 498: Section EA Professor: Eyal Amir Fall Semester 2004.
Reasoning with Propositional Logic automated processing of a simple knowledge base CD.
Knowledge Representation and Reasoning University "Politehnica" of Bucharest Department of Computer Science Fall 2011 Adina Magda Florea
Of 29 lecture 15: description logic - introduction.
LDK R Logics for Data and Knowledge Representation Description Logics: family of languages.
Ontology Technology applied to Catalogues Paul Kopp.
CS621 : Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 16 Description Logic.
Knowledge Representation Lecture 2 out of 5. Last Week Intelligence needs knowledge We need to represent this knowledge in a way a computer can process.
1 Representing and Reasoning on XML Documents: A Description Logic Approach D. Calvanese, G. D. Giacomo, M. Lenzerini Presented by Daisy Yutao Guo University.
OWL (Ontology Web Language and Applications) Maw-Sheng Horng Department of Mathematics and Information Education National Taipei University of Education.
Knowledge Representation Part II Description Logic & Introduction to Protégé Jan Pettersen Nytun.
ece 720 intelligent web: ontology and beyond
The Foundations: Logic and Proofs
Ontology.
ece 720 intelligent web: ontology and beyond
Updating TBoxes in DL-Lite
Local Closed World Reasoning in the Semantic Web
Ontologies and Databases
Description Logics.
Predicates and Quantifiers
Logics for Data and Knowledge Representation
A Tutorial Summary of Description Logic and Hybrid Rules
CIS Monthly Seminar – Software Engineering and Knowledge Management IS Enterprise Modeling Ontologies Presenter : Dr. S. Vasanthapriyan Senior Lecturer.
Presentation transcript:

Charting the Potential of Description Logic for the Generation of Referring Expression SELLC, Guangzhou, Dec Yuan Ren, Kees van Deemter and Jeff Z. Pan Department of Computing Science University of Aberdeen, UK

Background Generation of Referring Expressions (GRE) algorithms identify a target referent: Express info known to be true of target false of all else But how does GRE model knowledge?

Knowledge Representation in classic GRE Sets of pairs, e.g. – These are atomic facts. Can’t say – All poodles are grey – Kees is Dutch or Belgian – If x is part of y and y is part of z then x is part of z

Modern KR can do all this (and more) KL-One and semantic nets Modern descendants: Conceptual Graphs and Description Logic – Represent complex knowledge – Perform efficient automatic deduction Why not use modern KR for GRE? This talk: Description Logic (DL) – DL/OWL is now the language of the semantic web

Advantages A richer model of reference – New targets become identifiable Re-use – existing algorithms – existing ontologies – deduction for proving uniqueness Info represented succinctly, e.g. – a part of b, b part of c, c part of d, … – For all x,y,z: if x part of y and y part of z then x part of z

Remainder of this talk Introduction to DL Areces et al. on DL and GRE Extending the expressivity Extending the algorithm Caveat: few empirical claims This talk is about what’s possible in GRE

Description Logics? A family of logic-based KR formalisms Describe domain in terms of concepts (classes), roles (relations) and individuals Smallest propositionally closed DL is ALC (equivalent to K (m) ) – Concepts constructed using ∪, ∩, ∀, ∃ E.g., domain elements that have a child who is a Doctor:

DL Knowledge Base A TBox is a set of “schema” axioms (sentences), e.g.: { Dog ⊂ Animal, Dog => ⊂  Woman hasFather ◦ hasBrother ⊂ hasUncle, feed ◦ love ⊂ feed } – i.e., a background theory An ABox is a set of “ data ” axioms (ground facts), e.g.: {d1:Dog, w1:Woman, c1:Animal, (w1,d1):feed, (d1,c1):love } An NBox (Negation as Failure Box) is a set of “complete” concepts and properties e.g.: { Dog, Animal }

GRE example Uniquely identifying an object in context. – d1: the Dog that loves some Cat – w2: the Woman that feeds some Dog that loves no Cat w1 w2 Woman d1 d2 Dog c1 c2 Cat feed love

From the DL point of view ABox assertion axioms – w1:Woman w2:Woman – d1:Dog, d2:Dog – c1:Cat, c2:Cat – (w1,d1):feed (w2,d2):feed (w2,d1):feed – (d1,c1):love w1 w2 Woman d1 d2 Dog c1 c2 Cat feed love

DL for GRE: the story so far Areces et al. (2008) re-interpret GRE as a problem of computing ALC formulas. An algorithm to compute distinguishing REs (if one exists) for all objects: – Generate-Test strategy – Start from atomic concept names, then extend with (negative) existential quantifier.

DL for GRE example Referring Expression as a DL formula w1 w2 Woman d1 d2 Dog c1 c2 Cat feed love

Logical foundations of GRE DL forces us to make these explicit, allowing subtle distinctions GRE relies on a Unique Name Assumption (UNA) – If Dog={d1,d2} but d1=d2 then Dog refers uniquely – In DL, we can use UNA, or write d1=/=d2, etc. GRE relies on a closed world assumption (CWA) – In the earlier example, there are no more than two dogs, no more than two women, etc. – Without this, we would never be sure of uniqueness – DL: we can localise CWA, using an NBox

ABox Not Enough Tbox: – Woman ⊂  Dog – Dog ⊂  Cat – Cat ⊂  Woman – T ⊂ Dog ∪ Woman ∪ Cat – feed ◦ love ⊂ feed Nbox: – { Dog,Woman,Cat,love,feed } w1 w2 Woman d1 d2 Dog c1 c2 Cat feed love

Additional Quantifiers Now Areces et al. cannot identify any of the 6 objects Additional quantifiers make them referable – c1: The cat which is fed by at least 2 women – w1: The woman feeding only those fed by at least 2 women – w2: The woman who feeds all the dogs w1 w2 Woman d1 d2 Dog c1 c2 Cat feed love

Representing Quantifiers in Ontologies We can use Generalised quantifiers, e.g., “only”, “five”, “at least two” Example: English: “The woman who loves at least two dogs” Set theory + GQ: {y  Woman: ≥2 (Dog,{z: Love(y,z)})} Literally, this says: “Women y such that there are at least two Dogs in the set of things that are loved by y” But which quantifiers exactly?

Representing Quantifiers with Ontologies Quantifiers of Type 1, e.g., n = 1: the existential quantifier; Quantifiers of Type 7, e.g., n = 0: the only quantifier; Quantifiers of Type 6, e.g., n = 0: the all quantifier; Example: N2: Dog R: Love

Generating SROIQ-enabled REs GROWL: a GRE algorithm using OWL-2 – Generate-and-test strategy – Using DL reasoning – Generating increasingly complex descriptions Complexity measured by the structure of the expressions – 1 complexity of (negated) atomic concept is 1 – 2 complexity of conjunction (disjunction) is the maximal complexity of conjuncts (disjuncts) +1 – 4 complexity of existential (universal) restriction is the complexity of filler + 1

Generating SROIQ-enabled REs GROWL: a GRE algorithm using OWL-2 – Starting from the basic terms, such as Names (e.g. Dog, feed) Inverse of named roles (such as feed - ) Negation of another basic term (e.g.,  Dog,  feed,  feed - ) – For each existing term / description, create new description by Extending with conjunction/disjunction Extending with existential/universal restriction Extending with cardinality restriction – Until no new non-empty description can be created

Generating SROIQ-enabled REs Deciding whether a new description should be accepted – Accept if no existing description has same extension – But still non-empty Using standard reasoning services – Concept subsumption checking – Concept satisfiability checking

Example Revisited Starting from basic terms – D={Woman, Dog, Cat,  Woman,  Dog,  Cat, feed, feed -,  feed - etc.} – Ins(Woman) = {w1,w2} – Ins(Dog) = {d1,d2} – Ins(Cat) = {c1,c2} – Ins(  Woman) = {c1,c2,d1,d2} – … – Ins(feed) = {(w1,d1),(w1,c1), (w2,d1), (w2,c1),(w2,d2),(w2,c2)} – Ins(feed - )= {(d1,w1),(c1,w1), (d1,w2), (c1,w2),(d2,w2),(c2,w2)} – Ins(  feed - ) = {(d1,d2),(d1,c1),(d1,c2),(d1,d1),(d2,d1),(d2,d2),(d2,c1),(d2,c2),(d2,w1) (c1,c1),(c1,c2),(c1,d1),(c1,d2),(c2,c2),(c2,c1),(c2,d1) (c2,d2),(c2,w1),(w1,w1),(w1,w2),(w1,d1),(w1,d2) (w1,c1),(w1,c2 ),(w2,w1),(w2,w2),(w2,d1),(w2,d2) (w2,c1),(w2,c2),} w1 w2 Woman d1 d2 Dog c1 c2 Cat feed love

How to Refer to c1? Extending Woman with maximal Cardinality restriction <=0(  feed - ).Woman (things that are fed by all women) – Ins(<=0(  feed - ).Woman) = {c1,d1}, should be accepted as a candidate description because its extension is not covered by any existing description – D becomes {Woman, Dog, Cat,  Woman,  Dog,  Cat, <=0(  feed - ). Woman, etc.} Extending <=0(  feed - ).Woman with conjunction – Ins(Cat & <=0(  feed - ).Woman) = {c1}, c1 identified! w1 w2 Woman d1 d2 Dog c1 c2 Cat feed love

All objects become referable w1: the woman that does not feed all cats; w2: the woman that feeds all cats; d1: the dog that is fed by all women; d2: the dog that is not fed by all women; c1: the cat that is fed by all women; c2: the cat that is not fed by all women w1 w2 Woman d1 d2 Dog c1 c2 Cat feed love

Conclusion We advocate using modern KR – background knowledge should be considered – expressing complex as well as atomic info – reusing reasoning algorithms

Conclusion Specific contributions: – Using DL reasoning to infer implicit knowledge (i.e., computing non-asserted information) – Generating REs taking into account incomplete knowledge – Extending the expressivity of GRE to OWL-2 (i.e., using new quantifiers, such as ≥2feed.Dog) – Introducing Nbox for a partially closed world Open issues: – How useful are the newly generated REs? – How to choose the “best” RE?