1 Model Theory and Calculus for DL-Lite Evgeny Kharlamov Diego Calvanese, Werner Nutt Free University of Bozen-Bolzano Dresden University of Technology.

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
Validating the Evaluation of Adaptive Systems by User Profile Simulation Javier Bravo and Alvaro Ortigosa {javier.bravo, Universidad.
2005conjunctive-ii1 Query languages II: equivalence & containment (Motivation: rewriting queries using views)  conjunctive queries – CQ’s  Extensions.
Information Integration Using Logical Views Jeffrey D. Ullman.
AR for Horn clause logic Introducing: Unification.
Fast Algorithms For Hierarchical Range Histogram Constructions
1 541: Relational Calculus. 2 Relational Calculus  Comes in two flavours: Tuple relational calculus (TRC) and Domain relational calculus (DRC).  Calculus.
Evolution in OWL 2 QL & OWL 2 EL Ontologies Dmitriy Zheleznyakov 28 th of January, 2014, Oslo.
1 A Description Logic with Concrete Domains CS848 presentation Presenter: Yongjuan Zou.
Department of Software and Computing Systems Physical Modeling of Data Warehouses using UML Sergio Luján-Mora Juan Trujillo DOLAP 2004.
IJCAI Reasoning with Inconsistent Ontologies Zhisheng Huang, Frank van Harmelen, and Annette ten Teije Vrije University Amsterdam.
The International RuleML Symposium on Rule Interchange and Applications Local and Distributed Defeasible Reasoning in Multi-Context Systems Antonis Bikakis,
DL-LITE: TRACTABLE DESCRIPTION LOGICS FOR ONTOLOGIES AUTHORS: DIEGO CALVANESE, GIUSEPPE DE GIACOMO, DOMENICO LEMBO, MAURIZIO LENZERINI, RICCARDO ROSATI.
PR-OWL: A Framework for Probabilistic Ontologies by Paulo C. G. COSTA, Kathryn B. LASKEY George Mason University presented by Thomas Packer 1PR-OWL.
Logic in general Logics are formal languages for representing information such that conclusions can be drawn Syntax defines the sentences in the language.
Default and Cooperative Reasoning in Multi-Agent Systems Chiaki Sakama Wakayama University, Japan Programming Multi-Agent Systems based on Logic Dagstuhl.
Dynamic Ontologies on the Web Jeff Heflin, James Hendler.
China05 1 Reasoning with Inconsistent Ontologies 非协调本体的推理 Zhisheng Huang, Frank van Harmelen, and Annette ten Teije Vrije.
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment Natalya Fridman Noy and Mark A. Musen.
How can Computer Science contribute to Research Publishing?
Semantic Web The Story So Far Ian Horrocks Oxford University Computing Laboratory.
Schemas as Toposes Steven Vickers Department of Pure Mathematics Open University Z schemas – specification1st order theories – logic geometric theories.
2005lav-iii1 The Infomaster system & the inverse rules algorithm  The InfoMaster system  The inverse rules algorithm  A side trip – equivalence & containment.
Efficient Methods for Solving Finite Satisfiability Problems in UML Class Diagrams Mira Balaban and Azzam Maraee.
Rutgers University Relational Calculus 198:541 Rutgers University.
ANSWERING CONTROLLED NATURAL LANGUAGE QUERIES USING ANSWER SET PROGRAMMING Syeed Ibn Faiz.
Cooperative Query Answering Based on a talk by Erick Martinez.
Integrating bio-ontologies with a workflow/Petri Net model to qualitatively represent and simulate biological systems Mor Peleg, Irene Gbashvili, and Russ.
Efficient Reasoning on Finite Satisfiability in UML Class Diagrams
2007/9/15AIAI '07 (Aix-en-Provence, France)1 Reconsideration of Circumscriptive Induction with Pointwise Circumscription Koji Iwanuma 1 Katsumi Inoue 2.
Notes for Chapter 12 Logic Programming The AI War Basic Concepts of Logic Programming Prolog Review questions.
A Multi-Agent Systems Based Conceptual Ship Design Decision Support System The Ship Stability Research Centre Department of Naval Architecture and Marine.
An Introduction to Description Logics. What Are Description Logics? A family of logic based Knowledge Representation formalisms –Descendants of semantic.
Marking Scheme ISM ISM Top-up. Project Contents Abstract, – A one page summary (max. 400 words) of the Intent, work undertaken. Introduction, – An overview.
Using the TBox to Optimise SPARQL Queries Birte Glimm Yevgeny Kazakov Ilianna Kollia and Giorgos Stamou CS 848 Paper Critique Vishnu Prathish.
Nature of Science Notes. Scientific Method 1.Problem : the question you want to answer. 2.Hypothesis : an educated guess to the problem, or an if- then.
Agenda Intro: Information management in Biology Information management engineering Formats and standards XML MAGE example Perspectives: the Semantic Web.
The Evolution of ICT-Based Learning Environments: Which Perspectives for School of the Future? Reporter: Lee Chun-Yi Advisor: Chen Ming-Puu Bottino, R.
Updating ABoxes in DL-Lite D. Calvanese, E. Kharlamov, W. Nutt, D. Zheleznyakov Free University of Bozen-Bolzano AMW 2010, May 2010.
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
7th November 2005SWPW, Galway, Ireland. SWPW Panel - Policies & Ontologies - Karl Quinn, Knowledge & Data Engineering Group, Trinity College Dublin, Ireland.
Logical Agents Chapter 7. Outline Knowledge-based agents Logic in general Propositional (Boolean) logic Equivalence, validity, satisfiability.
Shridhar Bhalerao CMSC 601 Finding Implicit Relations in the Semantic Web.
© 2010 IBM Corporation Business-Intelligence Queries with Order Dependencies in DB2 Jarek Szlichta University of Toronto and IBM CAS Joint work with Parke.
Website: Answering Continuous Queries Using Views Over Data Streams Alasdair J G Gray Werner.
Artificial Intelligence “Introduction to Formal Logic” Jennifer J. Burg Department of Mathematics and Computer Science.
Conclusions Presenter: Manolis Koubarakis Extended Semantic Web Conference 2012.
Statistics Netherlands CRISTAL, a Model for Data and Metadata Statistics Netherlands Erik van Bracht METIS Feb 2004.
1 Reasoning with Infinite stable models Piero A. Bonatti presented by Axel Polleres (IJCAI 2001,
1 Developing an Ontology of Ontologies for OOR Preparation for Ontology Summit 2008 Panel Discussion April 10, 2008 Barry Smith and Michael Gruninger.
University of Maryland Scaling Heterogeneous Information Access for Wide area Environments Michael Franklin and Louiqa Raschid.
Database Management Systems, R. Ramakrishnan1 Relational Calculus Chapter 4, Part B.
Hitzler ● OWL1.1 Rules ● DedSys Saarbrücken ● March 2008 AIFB ReaSem Slide 1 OWL 1.1 Rules Markus Krötzsch Sebastian Rudolph Pascal Hitzler AIFB, University.
Presented by Kyumars Sheykh Esmaili Description Logics for Data Bases (DLHB,Chapter 16) Semantic Web Seminar.
1 Simulating Reachability using First-Order Logic with Applications to Verification of Linked Data Structures Tal Lev-Ami 1, Neil Immerman 2, Tom Reps.
CSE-291: Ontologies in Data Integration Department of Computer Science & Engineering University of California, San Diego CSE-291: Ontologies in Data Integration.
1 Developing an Ontology of Ontologies for OOR Ontology Summit 2008 April 28-29, 2008 Michael Gruninger and Pat Hayes.
The TONES Consortium: Free University of Bozen-Bolzano Università di Roma “La Sapienza” The University of Manchester Technische Universität Dresden Hamburg.
Co-funded by the European Union under FP7-ICT Co-ordinated by aparsen.eu #APARSEN Provenance Interoperability and Reasoning Yannis Tzitzikas Assistant.
1 Representing and Reasoning on XML Documents: A Description Logic Approach D. Calvanese, G. D. Giacomo, M. Lenzerini Presented by Daisy Yutao Guo University.
<Student’s name>
Outline Basic English Information Inquiry Reading Technical Materials
Information mediators
Introduction to Prolog
Small is Again Beautiful in Description Logics
Reasoning Rationally.
Finite-Trace Linear Temporal Logic: Coinductive Completeness
Ontologies and Databases
ONTOMERGE Ontology translations by merging ontologies Paper: Ontology Translation on the Semantic Web by Dejing Dou, Drew McDermott and Peishen Qi 2003.
Presentation transcript:

1 Model Theory and Calculus for DL-Lite Evgeny Kharlamov Diego Calvanese, Werner Nutt Free University of Bozen-Bolzano Dresden University of Technology October 2006

2 Motivation

3 Problem: Data Integration Information Sources User Interface Query: q

4 Ontology Information Sources Solution: q Data Integration System Motivation

5 Solution: Ontology Information Sources q Motivation Data Warehouse

6 Motivation Pre-process (data from the sources): Incompleteness of the sources wrt the ontology 23Golf 7 …  VW is a Car VW Car … 7Golf... …

7 Solution: Ontology Information Sources q Motivation Data Warehouse DL-Lite Size??

8 Ontology Information Sources Solution: q Data Integration System Motivation q 1,..., q n

9 Motivation Evaluation of Mediators:  Response time  Correctness of answers q L1L1 q 1,..., q n L3L3 L2L2

10 Motivation Evaluation of Mediators:  Response time ~ LogSpace  Correctness of answers ~ correct q DL-Lite q 1,..., q n UCQs CQs

11 Ontology Information Sources QuOnto: q Data Integration System QuOnto q 1,..., q n CQ DL-Lite UCQ

12 Aim of this Thesis Better understanding of properties of DL-Lite Relationship: ontology - size of the Warehouse Relationship: ontology - query answering  Response time  Correctness of answers

13 DL-Lite

14 DL-Lite Vocabulary (of the ontology):  Classes: Car Elements that participate in a relation: A = {x | there is y s.t. Has_engine(x,y)} B = {y | there is x s.t. Has_engine(x,y)}  Relations: Has_engine

15 DL-Lite Ontology:  Inclusion dependency: VW IsA Car VW IsA Has_engine  Disjointness: VW IsA ¬ Mercedes Has_engine IsA ¬ Animal

16 DL-Lite Ontology:  Functional dependency func (Has_id) func (Has_engine)

17 DL-Lite Data (sources): Car(vw_golf) Has_engine(vw_golf, td)

18 Universal Models

19 Universal Models VW  Car Mercedes  Car VW  ¬Mercedes Car  ¬Animal func (Has_id) func (Has_engine)...

20 Universal Models Properties:  If there is a completion  UM  If there is a UM  there is a class of Ums  Chase of a DB with an Ontology is a UM

21 Universal Models Infinite universal models:  Bob is a Person  Every person has a father  Every father is a person  No one can be an ancestor of him/herself BobPerson Bill Father Person Sam Father Person …

22 Chase of Polynomial Size weakly-acyclic ontology VW  Car Mercedes  Car VW  ¬Mercedes Car  ¬Animal func (Has_id) func (Has_engine)... pol(n+m) m n

23 Chase of polynomial size: Chase as Data Warehouse Information Sources q User Interface weakly-acyclic Ontology =

24 Results  Introduced the notion of UM  Shown that any chase is a UM  Proposed weakly-acyclic ontologies for which chase is finite and of polynomial size

25 Deduction as Query Answering

26 Deduction as Query Answering Information Sources Query Ontology T(Information Sources) T(Query) T(Ontology) Calculus All Answers Derivation Extended Horn Logic (EHL)

27 Extended Horn Logic HL: X Y Z bro(X,Z):- bro(X,Y), bro(Y,Z) EHL: X Y Z bro(bob,Z):- bro(X,Y), bro(Y,bob)

28 Calculus Extends Resolution-based calculus with Extended resolution Query homorphisms

29 Results  Introduced EHL  Defined reduction from DL-Lite to EHL  Introduced a calculus for EHL  Shown soundness and completeness of the calculus wrt query answering  query answering in DL-Lite is reducible to reasoning in EHL

30 Conclusion We investigated properties of DL-Lite logic:  Model theory: Universal models other properties  Proof Theory Calculus as a tool for query answering

31 Further work  Extend query language (in QuOnto)  Find good algorithms and optimisations

32 Thank you