AR&A in Temporal & Spatial Reasoning SARA 2000 Tony Cohn (University of Leeds) chair Claudio Bettini (Università degli Studi di Milano) Ben Kuipers (University.

Slides:

Advertisements

Similar presentations

Three-Step Database Design

Advertisements

Tony Cohn, The University of Leeds 2007 NESC :35 Qualitative representations of the geospatial world Tony Cohn School of Computing The University.

Ordnance Survey & Ontologies Dr John Goodwin. Ordnance Survey and Linked Data.

Dr. Leo Obrst MITRE Information Semantics Group Information Discovery & Understanding Dept Command & Control Center Challenges of Spatial Semantics: Nearness,

Foundations of Constraint Processing Temporal Constraints Networks 1Topic Foundations of Constraint Processing CSCE421/821, Spring

Constraint Propagation Algorithms for Temporal Reasoning Marc Vilain, Henry Kautz (AAAI 1986) Presentation by Lin XU March 4, 2002.

Sotiris Batsakis, Euripides G.M. Petrakis Technical University Of Crete Intelligent Systems Laboratory.

Representing Contextual Aspects of Data WP2 - Andreas Harth 1st year review Luxembourg, December 2011.

Some computational aspects of geoinformatics Mike Worboys NCGIA, University of Maine, USA.

A More Expressive 3D Region Connection Calculus Chaman Sabharwal, Jennifer Leopold, & Nate Eloe.

1 A Description Logic with Concrete Domains CS848 presentation Presenter: Yongjuan Zou.

DL-LITE: TRACTABLE DESCRIPTION LOGICS FOR ONTOLOGIES AUTHORS: DIEGO CALVANESE, GIUSEPPE DE GIACOMO, DOMENICO LEMBO, MAURIZIO LENZERINI, RICCARDO ROSATI.

Object-Oriented Analysis and Design: Object Modeling – Class Diagrams

CS 599 – Spatial and Temporal Databases Realm based Spatial data types: The Rose Algebra Ralf Hartmut Guting Markus Schneider.

CHOSUN UNIV. The Study on the Semantic Image Retrieval Using the Cognitive Spatial Relationships in the Semantic Web Hyunjang Kong,Myunggwun.

Investigating a bottom-up approach for extracting domain ontologies from urban databases Christophe Chaidron 1, Roland Billen 1 & Jacques Teller 2 1 University.

The Vuel Concept: Towards a new way to manage Multiple Representations in Spatial Databases ISPRS / ICA Workshop Multi-Scale Representations of Spatial.

Geog 458: Map Sources and Errors January Representing Geography.

Qualitative Spatial Reasoning Anthony G Cohn Division of AI School of Computer Studies The University of Leeds

Spatio-Temporal Databases

Spatio-Temporal Databases. Outline Spatial Databases Temporal Databases Spatio-temporal Databases Multimedia Databases …..

Peter BogaertSeBGIS 2005 The Double-Cross and the Generalization Concept as a Basis for Representing and Comparing Shapes of Polylines Presentation: Peter.

Geographic Information Systems

Münster GI-Days 2004 – 1-2 july – Germany 1 Description, Definition and Proof of a Qualitative State Change of Moving Objects along a Road Network Peter.

Fast Texture Synthesis Tree-structure Vector Quantization Li-Yi WeiMarc Levoy Stanford University.

Qualitative Spatial- Temporal Reasoning Jason J. Li Advanced Topics in A.I. The Australian National University Jason J. Li Advanced Topics in A.I. The.

CHOROS: IMPROVING THE PERFORMANCE OF QUALITATIVE SPATIAL REASONING IN OWL Nikolaos Mainas, Euripides G.M. Petrakis Technical University Of Crete (TUC),

IB Math Studies – Topic 3. IB Course Guide Description.

Background in geospatial data modeling Presenter: Manolis Koubarakis Extended Semantic Web Conference 2012.

Georgios Christodoulou, Euripides G.M. Petrakis, and Sotirios Batsakis Department of Electronic and Computer Engineering, Technical University of Crete.

U.S. Department of the Interior U.S. Geological Survey Geospatial Semantic Vocabulary for The National Map Dalia Varanka, Lynn Usery, and David Mattli.

A Grammar-based Entity Representation Framework for Data Cleaning Authors: Arvind Arasu Raghav Kaushik Presented by Rashmi Havaldar.

GIS 1110 Designing Geodatabases. Representation Q. How will we model our real world data? A. Typically: Features Continuous Surfaces and Imagery Map Graphics.

Geometric Conceptual Spaces Ben Adams GEOG 288MR Spring 2008.

GeoUML a conceptual data model for geographical data conformant to ISO TC 211 Main GeoUML constructs Alberto BelussiNovembre 2004.

Applied Cartography and Introduction to GIS GEOG 2017 EL Lecture-2 Chapters 3 and 4.

School of Computing FACULTY OF ENGINEERING Developing a methodology for building small scale domain ontologies: HISO case study Ilaria Corda PhD student.

8. Geographic Data Modeling. Outline Definitions Data models / modeling GIS data models – Topology.

Spatial Concepts and Data Models Reading: Shekhar & Chawla Chapter 2 November 22, 2005.

1 Data models Vector data model Raster data model.

URBDP 422 Urban and Regional Geo-Spatial Analysis Lecture 2: Spatial Data Models and Structures Lab Exercise 2: Topology January 9, 2014.

University of L’Aquila, Department of Electrical and Information Engineering

Methodology - Conceptual Database Design

Lighting affects appearance. How do we represent light? (1) Ideal distant point source: - No cast shadows - Light distant - Three parameters - Example:

1 CS599 Spatial & Temporal Database Spatial Data Mining: Progress and Challenges Survey Paper appeared in DMKD96 by Koperski, K., Adhikary, J. and Han,

1 Spatial Data Models and Structure. 2 Part 1: Basic Geographic Concepts Real world -> Digital Environment –GIS data represent a simplified view of physical.

Challenges in Mining Large Image Datasets Jelena Tešić, B.S. Manjunath University of California, Santa Barbara

NR 143 Study Overview: part 1 By Austin Troy University of Vermont Using GIS-- Introduction to GIS.

Peter BogaertGeos 2005 A Qualitative Trajectory Calculus and the Composition of its Relations Authors: Nico Van de Weghe, Bart Kuijpers, Peter Bogaert.

Probabilistic Latent Semantic Analysis as a Potential Method for Integrating Spatial Data Concepts R.A. Wadsworth 1, A.J. Comber 2, P.F. Fisher 2 1.Centre.

CS654: Digital Image Analysis Lecture 4: Basic relationship between Pixels.

Efficient OLAP Operations in Spatial Data Warehouses Dimitris Papadias, Panos Kalnis, Jun Zhang and Yufei Tao Department of Computer Science Hong Kong.

 In the previews parts we have seen some kind of segmentation method.  In this lecture we will see graph cut, which is a another segmentation method.

Set Theory Concepts Set – A collection of “elements” (objects, members) denoted by upper case letters A, B, etc. elements are lower case brackets are used.

What is GIS? “A powerful set of tools for collecting, storing, retrieving, transforming and displaying spatial data”

Morphology Morphology deals with form and structure Mathematical morphology is a tool for extracting image components useful in: –representation and description.

Introduction to GIS All materials by Austin Troy © 2003, except where noted Lecture 8: Site Selection and Suitability Analysis and Criterion- based mapping.

RESEARCH METHODS Lecture 7. HYPOTHESIS Background Once variables identified Establish the relationship through logical reasoning. Proposition. Proposition.

A Methodology for automatic retrieval of similarly shaped machinable components Mark Ascher - Dept of ECE.

Presentation to Networking Geospatial Information Technology for Interoperability and Spatial Ontology School of Computing University of Leeds Tony.

Business System Development

RESEARCH METHODS Lecture 7

Spatio-temporal Relational Constraint Calculi

Geographic Information Systems

Mean Shift Segmentation

Knowledge Representation

Propositional Calculus: Boolean Algebra and Simplification

Introduction to Geoinformatics: Topology

DATA MANAGEMENT IN GIS SPATIAL (GEO) DATA MODELS

Ying Dai Faculty of software and information science,

Presentation transcript:

AR&A in Temporal & Spatial Reasoning SARA 2000 Tony Cohn (University of Leeds) chair Claudio Bettini (Università degli Studi di Milano) Ben Kuipers (University of Texas at Austin) Ivan Ordonez (Ohio State University)

AR&A in Temporal and Spatial Reasoning. the role of AR&A in S&T reasoning the types of abstraction and approximations that are useful for S&T reasoning the differences in the use of abstraction and approximation in S&T reasoning wish list for work on AR&A in S&T...

Abstraction in Spatial Reasoning Why? Efficiency –eg: quad trees for very large spatial DBs Data integration –DBs may contain data at different scales HCI: –eg: Cartographic generalisation –eg: high level queries (incl. NL) Spatial planning (eg navigation) High level vision...

Kinds of spatial abstraction Regions rather than points (aggregation) granularity shifts (eg pixel size) dimension changing qualitative relations –relevant abstractions...

Qualitative Spatial Representations DC EC PO TPP NTPP EQ TPPi NTPPi l1l1 l2l2 l3l

Changing scale: Baarle-Nassau/ Baarle-Hertog (thanks to Barry Smith for the example)

Approximation Qualitative relations –(eg sector orientations) Regions, and regions with indeterminate boundaries –the “egg/yolk” calculus –X is crisper than Y...

Conceptal neighbourhoods & approximation Conceptual neighbourhoods give “next” relation Uncertainty of relation gives connected sub-graph –e.g. composition table entries

Finer grained representations can be more efficient Constraint satisfaction in CYCORD is NP complete –24 relation calculus is polynomial on base relations Similarly: tractable subsets of RCC8, RCC5,.. –Cf Buerkert & Nebel’s analysis of Allen

Reformulation RCC: 1st order theory  Zero order formulation 9-intersection+DEM (81+ relations)  CMB (5 polymorphic relations) spatial analogies: reformulating other domains as a spatial problem –eg: view database class integration as a spatial problem using egg/yolk theory global orientation  local orientation Vector  raster

Intuitionistic Encoding of RCC8: (Bennett 94) Motivated by problem of generating composition tables Zero order logic –“Propositional letters” denote (open) regions –logical connectives denote spatial operations e.g.  is sum e.g.  is P Spatial logic rather than logical theory of space

Represent RCC relation by two sets of constraints: “model constraints”“entailment constraints” DC (x,y)~x  y ~x  y EC (x,y) ~(x  y) ~x  y, ~x  y PO (x,y)--- ~x  y, ~x  y, y  x, ~x  y TPP (x,y)x  y ~x  y, ~x  y, y  x NTPP (x,y) ~x  y ~x  y, y  x EQ (x,y) x  y ~x  y Decidable, tractable representation

9-intersection+DEM DEM: when entry is ‘¬’, replace with dimension of intersection: 0,1,2 81+ region-region relations

 CMB (5 polymorphic relations) disjoint: x  y =  touch (a/a, l/l, l/a, p/a, p/l): x  y  b(x)  b(y) in: x  y  y overlap (a/a, l/l): dim(x)=dim(y)=dim(x  y)  x  y  y  x  y  x cross (l/l, l/a): dim(int(x))  int(y))=max(int(x)),int(y))  x  y  y  x  y  x EG: touch(L,A)  cross(L,b(A))   disjoint(f(L),A)  disjoint(t(L),A) L

Research issues Moving between abstraction levels –Qualitative/quantitative integration Choosing abstraction level Expressiveness/efficiency tradeoff Cognitive Evaluation Ambiguity...