A Qualitative Approach to Vague Spatio-Thematic Query Processing

1. A Qualitative Approach to Vague Spatio-Thematic Query Processing Rolf Grütter and Thomas Scharrenbach Swiss Federal Institute for Forest, Snow and Landscape Research WSLZürcherstrasse 111, 8903 Birmensdorf, Switzerland {Rolf.Gruetter, Thomas.Scharrenbach}@wsl.ch

2. Finding Landscapes Close to Communities Albiskette-Reppischtal Aesch (ZH) R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

3. Finding Landscapes Close to Aesch (ZH) • Albiskette-Reppischtal is a landscape of national importance potentially close to Aesch (ZH) • <Landschaften "in der Nähe von" "Aesch (ZH)">Returns 1’350 matches • None of top 30 deal with a landscape of national importance • <Albiskette-Reppischtal>Returns 230 matches • None appear among top 30 of initial search Aesch (ZH) Albiskette-Reppischtal R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

4. Representing Spatial Knowledge Approach • Representing spatial knowledge • Region Connection Calculus (RCC) • Extending RCC such as to include “close to” • Implementing RCC in OWL DL and DL-safe rules • Knowledge Base (KB) • Rule Base (RB) • Processing (possibly vague) spatio-thematic queries R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

5. Administrative regions are social artifacts Mirror how a collective perceives spatial closeness on increasing scales of social organization Administrative regions are organized in partitions Representing Spatial Knowledge Partitions in RCC R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

6. 1. y = SUMi  Ixifor finite index set I 2. xixjDR(xi, xj) for i ≠ j; 3. regions(xi)i  I are named for all iI. Representing Spatial KnowledgeFamily of regions (xi)i  I is partition of region y: wrong correct x1 x1 x2 x3 x2 x3 x1 x3 x2 y x1 x2 R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

7. a1 More fine-grained c1 e2 d1 b1 b2 e1 d2 c2 f2 g2 f3 g1 g4 f1 f4 g3 Representing Spatial KnowledgePartial Order on Typed Partitions in RCC A • Distinguish partitions by typed elements • Example: Community(xi) says that xi is of type Community • Different scales by partial order on partitions • reflexive, transitive and antisymmetric. • Example: Community(xi)i  IDistrict(yj)j  J B C D E F G R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

8. Representing Spatial KnowledgeMinimal Partial Order on Typed Partitions C • A minimal partial order (m.p.o.) in RCC links to partial order in conceptualization: If C(xi)i  I (wk)k  KD(yj)j  J , then (wk)k  K must by typed • m.p.o. on typed partitions is intransitive. • Example: • Conceptualization provides administrative types District and Commune. • Partial order comprising non-typed partition of intermediate granularity is not minimal. c1 More fine-grained No type! E e1 w1 w2 e2 D d2 d1 d4 d3 Not minimal! R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

9. y5 y6 x6 y10 x4 x2 x1 x8 x7 y2 y7 x3 x10 x9 y9 x11 x5 y1 y4 z1 y8 y3 y11 Defining Closeness Formalization in RCC • Closeness in RCC can be inferred by composition rulexiyjz [P(xi, yj) XC(z, yj) CL(z, xi)] • XC(z, yj) C(z, yj)Pi(z, yj) • Example: • Community(xi)i  IminDistrict(yj)j  J • Community(x5) • P(x5, y7) XC(z1, y7)  CL(z1, x5) R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

10. Implementing RCCA DL Knowledge Base and Rule Base • Knowledge Base KB • P(xi, yj) and subrelations are functional roles:an individual xi is only part of a single region yj • Partitions are nominals • m.p.o. on typed partitions:asserting partOf(xi, yj) and subrelations, exclusively for (xi, yj) with C(xi)i  IminD(yj)j  J • Rule Base RB • Composition as DL-safe rule R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

11. Query ProcessingAn Example Query • Example Query: ( LandscapecloseTo.{Aesch_(ZH)} ) (z) • Evaluation in KB and RB returns {Albiskette-Reppischtal} • Logically enabled search engine expected to return all matches for Albiskette-Reppischtalusing query:<Landschaften "in der Nähe von" "Aesch (ZH)"> R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

12. Conclusion • Closeness is a vague concept • Borderline cases exist, for which coverage by the concept is difficult to decide • Account for borderline cases by using a qualitative formalism • The concept of closeness evolves over time • Evolution includes revision of administrative structures • Accounts for evolution by taking the administrative structures as a frame of reference R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

13. Outlook Future Research • Extension to arbitrary regions? • Does the proposed implementation scale well?Do alternative implementations scale better? • Can other vague spatial concepts be formalized in a similar way? • “near”, “next to”, “a short distance outside”, • “a long way off”, and “far away from” R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

14. Thank you for your attention! Now, it‘s time for answers! R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing

15. Query ProcessingAlgorithm • CLOSETO computes (QcloseTo.{a})(z) from KB and RB • FUNCTION CLOSETO • INPUT: Knowledge Base KB = {T, A}, Rule Base RB, Concept Q, Individual a • OUTPUT: Set<Individual> • {b} ← {b | ApartOf(a, b)} • V← {viI | AexclusivelyConnectsWith(vi, b)} • W ← {wjJ| AQ(wj)} • Z ← V∩W • OUTPUTZ • (QcloseTo.{a})(z)returns the set of those individuals of type Q that are close to a given individual a of a partition R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing