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A Qualitative Approach to Vague Spatio-Thematic Query Processing. Rolf Grütter and Thomas Scharrenbach Swiss Federal Institute for Forest, Snow and Landscape Research WSL Zürcherstrasse 111, 8903 Birmensdorf, Switzerland {Rolf.Gruetter, [email protected]

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A qualitative approach to vague spatio thematic query processing l.jpg

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, [email protected]


Finding landscapes close to communities l.jpg
Finding Landscapes Close to Communities

Albiskette-Reppischtal

Aesch (ZH)

R. Grütter and T. Scharrenbach: A Qualitative Approach to Vague Spatio-Thematic Query Processing


Finding landscapes close to aesch zh l.jpg
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


Representing spatial knowledge approach l.jpg
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


Representing spatial knowledge partitions in rcc l.jpg

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


Representing spatial knowledge family of regions x i i i is partition of region y l.jpg

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


Representing spatial knowledge partial order on typed partitions in rcc l.jpg

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


Representing spatial knowledge minimal partial order on typed partitions l.jpg
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


Defining closeness formalization in rcc l.jpg

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


Implementing rcc a dl knowledge base and rule base l.jpg
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


Query processing an example query l.jpg
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


Conclusion l.jpg
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


Outlook future research l.jpg
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


Slide14 l.jpg

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


Query processing algorithm l.jpg
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


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