1. Ontologies for Reasoning, Action and Interaction in Space
John Bateman
University of Bremen
SOCop Meeting: 12th November 2009

2. Overview of talk Context of the work Representations of Space
within our Collaborative Research Center: “Spatial Cognition”...
... and my work within that
Representations of Space
Results and Conclusions
Proposals for Ontology Best Practice
Next targets and challenges

3. Spatial Cognition: SFB/TR8
Areas: spatial reasoning, representation, action and communication
Organisation:
Collaborative Research Center
Bremen / Freiburg
funded by: Deutsche Forschungsgemeinschaft (DFG: German Research Council )
3 Phases
1st phase: : 12 projects
2nd phase: : 18 projects
3rd phase: being proposed

4. Spatial Assistance Mobility support Spatially-embedded tasks
Descriptions of spatial situations
verbally
visually
Exploration
Navigation
Dialogic interaction

5. Bremen Autonomous Wheelchair: Rolland
Qualitative Information “In front to the right is the seminar room”
Quantitative information
: Rolland
Symbolic information
[door_1 recognized]

6. Test-Bed: BAALL Bremen Ambient Assisted Living Lab
Environment
heating, lighting
safety
soft / hard / middleware interaction
Appliances
refrigerator
cooker
cupboards,
drawers
washing machine,
microwave
TV, PC, …
mobile phone
doors
Autonomous assistance devices
wheelchairs
walkers
Health monitoring
Architecture

7. Sensor data: ‘free-space’ maps

8. Voronoi calculation on a scanned floor plan
“where are you?”
Voronoi map From SFB/TR8 project: A1-[RoboMap]

9. Unnatural / unhelpful descriptions
25.4 m NW of you
GPS: “34° 15´ N / 3° 27´ E”
“3.45m away from edge 98 (with 80% certainty)”

10. Natural route descriptions
Leave the room and turn right into the corridor.
Go to the window and then turn left.
Follow the corridor and I’m in the last room on the left.
Many problems of semantic interpretation involved here...

11. Context-specific way-finding assistance
schematization
Kai-Florian Richter, Thomas Barkowksy et al.
"At the next junction go straight on, and then, turn right before a map. Keep following the river until a Telekom sign, and then, turn left after the Telekom sign. Go towards the Universum until a bus stop, and then, turn right after the bus stop."

12. Sources of relevant knowledge
Location-based services
Geographic Information Systems
Commonsense objects and activities
Spatial awareness and understanding
Natural language capabilities
Robot perception

13. Basic problem
Getting these diverse areas of expertise to talk to each other is a serious issue
different communities
different interests
different representations
The kinds of knowledge maintained by such systems are very different

14. Consequences
Component specifications are developed again and again (and again and again)
Each community of practice defines them slightly differently (or not, it is difficult to tell)
Each standardisation group has little time to look at parallel activities and must reflect the demands of its own community before considering others
Lack of foundation leads to a proliferation of ‘standardisation’ efforts

15. 15 Research Foci: John Bateman Computational Linguistics
Multilingual natural language generation
Interfaces between language technology and world/domain knowledge
Development of linguistically-motivated ontologies
Formal Ontology
General design principles for ontology
Relations between differently motivated ontologies
Spatial representation and language
Dialogic natural language interaction
Spatial language
1988
1992
2001 15

16. Solution we are pursuing
High degree of interoperability between diverse knowledge-rich systems is to be achieved by ontological engineering, taking in:
knowledge of the human world (commonsense)
knowledge of the robot world (programmed, emergent)
geo-knowledge (GML, other standards)
spatial knowledge (spatial calculi, spatial ontologies)
knowledge of language (linguistics)

17. HIGHLY STRUCTURED AND MOTIVATED SEMANTICS
Goals
HIGHLY STRUCTURED AND MOTIVATED SEMANTICS
Achieving interaction between system modules using ONTOLOGIES
ONTOLOGY 1
ONTOLOGY 2
inter-ontology mediation
ONTOLOGIES
DOMAINS

18. Goals Achieving interaction between system modules using ONTOLOGIES
SPATIAL REPRESENTATIONS
LINGUISTIC REPRESENTATIONS
inter-ontology mediation
ONTOLOGIES
DOMAINS

19. Goals Achieving interaction between system modules using ONTOLOGIES
SPATIAL REPRESENTATIONS
GENERAL ONTOLOGY
inter-ontology mediation
ONTOLOGIES
DOMAINS

20. Goals Achieving interaction between system modules using ONTOLOGIES
ROUTE GRAPH R3: GVG
ROUTE GRAPH A1: PATH
inter-ontology mediation
ONTOLOGIES
DOMAINS

21. Ontologies...
... but where to start?

22. Fundamental issue The ontologies present are diverse:
different methodologies
different motivations
different domains of application
different worlds
different purposes
different communities

23. Representations of Space
Ontology and Space
Qualitative Spatial Representation and Reasoning
Language
GIS

24. Representations of Space
ontology
Foundational Ontologies
Qualitative Spatial Reasoning +
Representation
Linguistics
physical
mathematical
Geometry R3

25. Ontology + QSR: Varying primitives
Bateman/Farrar (2006) Spatial Ontology Baseline 25 25

26. Spatial Representations
Ontology
Qualitative Spatial Reasoning and Representation
Language 26 26

27. Ontologies: SUMO 27 27

28. Ontologies: SUMO 28 Shape: internal attribute (inheres in some entity)
Position: relational attribute 28 28

29. Ontologies: SUMO
Spatial Relations 29 29

30. Ontologies: Cyc 30 30

31. Ontologies: Cyc 31 31

32. Ontologies: Cyc 32 32

33. Ontologies: Cyc: Paths33 33

34. Navigation: route graphs
MZH
3rd
Floor
kitchen
MZH 3100
MZH 3110
Route
Graph 34 34

35. Ontologies: DOLCE basic categories35 35

36. DOLCE basic categories
Rooms, offices, buildings,
tables, chairs, ... 36 36

37. DOLCE basic categories
Events, happenings,
movements, changes 37 37

38. DOLCE basic categories
Agents, states of belief,
plans, goals 38 38

39. One-sided generic constant dependence
DOLCE inter-entity relationships
Feature
(F)
Non-Agentive
Physical Object
(NAPO)
Amount of Matter
(M)
Physical Endurant (PED)
Physical Quality
(PQ)
OGD GK
MSDS
Mutual specific
spatial dependence
One-sided generic constant dependence
Generic constant
constitution
Quality (Q)
heavy
pebbly
wood
rocks, sand, water
table
coast
corner of a table
curve of a bay 39 39

40. Spatial Representations
Ontology
Qualitative Spatial Reasoning and Representation
Language 40 40

41. QSR: Region-Connection Calculus with 8 base relations
Randell, Cohn, Cui 1992: RCC8 41 41

42. QSR: Region-Connection Spaces: RCC-5, RCC-8, 9+, etc.
Randell, Cohn, Cui 1992 etc. / Egenhofer 42 42

43. QSR: Reasoning by Composition
Composition table for RCC-8 43 43

44. QSR: Double cross calculus: Freksa / Zimmermann (1996)
Qualitative description of position relative to a directed line segment 44 44

45. QSR: Reasoning by Composition
ab:c ○ bc:d →ab:d
Composition for reasoning 45 45

46. QSR: Reasoning by Composition
Homing, Shortcut, Inverse, Homing-Inverse,Shortcut-inverse
Composition with additional relations 46 46

47. QSR: Star Calculus
Renz/Mitra 2004 47 47

48. QSR: Dipole
Moratz et al. 2000 48 48

49. QSR: OPRA
Moratz et al. 2005 49 49

50. QSR: QTC 50 Van de Weghe et al. Qualitative Trajectory Calculus
single object moving
two objects moving 50 50

51. Spatial Representations
Ontology
Qualitative Spatial Reasoning and Representation
Language 51 51

52. Linguistic usage evidence…
what does ‘on’ mean?
Herskovits (1986) 52 52

53. And more usage evidence…
what does ‘in’ mean?
Herskovits (1986) 53 53

54. And more usage evidence…
what does ‘in’ mean?
“The cat is in the table”
Herskovits (1986:125) 54 54

55. And more usage evidence…
what does ‘in’ mean?
The potato is in the bowl
Herskovits (1986) 55 55

56. Relating calculi and language
Egenhofer and colleagues 56 56

57. Spatial Language extremely flexible sensitive to function and purpose57
Coventry, Garrod and others 57

58. Many types of spatial information
Ontology
Qualitative calculi:
RCC-n, Dipoles, Doublecross, etc.
Way-finding abstractions: choremes
Free-space representations (Voronoi)
Natural language descriptions
Metric maps
With different reasoning methods, different coverage, different strengths and weaknesses 58 58

59. Representations of Space
ontology
Foundational Ontologies
Qualitative Spatial Reasoning +
Representation
Linguistics
physical
mathematical
Geometry R3
BFO
DOLCE
GFO
RCC DC
OPRA 9+
GUM-Space ? ? ? ?
‘alignment’ 59 59

60. Methodological conclusion and starting point
There is no sense in which a simple ‘merging’ of all of the above is a sensible strategy to follow 60 60

61. Distinct facets or ‘theories’ rather than inheritance
link in transit system (ferry)
geographical region
lake
source of pure water
recreational area
obstacle 61 61

62. Many perspectives on ‘reality’: many ontologies
space-1
event
time
space-2
event
Ontologically diverse 62 62

63. Ontological diversity inter-ontology mappings
landmarks
event types
time
choremes
CASL
CASL
CASL
CASL
route graphs
Way description
CASL 63 63

64. Mapping between modules
“Hyperontology”
points of interest
road conditions
time
directions
Mapping between modules
health status
problem area 64

65. Essential properties we are currently developing
Perspectivalism
Objects
Activities
Artifacts: spatial artifacts
Language
Granular partitions
Plug-and-play spatial theories 65 65

66. Essential ingredients we are drawing on
Existing ontologies
Existing formal tools
Extensions for the specific problem of combining information flexibly
Combining distinct reasoning possibilities 66 66

67. Essential ingredients we are drawing on
Existing ontologies
DOLCE (for cross-category binding and axiomatization)
BFO (for sites, niches and places and for SNAP/SPAN)
GUM (generalized upper model for linguistic semantics) 67 67

68. DOLCE basic categories68 68

69. Defining Qualities 69 I1-[OntoSpace] Quality Space
Gärdenfors: Geometric
Fauconnier: Logical 69 69

70. Defining Qualities 70 I1-[OntoSpace] Quality Space
Gärdenfors: Geometric
Fauconnier: Logical
Goguen:
algebraic theory
Theory 70 70

71. Color Space (1): Color wheel
HUE
ORANGE
YELLOW
CHROMATICNESS
GREY
RED
GREEN
VIOLET 71
From: Gärdenfors (2000, p10) 71

72. Color Space (2) 72 WHITE GREEN YELLOW CHROMATICNESS BLUE RED
From: Gärdenfors (2000, p11)
BLACK 72 72

73. Defining Qualities 73 Quality Space 73 WHITE GREEN YELLOW BLUE RED
BLACK
WHITE 73 73

74. Dolce treatment of qualities
Qualia: the position of an individual quality within a quality space 74 74

75. DOLCE: spatial information
For DOLCE, space is also a quality... 75 75

76. DOLCE: relevant for space
Physical Objects
Physical Endurants (PED)
Spatial Location
Space Region 76 76

77. DOLCE: relevant for space
Physical Objects
Physical Endurants (PED)
Spatial Location
Space Region 77 77

78. Dolce treatment of qualities
Qualia: the position of an individual quality within a quality space 78 78

79. Dolce treatment of qualities
Space Region
Qualia: the position of an individual quality within a quality space 79 79

80. Valuable for `swappable’ treatments of spaceql PQ
PED qt
Physical Endurant
Physical Quality
Quality Space: quale 80 80

81. `Swappable’ treatments of space
Should be possible to select formalization for the reasoning task at hand
Formalized modules ql PQ
PED qt
RCC-5,7,8,10,15,23
Dipoles: D14 , DRA14 DRAfp
Cardinal directions
Double Cross 81 81

82. Essential ingredients we are drawing on
Formal and computational tools
CASL Common Algebraic Specification Language (for specification, structuring and relating)
HETS Heterogeneous Tool Set (for connecting to a range of reasoners) 82 82

83. Formalization choice: CASL Common Algebraic Specification Language
Standardised first-order specification language
designed by CoFI “Common Framework Initiative for algebraic specification and development” since 1995
de facto standard approved by IFIP WG 1.3 “Foundations of Systems Specifications” (1998), extensive documentation (LNCS 2900, 2960)
extensive User Manual and Reference Manual now available from Springer (LNCS 2900, LNCS 2960)
supports structured specifications including imports, hiding, renaming, union, extensions, etc. 83

84. The Tool HeTS
Institution Theory 84

85. CASL language constructs
Structuring mechanisms: potentially applicable to any specification language as an additional layer of ‘meta’-organisation for semantic modularity
OWL, UML, CommonLogic, etc. 85

86. Formalization choice: CASL Common Algebraic Specification Language
Extensions:
we have now added OWL-DL to the family of logics supported
we are exploring combining the structuring principles of CASL and description logics
we are progressively formalizing the entire family of qualitative spatial calculi 86 86

87. Lüttich & Mossakowski (FOIS 2004)
Axiomatized Ontology in CASL
DOLCE
Primitives
GenParthood 87 87

88. Lüttich & Mossakowski (FOIS 2004)
GenParthood
DOLCE
GenMereology 88 88

89. Lüttich & Mossakowski (FOIS 2004)
Primitives
GenParthood
GenMereology
spec MEREOLOGY =
PRIMITIVES
then
%%Ad7, Ad8, Ad9 and Ad10 are generated by
%% instantiation of GenMereology
GENMEREOLOGY [sort T]
GENMEREOLOGY [sort S]
GENMEREOLOGY [sort PD]
end
Mereology 89 89

90. The DOLCE ontology in CASL
spec PreDolce =
Mereology_and_TemporalPart
and Temporary_Mereology
and Participation
and Constitution
and Dependence
and Direct_Quality
and Temporary_Quale
and Immediate_Quale
end
spec Dolce =
PreDolce
and
Taxonomy
end
work continuing... 90 90

91. Lüttich & Mossakowski (FOIS 2004)
Development Graph
showing dependencies between specifications
and proof obligations
Links: theory morphisms
imports of theories
relative interpretations of theories
open
proved 91 91

92. Reasoning 92 First-Order Reasoning with CASL/HETS reasoners
Description logic reasoning with DL reasoners
Spatial Reasoning with specialized spatial reasoners: SparQ, GQR 92 92

93. Reasoning: SparQ
Frank Dylla, Lutz Frommberger, Jan Oliver Wallgrün, and Diedrich Wolter 93 93

94. Lessons drawn
Idea: Providing channels to ontologies provides access to detailed contextual ‘world-knowledge’ that does not then have to be worked out again…
Ontology
Application 94 94

95. Sharing knowledge and achieving interoperability
Many projects, many products, many information providers now constructing ontologies
BUT:
proliferation of unrelated designs,
impoverished or application-specific semantics,
‘roll your own’ ignoring previous attempts
lack of interoperability
... which was precisely what ontologies were meant to provide! 95

96. modelling language dependence
Metamodels: commonly restricted to ensuring translatability across formal languages not content
modelling language dependence
Horiuchi 96

97. LOA, Dolce group: EU WonderWeb Project97 97

98. Problems... 98 Looseness of definition Sparseness of definition
does not give much to ‘get hold of’ for relating distinct accounts/levels of abstraction 98 98

99. Conclusions: Ontology Best Practice
‘Light’ ontologies: semantic web ...
‘Heavy’ ontologies:
Rich axiomatization
Formal principles
Well-defined design criteria 99 99

100. Lessons drawn 100 Ontological best design principles axiomatization
modularity
heterogeneity
perspectivalism
100
100

101. Ontology construction
Axioms are grouped into logically appropriate theories
Theories may be extended via parameterization to achieve semantic re-use
Theories may be created and related by views: theory morphisms
Only with this availability of working with meaningful interrelationships can the complexity of distinct axiomatized ontologies really be harnessed.
101
101

102. Where next?
Geospatial Information set to become the next major area of ontological development?
However, just converting existing schema to OWL is probably not going to be adequate
102
102

103. Questions: modularity?
OpenGIS® City Geography Markup Language (CityGML) Encoding Standard (2008)
103
103

104. Questions: modularity?
104
Geometry to BDM to IFC
104

105. Selected Application Scenarios
Our Next Steps
Selected Application Scenarios
assisted ambient living (AAL)
ADL, spatial activities, ...
geographic information science (GIS)
OpenGIS, OGC, CityML, OpenStreetMap, ...
assisted architectural design (AAD)
IFC, BIM, ...
In each application area, we want to interact directly with the appropriate national and international standards
105
105

106. Geographic Information Systems
<way id=" " visible="true" …>
<nd ref=" "/> …
<tag k="landuse" v="forest"/>
</way>
Our Next Steps
Geographic Information Systems
Ontologies
framework for connecting distinct geographic layers
modular breakdown of relevant knowledge improving re-use
relation to non-geographic modeling
relation to qualitative representations
relation to existing standards
support for verbalisation and visualisation
<way id=" " visible="true" …>
<nd ref=" "/> …
<nd ref=" "/>
<tag k="name" v="Feldackerweg"/>
<tag k="surface" v="paved"/>
<tag k="maxspeed" v="30"/>
<tag k="highway" v="residential"/>
</way>
<way id=" " visible="true" …>
<nd ref=" "/> …
<tag k="landuse" v="forest"/>
</way>
OpenStreetMap
106
106

107. and we can only do that in cooperation with those with the detailed expert knowledge!
107
107

108. Acknowledgements 108 The entire SFB/TR8 team!
108
108

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