Research Labs
GeoSemantics
Key challenges in expressing and
utilising geospatial semantics at
Ordnance Survey
Presentation by Katalin Kovacs and Sheng Zhou
Our colleagues: Cathy Dolbear, John Goodwin, Glen
Hart
European GeoInformatics Workshop
9th March 2007
Ordnance Survey
• The national mapping agency of Great Britain
• Information provider
• Responsibility to collect and disseminate
geographical information
• Responsibility to maintain the accuracy, currency and
delivery of geographical information
To Benefit the Nation
GeoSemantics at Ordnance Survey
• Responsibility to develop methods to add semantic
content to geographical information
• To maximise use and value that our customers and
partners get out of our data
Why?
To Benefit Society
Challenges in Utilising Semantics
What? – Getting the content right
• Ontology Authoring
How? - Making it work
• Identifying Fields in OS MasterMap™ (Glen Hart)
• Mapping the ontology to a database (Cathy Dolbear)
• Automatic sentences conversion and CONFLUENCE
project (Sheng Zhou)
Ontology Authoring
• Domain expert develops conceptual ontology with a
purpose in mind using structured sentences (human
readable)
• Knowledge modeller converts it to OWL (Protégé,
Machine readable)
• Iterate and Cooperate
• Publish both the human and machine readable
ontologies
http://www.ordnancesurvey.co.uk/oswebsite/ontology/
Getting the CONTENT right
Identifying Fields in OS MasterMap™
• DEFRA, Natural England, Natural Heritage Fund need to know
where fields are.
• Currently, there is no classification for a field in OS data.
North Mine
(disused)
North Mine
Tra
ck
Caravan Bridge
(disused)
Shaft
Shaft
Shaft

Bertriggen
th
Pa
Theme:
Land
Descriptive Group:
Natural Environment
Descriptive Term:
Scrub
Make:
Natural
Theme:
Land
Descriptive Group:
Natural Environment
Descriptive Term:
Heath
Make:
Natural
)
(um
Southerly
Updown Cottage
South Mine
Wild
Winds
(disused)
Shaft
Shaft

Colipin
Knoll
Shaft
Shaft
Shaft
Tra
ck
Tra
ck
Sandover
Farm
Shaft
South Mine
Track
Moonraker
Cottage
(disused)
Cornish Tree Surgery
Binner Downs
Shaft
Tra
ck
Binner Downs
Shaft

Theme:
Land
Descriptive Group:
General Surface
222
000
022
3330
3
333
333
B
BB
BB
B
Descriptive Term:

Make:
Stone
BM 89.73m
Lawford House
April Cottage

Easter Cottage
Natural
So what does define a field?
•
Through experimentation a field was determined to be:
•
Single Polygon features with:
• Theme: Land
• Make: Natural
And
• Descriptive Group: General Surface and Descriptive Term: Null
Or
•
•
•
•
•
•
Descriptive Group: Natural Environment and Descriptive Term: Orchard,
Coniferous trees (scattered),
Non-coniferous trees (scattered),
Boulders (scattered)
rough grassland
heath.
• The smallest that a field can be is 0.1 Ha and the largest 30Ha
• Fields must not be long and thin (e.g. a road side verge): area/perimeter > 8.7
An Ontological Approach
• Cumbersome to reclassify within the database
• But fields could be defined in an ontology and then mapped to
the database
Domain Ontology
Data Ontology
Database
Simplified Example
Field
Is a
Domain Ontology
Orchard
Has
Footprint
Arable /
Pasture
Is a
DB
Orchard
Data Ontology
Polygon
Is
equivalent
to
Has
Column
Feature Table
and Desc Term
and
(has FieldValue
has
“Orchard”)
Desc
Term
Is in
Has
Column
Feature
Table
Mapping the ontology to a database
Current technology options:
• D2R Server (SPARQL queries on virtual RDF graph) [1]
• Oracle RDF or other triple stores [2,3]
• Semantic Web Services
[1] http://sites.wiwiss.fu-berlin.de/suhl/bizer/D2RQ/
[2] Developing Semantic Web Applications using the Oracle Database 10g RDF Data Model
Xavier Lopez, and Melliyal Annamalai, http://www.oracle.com/technology/tech/
semantic_technologies/pdf/oow2006_ semantics_061128.pdf
[3] S. Harris, N. Gibbins. 3store: Efficient Bulk RDF Storage, 2003. 1st International Workshop
on Practical and Scalable Semantic Systems.
D2RQ
Custom SPARQL functions can be written for spatial queries – but exact
implementation (+ meaning) hidden in code
SPARQL
query
Query result
D2RQ Server
Joseki SPARQL service
JDBC
connection
Oracle spatial
database
D2RQ
D2RQ Map
RDF virtual
graph
Jena
OWL
ontology
DIG reasoner
(e.g. Pellet)
Oracle RDF Store
• Limited reasoning (RDFS in 10g, subset of OWL in 11g)
• How much reasoning do we really need?
• Will companies convert all their relational data to RDF?
• RDF likely to degrade performance – 11GB for Southampton
area triples.
• Spatial component must be executed after RDF filtering
• Would it be more efficient to perform the spatial query
first to minimise the size of the RDF graph?
Our proposal
SPARQL+Geo
Query
• The meaning is stored in the
ontology,
• The doing is stored in the PL/SQL.
• Query optimisation is carried out
in the OS API
Query Results
Jena API
Joseki
OWL
ontology
Reasoner
RDF Graph
+
Spatial Indexing
Proposed API
SQL
Rose
Cott
e
Garag
2
BRAD
BRAD
BRAD
BRAD
BRAD
BRAD
FORD
FORD
FORD
FORD
FORD
4
El
1
Sub
21
Place
1
Rest miss
Com
Sta
s
QUAY
QUAY
QUAY
QUAY
QUAY
QUAY
Flora
ROAD
ROAD
ROAD
ROAD
ROAD
ROAD
6.4m
VIEW
VIEW
VIEW
VIEW
VIEW
VIEW
1
s
ioner
GE
GE
GE
GE
GE
GE
BRID
BRID
BRID
BRID
BRID
BRID
3
1to5
28
44
29
or
Man
44a
3
10
8 to
38
to
29
Sta
5
6
Chu
Cottage
BM 7.77m
Mud

15
1
2
1
3
Orchard
14
Walk
1
1
17
Mud
Mud
MLW
to
Cycl
e
el
k
Trac
r Cam
4
7
Sta
Rive
FB
7.8m
11
Bank
Posts

P's
Sub
1
12
16
1
2
Henwood's
Court
Bank
Bank
Hockings
Court
ET
ET
ET
4
STRE
STRE
STRE
H
H
H
H
H
H
ORT
ORT
ORT
ORT
ORT
ORT
ESW
ESW
ESW
Walk
MOL
MOL
MOL
MOL
MOL
MOL
ond
Lovib
16
6
6
1
4
11
Hall
Ct
Mallets
ET
ET
ET
ET
ET
ET
1
ROA
ROA
ROA
ROA
ROA
ROA
D
D
D
D
D
Sub
El
The
Malt
House
Dukes
Mead
6.9m
rch
ts
Mallet
4
IONE
IONE
IONE
IONE
IONE
IONE
RS
RS
RS
RS
RS
RS

Bridge
Medical
Centre
ROAD
ROAD
ROAD
ROAD
ROAD
ROAD
PLA
PLA
PLA
PLA
PLA
PLA
CE
CE
CE
CE
CE
2
LB
7.0m
7.0m
OUR
OUR
OUR
OUR
OUR
OUR
EDD
EDD
EDD 1
EDD
EDD
EDD
2
YST
YST
YST
YST
YST
ONE
ONE
ONE
ONE
ONE

1
y
28
GP
Water
Qua
HARB
HARB
HARB
HARB
HARB
HARB
27
Mud
The
Old Bridge
Marin
Terra
e
ce
5
NN
NN
N
N
STRE
STRE 4
STRE
STRE
STRE
Ct
wen
Bosla end
Bridg
Court
Bridge
House
n
9
26
High
Tow
to
to
Mean
1
22
swor
th
t
Cour
7.0m
D
DD
DD
D
COM
COM
COM
COM
COM
COM
MISS
MISS
MISS
MISS
MISS
19
21
to
1 to
10
MLW
ROAD
ROAD
ROAD
ROAD
E
E
E
E
E ROAD
STON
STON
STON
STON
STON
EDDY
EDDY
EDDY
EDDY
EDDY
EDDY
ROA
ROA
ROA
ROA
ROA
R
RRR
R
tone
BOU
BOU
BOU
BOU
BOU
BOU Eddys House
HAR
HAR
HAR
HAR
HAR
HAR
Wat
er
Eddy
Terra stone
ce
6
ANSO
ANSO
ANSO
ANSO
ANSO
ANSO
Mole
Stable
Court
High
45
28
TREV
TREV
TREV
TREV
TREV
TREV
Sta
9
n
to
10 9
9a
Sub
1 to
Mea
Mud
y
20
Qua
El
Path
Glebe
Court
n
Tow

Church
The Old
Coach
House
41
27
MLW
Quay
Depot
35
Kingf
MLWisher
Drain
Spatially Enabled
Database
Other Things we are working on
Places and Buildings ontology
Administrative Geography ontology – with instances!
Ontology Merging
So a knowledge modeller walks into a Starbucks ...
®
...and he says:
“I'll have a Venti Physical Non-Agentative Non-Chemical
Mental Object with a Stative Abstract Quality and
Double Qualia inferred from the Achievement of a
Temporal Region.”
And the barista says:
“Endurent or Perdurent?”
Introduction to Rabbit
Purpose: to allow domain experts to create and
work with ontologies
• Domain experts hold all the domain information
• Do not have description logic experience
Current ontology building at Ordnance
Survey
• A Two stage process
•Domain experts + Knowledge Engineers
•Pros:
•Interactive and iterative interpretation by
domain experts and knowledge engineers
•Capable of complex modelling
•Cons:
•Tedious
•Could be more efficient
•Inconsistent conversion from conceptual
ontology to logical ontology
The CONFLUENCE Project
•Objective:
•To develop a tool for domain experts
•To enable one-step ontology development in a
controlled natural language
•Output:
•A public domain software tool with full GUI
•Working plan:
•Cooperation between Ordnance Survey and
Leeds University (Prof. Cohn and Dr. Dimitrova)
Elements of CONFLUENCE
CONFLUENCE
•Glossary population:
•tools to help domain experts to organise
knowledge, discover concepts and relations.
•Formal structuring:
•interactive ontology authoring with Rabbit
•Ontology documentation and maintenance
•Ontology inspection and evaluation
Rabbit: an interface languages for CONFLUENCE
•What is Rabbit:
•A controlled natural language (English) for
ontology authoring
•(Potentially) a high-level syntax for OWL
•Why yet another CNL
•Rabbit includes specific constructs and
interpretations originated from real world
ontology building practice
Golden Egg!
Rabbit: feature list
•A feature list for Rabbit:
•Formalisation of “structured
sentences”
•Support of most OWL elements
•Support of references to external
resources
•Stand-alone (independent of the
tool)
•Experiment on the usability of Rabbit
will be carried out soon.
Rabbit by Example
Rabbit is a kind of mammal
=> Rabbit subClassOf Mammal
A rabbit has exactly 2 big ears.
=> hasEar exactly 2 BigEar
A rabbit has exactly 2 big eyes.
hasEye exactly 2 (Eye AND BigThing)
A rabbit has whiskers.
=> hasWhiskers some Whiskers
A rabbit has exactly 1 short tail.
=> hasTail exactly 1 ShortTail
A rabbit has soft fur.
=> hasFur some SoftFur
A rabbit has fur colour only white, brown or
black.
hasFurColour some {white, brown, black}
A rabbit eats fresh vegatables.
=> eats some FreshVegatable
Peter Rabbit is an instance of Rabbit.
=> Rabbit: PeterRabbit
Rabbit-to-OWL Conversion
•Conversion module
•Responsibility of Ordnance Survey
•Purpose:
•to provide automated and consistent conversion
from Rabbit sentences to OWL ontology
•Development is in progress
•A Rabbit Ontology model + Protégé API
•Plan to release the first prototype in the near
future
Plan for the future: breeding rabbits
•To improve current Rabbit phrase set / syntax
•Mechanism for expansion
•Query in Rabbit?
Conclusions
Getting the Content Right:
• Allow domain experts to building ontologies – Rabbit
Making it Work:
• Making semantic sense of existing spatial data
• Linking ontologies to data bases – OS API
Putting first things first:
• Solve more real world problems for more real people
• To benefit Society before our personal research
agenda
Start asking:
“What can geosemantics do for you?”
Contact us
Our Website:
http://www.ordnancesurvey.co.uk/oswebsite/ontology/
Email: Catherine.Dolbear@ordnancesurvey.co.uk
Katalin.Kovacs@ordnancesurvey.co.uk
Sheng.Zhou@ordnancesurvey.co.uk
Break out session topic: Key Limitations
Getting the Content Right:
• Make it easier to domain experts to build and work
with ontologies
Making it Work:
• Meaning of data in existing data bases
• Linking ontologies to data bases
Putting first things first:
• Demonstrate that semantics can solve real world
problems for real people
• Benefit society before our personal research
interests