Pelagios 2011

The Pelagios ontology workshop

Semantic Reasoning over Qualitative Spatial Relations
for Image Interpretation

Monika Solanki
Department of Computer Science
University of Leicester
m.solanki@mcs.le.ac.uk

http://http://commons.wikimedia.org/

Monika Solanki Semantic Reasoning over Qualitative Spatial Relations for Image Interpretation

Talk outline The Pelagios ontology workshop

Outline

Motivation
Proposed framework
Conclusions and future work


Tracing Networks The Pelagios ontology workshop

Tracing Networks
investigates the network of contacts across and beyond the
Mediterranean region, between the late bronze age and the late
classical period (c.1500-c.200 BCE) by interrogating material
objects
seven archaeological case studies fully integrated with computer
science projects
programme sets technological networks in their greater social,
economic and political contexts to expand our understanding of
wider cultural developments
these networks from the past can help us devise new and more
effective ways of transmitting knowledge and information in our
digital world

http://www.tracingnetworks.org/



Tracing Networks

Archaeologists study a wide range of material objects.
By tracking them at every stage of their production,
distribution, use, and consumption across a large
geographical region, over a long time period, they can
trace the links between the people who made, used, and
taught others to make them.

The Chaîne opératoire
Cross-craft interaction



The TN-LOD cloud

Tracing Networks through Linked Open Data



Tracing Networks: Vocabularies

CIDOC-CRM
It provides deﬁnitions and a formal structure for describing
the implicit and explicit concepts and relationships used in
cultural heritage documentation.
An ontology of 86 classes and 137 properties for culture
and more.
International standard since 2006 - ISO 21127:2006.
The ontology has been encoded in OWL2.0, OWLDL and
RDFS.

http://cidoc.ics.forth.gr/index.html



Tracing Networks: Vocabularies

CIDOC-CRM
It provides deﬁnitions and a formal structure for describing
the implicit and explicit concepts and relationships used in
cultural heritage documentation.
An ontology of 86 classes and 137 properties for culture
and more.
International standard since 2006 - ISO 21127:2006.
The ontology has been encoded in OWL2.0, OWLDL and
RDFS.
Tracing Network vocabularies extend CIDOC-CRM

http://cidoc.ics.forth.gr/index.html



Transformation Framework

ORM Reverse Engineering.
ECA Rule-based Transformation.
Ontology Instance Generation.


SEA: Semantic Explorer for Archaeology The Pelagios ontology workshop

SEA: Semantic Explorer for Archaeology

Motivation
The most time-consuming part of an archaeological
investigation is the post-excavation analysis.
There is therefore a mileage in combining the task of
archiving, querying and analysing the data within a single
framework.
Archaeological data is fragmentary. Inferencing capabilities
of reasoners can be used to extract implicit knowledge and
contribute to their existing knowledge bases to complete
the fragments.



SEA: Semantic Explorer for Archaeology

A web application.
RESTful APIs for programmatically accessing the TN-LOD
cloud.
Interactive and global querying of linked datasets.
Data visualisations using user deﬁned perspectives.
Statistical analysis using bespoke criteria provided by
archaeologists at runtime.



Case study: Human representations

The scope of the project includes examining and analysing
human representations on a range of object types and in a
range of materials, such as bronze and pottery.
The project utilises details such as gestures and postures,
dress and associated objects as keys to understanding
how identity and new understandings of society are
communicated.
Raw data is collected through examining objects from
published literature or in museum collections.



Human representations: Informal queries

Example 1:
“Find images of riders who appear on the east side of objects
found in Austria where the altitude of the excavation site is 500
meters above sea level. I would also like to know the statistical
distribution of the material and the technologies used for the
production of these objects. I would like to visualise the results
as a pie chart and see the distribution of the sites where these
objects were found on Google Earth”.



Human representations: Informal queries

Example 2:
“Find all objects which have images of individuals in the orant
gesture who are wearing a triangular dress, earrings and who
carry a vessel on their head, where the vessel is supported by
their left hand. I would also like to know the statistical
distribution of the gender of these individuals according to the
country in which the objects were found. I would like to
visualise the results as a tree map and see the distribution of
the sites where these objects were found on Google Map”.


Motivation The Pelagios ontology workshop

Motivation/Problem statement

Capture “spatial” metadata about the content of images.
Spatial relation based on RCC and the cardinal/ordinal
directions.
Use ontologies to support annotation and consequently
search.
Multiple vocabularies to support the process.
Annotate only those regions of the image which are
contextually relevant and interesting.
Use “existing” spatial DL reasoners based on RCC-8.


Proposed Framework The Pelagios ontology workshop

Region Connection calculus: RCC-8

http://en.wikipedia.org/wiki/Region_connection_calculus

Defines a primitive reflexive and symmetric dyadic relation
C(x; y), - region x connects with region y.
On top of this relation, a number of other dyadic relations
are defined.



Region Connection calculus: RCC-8

http://en.wikipedia.org/wiki/Region_connection_calculus

disconnected (DC)
externally connected (EC)
equal (EQ)
partially overlapping (PO)
tangential proper part (TPP)
tangential proper part inverse (TPPi)
non-tangential proper part (NTPP)
non-tangential proper part inverse (NTPPi)



Cardinal and ordinal directions

http://en.wikipedia.org/wiki/File:Brosen_windrose.svg



A “coarse” notion of spatial fuzziness

X right_of Y: what does “right_of” entail in terms of cardinal and
ordinal directions?

north_west_of 

south_west_of 



west_north_west_of right_of

west_of 




west_south_west_of



south_east_of 


north_east_of 



east_south_east_of left_of

east_of 




east_north_east_of




Vocabularies used

CIDOC CRM version 5.0.2 (2010) (crm)
FOAF (foaf)
Annotation vocabularies (ano)
Dublic Core Metadata Initiative (dc)
Pellet spatial RCC-8 vocabulary (spa)
Cardinal-ordinal vocabulary (crd)
Domain speciﬁc vocabularies to describe images.



Image representation: TBox

ImageInArt foaf:Image, crm:E38
ImageInArt ∀ hasImageRegion.ImageRegion
ImageRegion ≡ ImageInArt
≥1hasInitEndCornerSelector
≥1hasAnnotation
∃hasAnnotation.ano:Note
∃hasInitEndCornerSelector.
ano:InitEndCornerSelector
hasImageRegion dc:hasPart
isImageRegionOf dc:isPartOf
hasImageRegion ≡ isImageRegionOf−
hasInitEndCornerSelector ⊆ isInitEndCornerSelectorOf−



Pellet Spatial

A Hybrid RCC-8 and RDF/OWL Reasoning and Query
Engine.
Provides consistency checking and query answering.
Supports all RCC-8 relations as well as standard
RDF/OWL semantic relations.
Supports SPARQL querying over consistent sets of spatial
relations and non-spatial semantic RDF relations.

http://clarkparsia.com/pellet/spatial/



Example

http://en.wikipedia.org/



Conclusions

A preliminary approach has been presented to query
image repositories based on the spatial orientation of
image regions.
A coarse notion of fuzziness has been used in deﬁning the
spatial orientation of image regions relative to each other.
Cardinal/ordinal directional predicates along with RCC-8
relations have been used.
Existing DL based spatial reasoners exploited.
Still WIP: lots more needs to be done.


The Pelagios ontology workshop

Many Thanks!!!


Pelagios 2011

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