pelagios 2011

The Pelagios ontology workshop

Semantic Reasoning over Qualitative Spatial Relationsfor Image Interpretation

Monika Solanki

Department of Computer ScienceUniversity of Leicester

[email protected]

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

Monika Solanki Semantic Reasoning over Qualitative Spatial Relations for Image Interpretation

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

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 theMediterranean region, between the late bronze age and the lateclassical period (c.1500-c.200 BCE) by interrogating materialobjects

seven archaeological case studies fully integrated with computerscience projects

programme sets technological networks in their greater social,economic and political contexts to expand our understanding ofwider cultural developments

these networks from the past can help us devise new and moreeffective ways of transmitting knowledge and information in ourdigital world

http://www.tracingnetworks.org/


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 largegeographical region, over a long time period, they cantrace the links between the people who made, used, andtaught others to make them.

The Chaîne opératoire

Cross-craft interaction



The TN-LOD cloud

Tracing Networks through Linked Open Data



Tracing Networks: Vocabularies

CIDOC-CRMIt provides definitions and a formal structure for describingthe implicit and explicit concepts and relationships used incultural heritage documentation.

An ontology of 86 classes and 137 properties for cultureand more.

International standard since 2006 - ISO 21127:2006.

The ontology has been encoded in OWL2.0, OWLDL andRDFS.

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




Tracing Networks: Vocabularies

CIDOC-CRMIt provides definitions and a formal structure for describingthe implicit and explicit concepts and relationships used incultural heritage documentation.

An ontology of 86 classes and 137 properties for cultureand more.

International standard since 2006 - ISO 21127:2006.

The ontology has been encoded in OWL2.0, OWLDL andRDFS.

Tracing Network vocabularies extend CIDOC-CRM





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

MotivationThe most time-consuming part of an archaeologicalinvestigation is the post-excavation analysis.

There is therefore a mileage in combining the task ofarchiving, querying and analysing the data within a singleframework.

Archaeological data is fragmentary. Inferencing capabilitiesof reasoners can be used to extract implicit knowledge andcontribute to their existing knowledge bases to completethe fragments.



SEA: Semantic Explorer for Archaeology

A web application.

RESTful APIs for programmatically accessing the TN-LODcloud.

Interactive and global querying of linked datasets.

Data visualisations using user defined perspectives.

Statistical analysis using bespoke criteria provided byarchaeologists at runtime.



Case study: Human representations

The scope of the project includes examining and analysinghuman representations on a range of object types and in arange of materials, such as bronze and pottery.

The project utilises details such as gestures and postures,dress and associated objects as keys to understandinghow identity and new understandings of society arecommunicated.

Raw data is collected through examining objects frompublished literature or in museum collections.



Human representations: Informal queries

Example 1:

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



Human representations: Informal queries

Example 2:

“Find all objects which have images of individuals in the orantgesture who are wearing a triangular dress, earrings and whocarry a vessel on their head, where the vessel is supported bytheir left hand. I would also like to know the statisticaldistribution of the gender of these individuals according to thecountry in which the objects were found. I would like tovisualise the results as a tree map and see the distribution ofthe 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/ordinaldirections.

Use ontologies to support annotation and consequentlysearch.

Multiple vocabularies to support the process.

Annotate only those regions of the image which arecontextually 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 relationC(x; y), - region x connects with region y.

On top of this relation, a number of other dyadic relationsare defined.




Region Connection calculus: RCC-8


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


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 andordinal directions?

north_west_of

south_west_of

west_north_west_of

west_of

west_south_west_of

right_of

south_east_of

north_east_of

east_south_east_of

east_of

east_north_east_of

left_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:E38ImageInArt ⊑ ∀ hasImageRegion.ImageRegionImageRegion ≡ ImageInArt

⊓ ≥1hasInitEndCornerSelector⊓ ≥1hasAnnotation⊓ ∃hasAnnotation.ano:Note⊓ ∃hasInitEndCornerSelector.ano:InitEndCornerSelector

hasImageRegion ⊑ dc:hasPartisImageRegionOf ⊑ dc:isPartOfhasImageRegion ≡ isImageRegionOf−

hasInitEndCornerSelector ⊆ isInitEndCornerSelectorOf−



Pellet Spatial

A Hybrid RCC-8 and RDF/OWL Reasoning and QueryEngine.

Provides consistency checking and query answering.

Supports all RCC-8 relations as well as standardRDF/OWL semantic relations.

Supports SPARQL querying over consistent sets of spatialrelations and non-spatial semantic RDF relations.

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


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


Example

http://en.wikipedia.org/


http://en.wikipedia.org/


Conclusions

A preliminary approach has been presented to queryimage repositories based on the spatial orientation ofimage regions.

A coarse notion of fuzziness has been used in defining thespatial orientation of image regions relative to each other.

Cardinal/ordinal directional predicates along with RCC-8relations 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

Documents

qualitative spatial

semantic explorer

technological networks

ontology instance generation

archaeological data

tracing network vocabularies

implicit knowledge

data visualisations