Semantically-Aware SpatioTemporal Data Analysis for
Humanitarian and Natural
Crisis Management
Dr Kristin Stock
Centre for Geospatial Science
University of Nottingham
Emergencies
• The River Trent is likely to flood – who
should be evacuated?
• Normal food crops in Liberia have failed –
where do I need to deliver emergency food
supplies?
• There has been a chicken pox outbreak in
Edinburgh - where are the high
concentrations of children?
Introduction: The Problem
• All of these scenarios can be aided by
geographic information.
• Requires time, expertise, significant
manual effort.
• Need to identify and search for:
– data and
– operations that must be performed to produce
the end result.
Introduction: The Vision
• The user would express requirements in
natural language.
• The requirements would be automatically
interpreted.
• Data and operations to meet requirements
would be automatically identified and
combined.
•  Result returned to user.
Introduction: The Solution
• Builds on and refines existing architectures.
• The Semantic Web:
– Ontologies that describe data and their semantics.
– Semantic Web Services (self-describing software
modules).
– Registries that describe resources.
• Allows web services to be automatically
discovered, interpreted, combined and invoked.
• But the full vision has not yet been realised…
Outline
• Three main research areas:
– Expressing and transforming user objectives
into formal, semantically equivalent
expressions.
– Decomposing user objectives and identifying
semantically equivalent web services.
– Implementing an ontological registry to
support semantic matching.
• Simple flood evacuation example.
The Research Problems
1. How to express the real world
problem?
Real World
Problem
3
1
Spatio-Temporal
Analysis
Process a
Spatiotemporal
processes may
also be
composed of
other spatiotemporal
processes
2. How to automatically map the real
world problem to a chain of spatiotemporal processes?
2
Spatio-Temporal
Analysis
Process b
Spatio-Temporal
Analysis
Process c
3. How to express the semantics of a
spatio-temporal process?
4. How to automatically map a spatiotemporal process to a chain of
spatio-temporal operations (or
other processes)?
1 loop until
2
Spatio-Temporal
Operation x
Spatio-Temporal
Operation y
Spatio-Temporal
Operation z
5. How to express the semantics of a
spatio-temporal operation?
Expressing User Objectives (1)
• The River Trent is likely to flood – who
should be evacuated?
• Using current approaches, in order to
solve this problem, the user would need to
know:
– The structure of available data.
– The function of available operations.
– How the operations might contribute to
achieve the goal.
Expressing User Objectives (2)
• Would be better if the user could express
the objective in natural language.
• Use Natural Language Processing.
• Data requirements: map against available
domain ontologies.
• Operational requirements: transform into
OWL-S/SWRL expressions.
Transforming Objectives into
OWL-S/SWRL Expressions (1)
• OWL-S is a language for expressing web
service semantics.
• Semantics are expressed by specifying
inputs, outputs, preconditions and results.
• SWRL is a logic language – allows
conditions to be expressed within OWL-S.
Transforming Objectives into
OWL-S/SWRL Expressions (2)
• Map to ontologies of:
– Goals (evacuate, alert, find) – maps to
required outcome.
– Situations (flood, tsunami, famine, infectious
disease) – maps to model of affected area.
• Use OWL-S/SWRL expressions stored in
ontologies to derive semantics of user
objective.
Identifying Implemented Web
Services to Achieve Objectives (1)
• Available Web Services have different
levels of granularity.
• Example high level objective =
FindPeopleToEvacuate.
• Web services at this level are not likely to
be available, so the objective must be
decomposed and semantically compared
with web services.
User Objective
Interpret and
Formalise
OWL-S/SWRL
Process Expression
of Objective
Decompose
OWL-S/SWRL
Process Expression
of Sub-objective
If not semantically equivalent,
continue decomposition
Determine
Semantic
Equivalence
OWL-S/SWRL
Process Expression
of Web Service
The Decomposition and Semantic Matching Process
A Decomposition of the FindPeopleToEvacuate User Objective
A Decomposition of the FindVulnerablePeople User Objective
Identifying Implemented Web
Services to Achieve Objectives (2)
• The semantic matching process may
prompt the user for further input.
• Community Services web service – may
ask which services to evacuate:
– Hospitals?
– Aged Care Homes?
– Libraries?
Semantic Matching between
Services and Sub-Objectives
• Requires ongoing research:
– Logical equivalence rules.
– Classification of spatial operations.
– Logical specification of spatial functions.
Ontological Registries (1)
• Will implement an ontological registry to
demonstrate and test the research.
• Current Spatial Data Infrastructures (SDI)
use registries that are semantically limited.
• An ontological registry will describe the
semantics of all the resources in the SDI.
Registry Resources
• Data
• Web Services
• Ontologies:
– Domain Ontologies
– Goal Ontologies
– Situation Ontologies
• All other information for SDI operation.
Ontological Registries (2)
• Ontological registries store and derive the
relationships between registry resources.
• Will assist in the process of decomposition
and semantic matching between web
services with different levels of granularity.
• Example: if a web service has semantics
that are too general, follow the links to
child web services.
Summary: Dynamic Spatio-Temporal
Analysis for Emergency Management
• The user expresses an objective in natural
language.
• The objective is mapped to ontologies and
transformed into formal expressions.
• The formal expression is semantically
matched with services and progressively
decomposed.
• Outcome: relevant services are executed
and a result provided to the user
KnowledgeScope
• A knowledge infrastructure for eScience.
• A computational framework for:
– expressing;
– managing;
– discovering;
– annotating and
– utilising scientific knowledge.
KnowledgeScope: Knowledge
Modelling
• Scientists create knowledge (publications,
theories, models, data) and tag it to
describe it.
• Scientists use a wiki-based interface to
assist with tagging.
• Scientists can create their own tags within
the tagging upper-ontology.
KnowledgeScope: Knowledge
Infrastructure
• The system generates and updates
networks that connect the resources
together.
• Results in a dynamic, evolving
representation of the scientific knowledge
in KnowledgeScope
KnowledgeScope: Visualisation
• Scientists can view knowledge in
KnowledgeScope on the basis of space
(maps), time (timelines) or themes
(concept maps).
• Knowledge in any of the views can be
filtered according to the other views.
KnowledgeScope: Querying and
Discovery
• Scientists will be able to express goals
and context in natural language.
• The system will interpret the request and
provide analysis and visualisation that is
tailored to requirements.
• The system will also identify other
research that may be of interest based on
the use of similar techniques or patterns in
other areas.
KnowledgeScope:
Enhancements
• Will add richer handling of research
theories, clusters of scientists, paradigms,
schools or thought and other collaborative
aspects to scientific knowledge.
Outcomes
• Collaboration with semantics researchers
to develop KnowledgeScope (grant
application).
• Development of ideas on semanticallyaware dynamic spatio-temporal analysis –
this work will continue...
Thank You!!