urban knowledge extraction, representation and reasoning as a...
TRANSCRIPT
Urban Knowledge Extraction, Representation and Reasoning
as a Bridge from Data City towards Smart City
Jaime De-Miguel-Rodríguez1, Juan Galán-Páez1
Gonzalo A. Aranda-Corral2, Joaquín Borrego-Díaz1
1 Dept. Computer Science and Artificial Intelligence. University of Sevilla-Spain2 Dept. Information Technologies. University of Huelva-Spain
Outline
• Motivation
• Formal Concept Analysis (FCA)
• Case 1: Self-City
• Case 2: Smart Pedestrian Mobility
• Case 3: Semandal
• Conclusions and future work
Motivation
• Massive availability of poorly structured data
• WWW, opendata, crowdsourced etc.
• Obtaining structured knowledge from digital information aids to:
• Obtain information on cities structure and dynamic
• Understand how citizens live and work within the city
• Formal Concept Analysis (FCA) can be used to organise knowledge and extract new concepts from rear data
Formal Concept Analysis
• Automated conceptual learning theory
• Detects and describes regularities and
structures of concepts
• Also provides data reasoning methods
• Logical implications between attributes
(Stem Basis, Luxenburger Basis)
• Basic data structures:
• The Formal Context (O,A,I)
• The Concept Lattice
Formal Context
• A formal context (O,A,I) consists on:
• A set of objects (O)
• A set of qualitative attributes (A)
• A relation I between objects and attributes
• Basic operations. Extension and Intension
Intension of {Bream}
is {Coast, Sea}
Extension of {Sea}
is {Bream, Sparus,
Eel}
Formal Concept
• A concept is a pair (X,Y) where:
• X is a subset of O
• Y a subset of A
• The Intension (common attributes) of X is Y and the
extension (common objects) of Y is X
A concept
Concept Lattice
• The Concept Lattice contains all concepts within the context
New Classes
All concepts within the concept lattice:
C1 := ({Escatofagus, Eel, Carp, Bream, Sparus},{}) [Any fish]
C2 := ({Escatofagus, Eel, Carp},{River}) [River fish]
C3 := ({Escatofagus, Eel, Bream, Sparus},{Coast}) [Coast fish]
C4 := ({Escatofagus, Eel},{River, Coast}) [Estuary fish]
C5 := ({Eel, Bream, Sparus},{Coast, Sea}) [Sea fish]
C6 := ({Eel},{River, Coast, Sea}) [Euryhaline fish]
Estuary fish
Euryhaline fish
Association Rules
• Stem basis is designed for true implications only.
• It does not take any exception into account.
• Association Rules (Luxenburger Basis):
• Support: Attribute set frequency (# covered objects)
• Confidence:
How we use FCA?
Case studies
1. Self-City: Estimating Social Perception on Housing Values
2. Exploiting Pedestrian Behavior for Smart Mobility
3. Semandal: Exploiting Real Time Government Information
SELF-CITYPEDESTRIAN
SIMULATIONSEMANDAL
OBJECTS Houses Positions News
ATTRIBUTESSize, price, trend, proximity
values etc.
Closer to destination?,
obstacle, other.Categories, keywords
AIMUnderstand socio-economic
dynamics
Behaviour mining,
pedestrian simulation in new
scenarios
Non-supervised clustering
(news classification)
KNOWLEDGE
EXTRACTEDSocio-economic patterns
Patterns of pedestrian
trajectories
Semantic and hierarchical
organization of news
METHODOLOGYApply FCA per street and
compare lattices
Use association rules as
multi-agent behavior
Use FCA lattice as a
hierarchical structure of
labels
Methodology
Case 1: Self-City(self-city.com)
A context for Real State infoAttributes:
• Dimensions (small, medium, big)
• Price (very low, low, medium, high, very high)
• Price decreased/increased in the last 3 months
• Price with respect to other homes in the neighbourhood (more expensive than average, average, cheaper than average)
• Amount of other homes for sale in the surroundings (none, few, lots)
• Access to public transport
Objects:
• Houses
A Concept Lattice for Seville
• Collection of 6000 (approx.) for sale homes in the city of Seville
• Global Concept Lattice with all the info aggregated
• Subsets (by streets, zones, ...) can be considered for a more detailed analysis
Concept Lattices by streets
• Analysing street dynamics:
• Comparison between concept lattices associated to different streets
Av. Kansas City
Av. República Argentina
Similar lattices:
- A significant difference:
Home’s dimensions
Idea:
- Analyse knowledge basis
Isolating
differences
• In order estimate the influence of House
dimensions, Attribute associate to big flats is
permuted in Avda. R. Argentina with the normal
size attribute
• The resultant lattice is very similar to the
associated to Av. Kansas City
• However, it is interesting how similar these
implication basis are
Estimating true association rules in
both
• Luxenburger (Kansas, 85%) ==> Lux(Republica’, 97%)
• Luxenburger (Republica’, 100%) ==> Lux(Kansas, 94%)
• That is, Knowledge about real estate of both streets are essentially similar
Conclusion of the comparison
• Two different areas in the city, apparently very
different
• They have same behavior from a socio-
economic point of view of real estate markets
(and the available information)
• The argumentation about why it occurs is aim of
urbanism specialists
Using the pattern within the District
Case 2Exploiting Pedestrian Behavior for Smart Mobility
Motivation
• Av. Constitución, Sevilla
• Recently redesigned
• Potential problems for pedestrian mobility
• Bike way and tramway
• Terraces
• Temporary exhibitions
Observations
Data on pedestrian
mobility (non-
aggregated)
Artificial models of
mobility
Discrete Agent-
Based modelFormal
Context
Attribute
selection & data
collection
Aim & Methodolgy
Mobility patterns
(implications)Inference
engine
Eva
lua
tion
New scenarios
- Attributes -
Knowledge representation for pedestrians
• Qualitative observable features (attributes)
describing pedestrian neighbourhood
• Qualitative distances to destination
• Empty space?
• Obstacle/zone type
• Other features (social, environmental, ...)
• The feature selection is performed by an
observer in each case
• Similar to pedestrian’s perception of its
neighbourhood
P
++
---+ -
+
=
=
Destination
- Objects -
A Formal Context for Pedestrians
video
Goal: Assessment of urban
planning
• Agent-based qualitative modelling of real urban
scenarios provides a simple but robust sandbox
for:
• Detecting and isolating existing planning flaws
• Assessing the impact of hypothetical urban
planning changes before implementing them
• Simulating and understanding pedestrian
behavioural patterns
Case 3Exploiting Real Time Government Information
Introduction
• Semandal is focused on the municipalities of
Andalucía, Spain.
• This scope was chosen to reduce the dimensions of
vocabularies, ontologies, and, even, databases.
• For this, we use Formal Concept Analysis.
Categories
• Attributes: categories + keywords
• Objects: news
• Refill all news adding all abstract concepts to
existent concrete concepts (superclasses)
Classification
• First step: Select the most
important words for each category
and mostly in that category (not
all)
• Creating a graph with resulting
words and categories.
• Some categories look like well
defined
Classification
• Build the formal context:
• Attributes are categories and words
• Objects are news
• Relations are words and categories previously extracted.
• We could build an emergent ontology from this. (out of
scope)
• Set of rules (association rules) obtained by means of FCA
Experiments
• We chose 2 news randomly
[Noticia 1] “El novillero de Écija Antonio David, proclamado triunfador de la V feria de
novilladas de promoción la granada de plata”
Context A
Turismo
Juventud
Context B
Turismo
Cultura
Context C
Festejos
Experiments
Noticia 2] “El ayuntamiento da luz verde para la construcción de otras 75 viviendas protegidas”
Context A
Vivienda
Context B
Turismo
Servicios sociales
Context C
Servicios sociales
Obras
SELF-CITYPEDESTRIAN
SIMULATIONSEMANDAL
OBJECTS Houses Positions News
ATTRIBUTESSize, price, trend, proximity
values etc.
Closer to destination?,
obstacle, other.Categories, keywords
AIMUnderstand socio-economic
dynamics
Behaviour mining,
pedestrian simulation in new
scenarios
Non-supervised clustering
(news classification)
KNOWLEDGE
EXTRACTEDSocio-economic patterns
Patterns of pedestrian
trajectories
Semantic and hierarchical
organization of news
METHODOLOGYApply FCA per street and
compare lattices
Use association rules as
multi-agent behavior
Use FCA lattice as a
hierarchical structure of
labels
• Knowledge Engineering techniques can
enhance city services towards Smart Cities
• FCA is a qualitative analysis and reasoning tool
valid for urban, inter-urban and intra-urban
contexts
• Future work is oriented to acquire better urban
knowledge mined from citizen’s sentiments and
opinions
Conclusions
