Poster for the Knowledge Extraction for the Web of Things (KE4WoT) Challenge: Co-located with The Web Conference 2018 (WWW 2018) Source: Semantic Web of Things Project, Poliba, Italy http://sisinflab.poliba.it/swottools/

1. Toward a Semantic Web of Vehicles
Michele Ruta, Floriano Scioscia, Filippo Gramegna, Saverio Ieva,
Giuseppe Loseto, Agnese Pinto, Eugenio Di Sciascio
Polytechnic University of Bari, Via E. Orabona, 4, I-70125 Bari, Italy
[name.surname]@poliba.it
Knowledge Extraction for the Web of Things (KE4WoT) Challenge
co-located with The Web Conference 2018 - Lyon, France, April 23-27, 2018
Semantic Web of Things
Semantic Matchmaking for
Discovery and Detection
Smart Cars
Knowledge-based system for on-board
diagnostics and driving assistance
Knowledge-based real-time
car monitoring
iDriveSafe prototype for iOS
MAFALDA (MAtchmaking Features for
mAchine Learning Data Analysis)
Ontology-based model training in SWoT MAFALDA for smart cars
Semantic-enhanced VANETs
 Emerging ICT vision: Internet of Things + Semantic
Web approaches
 Embed intelligence into everyday objects and
locations
 Take into account pervasive computing constraints
 Optimize queries and reasoning
 Ubiquitous Knowledge Bases (u-KBs)
 Exploiting Concept Abduction and Concept Contraction
non-standard inferences [Ruta et al., Web Intelligence and Agent
Systems, 9(3), 2011]
 Approximate matches and explanation of outcomes
 Mini-ME reasoner
 Mobile and embedded computing target architectures
SEMANTIC CHARACTERIZATION AND MATCHMAKING
DATA MANIPULATION
DATA GATHERING
Accelerometer
Magnetometer
GPS
ODB-II
Parameters
Google Maps
CloudMade
Google Weather
 Integration of multiple information sources
 Data fusion algorithms to identify high-level events
and conditions
 Matchmaking to discover possible risk factors
Vehicle parameters (e.g., emissions, gear setting, fuel
consumption) annotated w.r.t. an ontology for driving safety
Vehicle Status Driver and ContextOBD-II Data
Urban Route
Even Pace
Urban Route
Imprudent
Extra-Urban Route
Imprudent
 Semantic-enhanced data mining on sensor streams
 ML classification treated as a resource discovery
based on non-standard reasoning
 Outputs endowed with machine-understandable
descriptions
 Data corpuses translated to OWL-based datasets
 Reusing LOV vocabularies: M3-lite and Traffic Danger
 Feature to concept mapping
 Dynamic annotation of streaming samples
 Output model includes most relevant axioms
(significance threshold)
 Road surface, traffic congestion, driving style outputs
 Camera viewfinder: classification outputs without
looking away from the road
 Dataset publicly available on github.com/sisinflab-
swot/mafalda
Vehicular Ad-hoc Networks (VANETs) Concept Integration Problem (CIP)
for knowledge fusion
Semantic-enhanced VANETs
V2
V3
V1
Broadcast range Data propagation
V4
V1
V5V3
V4
Generated data Relayed data
(a) First BP (b) Second BP
1
2
3
4
5
Settings1
M’ (My) elements
(N and not others)
2
C’ (Confirmed)
elements (N and
others)
3
X’ (Clash)
observations (N
inconsistent with
others)
4
E’ (External)
elements (others
but not N)
5
 managing incompleteness
 reconciliating inconsistencies
 Moving vehicles (OBUs) and fixed stations (RSUs)
 Gathered environmental information to detect
dangers
 Bidirectional dissemination scheme for data
generated and relayed by a OBU - Broadcast Period
(BP) time span
 Efficient dissemination of annotations, compliance
with VANET standards
 Annotations of a vehicle in a Semantic Packet (SP)
C X M E
P1 T T T
P2 T T T
P3 T T T
P1
’ T
P4 T T T
P5 T T
P4
’ T
Multiple sensors and Electronic Control Units (ECUs)
connected on Controller Area Network (CAN) bus.
On-Board Diagnostics (OBD-II) protocol and connector
 real-time access to vehicle parameters
 wired (RS-232, USB) and wireless
(Bluetooth, IEEE 802.11) scan tools
 Augmented Machine Learning on low-level data for
Advanced Driver Assistance Systems (ADAS) and
autonomous driving
 Knowledge Representation and Reasoning for high-
level information extraction and processing
 quick adaptating
 robustness w.r.t inaccuracies