Robert Rallo discusses knowledge extraction from nanosafety data. Data is structured, semi-structured, and unstructured and requires management and processing. Knowledge extraction involves analysis, data mining, data management, and modeling to develop nano-QSAR models, predict fate and transport, and inform risk assessment and safe-by-design from the data warehouse. The nanosafety knowledge infrastructure aims to share this knowledge with academia, industry, and regulators.

1. Knowledge Extraction from Nanosafety Data
Robert Rallo
Universitat Rovira i Virgili, Catalunya
Nanosafety Knowledge Infrastructure (NSKI)
Brussels, January 26, 2016

2. Nanosafety (big) data
Structured Semi-Structured Unstructured
Management & Processing complexity

3. Knowledge extraction
Analysis
&
Data
Mining
Data
Management
Modeling
Use
DataInformationKnowledge
Nano-(Q)SAR, Fate &Transport, …
Nanodescriptors
Risk Assessment, Safe-by-design, …

4. Authentication&Access
Control
KI needs
Academia
IndustryRegulators
General
public
NANOSAFETY DATA WAREHOUSE
(structured, semi-structured, unstructured)
Data Curation and QA/QC protocols
LIMS
Real-time
monitoring
Data providers
DATA PROCESSING SERVICES
Literature
data
NANOSAFETY
ONTOLOGY
DATA
SHARING
STANDARDS
Analytics
(data mining, quantum chemistry, molecular modeling,…)
Modeling
(machine learning,statistical models, mechanistic models, …)
Execution
(Descriptor calculation,toxicity prediction, …)

5. Acknowledgement
• EU FP7 MODERN, Contract No. 309314
• 2014SGR 1352, Generalitat de Catalunya