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Project Chronos 
Machine Learning Italy 
Turin, 3 December 2014 
projectchronos.org
Project Chronos’ Milestones 
➔ 11 – 13 April '14: NASA's Hackathon SpaceApps 2014 the Group 
is born: 2nd Place, “Best Mission Concept”. 
➔ 6th May '14: Global Award Finalists for the category “Best 
Mission Concept” 
➔ October ‘14: First Chronos Knowledge Base and Xploration video-animation 
is realized 
➔ ➔ Nov ‘14: first collaboration on Climate Change 
monitoring. Project Chronos partners with 
iSeeChange
Our Mission 
Giving people new tools 
to research knowledge.
Our Mission 
Everyone can be a 
scientist. 
A Citizen Scientist.
What do we know about 
space? 
How do we look at it? 
Knowledge about the Universe relies mostly on the 
analysis of the electromagnetic radiation collected 
from Space. But also on particle detection, plasma 
investigation, soil analysis of the celestial bodies of the 
Solar System, and so on.
What can a spacecraft see, 
and how? 
Spacecraft house the vast majority of detectors and 
sensors capable of performing measurements on 
physical entities in Space and around the Earth.
An example 
Rosetta’s Orbiter Credits: Rosetta Blog
Rosetta’s Orbiter and its instrumentation 
Spectrometer: 
detects visible & 
infrared light 
Working 
mechanism? 
Scientific 
Goals? 
Online datasets? 
Credits: ESA
MAIN CHALLENGES IN IMPLEMENTATION 
HOW TO DESCRIBE AEROSPACE KNOWLEDGE: 
Agencies’ Open-data 
W3C Standards 
HOW TO COLLECT AND STORE DATA: 
No-SQL 
Python and libraries 
Crowd-sourcing (citizen science) 
“machine-learned crawling”?
Semantic Web: Knowledge Base 
NASA-STI Open-Data: a hierarchical 
taxonomy containing terms and concepts for 
aerospace technical and scientific papers. 
Divisions > Subjects > Scopes > Keywords 
Chronos Domain: Missions, Data, URLs and 
News from Agencies websites. 
Chronos Sensors: A Taxonomy For Sensors
AEROSPACE ACTIVITIES WITH W3C STANDARDS
Semantic Web: 
Ontology Development 
Chronos Domain: Class and Relations 
designed for describing Missions, 
Objectives, Destinations, DBpedias’, etc. 
Chronos Sensors: Taxonomy of Sensors, to 
describe astrophysics and other scientific 
activities in space
Semantic Web: Knowledge Base 
RDF (w3c) 
SKOS (w3c) 
OWL (w3c) 
SCHEMA.ORG (schema) 
TRIPLES-TURTLE (data format) 
XML (data format) 
JSON-LD (data format) 
MONGOdb (datacloud) 
PYTHON3.3 (scripting)
Most meaningful semantic interactions (schema) 
keywords subjects
Graph representation 
printed with NetworkX Python Library
XplorationApp: Mining the Cloud 
and learn-by-playing 
https://www.youtube.com/watch?v=ZMFZv_yYCBI 
or look in YouTube for “XplorationApp Project Chronos”
HELP! 
Come è possibile utilizzare le tecniche machine-learning per: 
creare delle procedure semi-automatizzate (crowd-sourcing 
dei beta-testers più algoritmi) per il crawling e l’ 
ampliamento della knowledge base? 
proposte per unsupervised machine-learning? 
migliorare le procedure semi-automatizzate per il 
riconoscimento semantico dei termini nelle pagine Web e loro 
automatico salvataggio nella knowledge base con le corrette 
relazioni? (natural language processing)
>>>> lorenzo@projectchronos.org 
>>> Cerchiamo programmatori Python e Javascript 
Full-Stack, nonché data scientists che vogliano 
scommettere sul progetto e cresecere con noi 
>>>> cominceremo a breve un funding round per 
la fase di seeding. Potete richiedere di 
ricevere una copia del business plan. 
>>>> stiamo pensando anche ad una campagna di 
crowd-funding su una delle maggiori 
piattaforme a livello globale 
GRAZIE PER L’ATTENZIONE 
projectchronos.org 
>>>> jacopo@projectchronos.org

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Project Chronos Presentation - Machine Learning Italy MeetUp in Turin

  • 1. Project Chronos Machine Learning Italy Turin, 3 December 2014 projectchronos.org
  • 2. Project Chronos’ Milestones ➔ 11 – 13 April '14: NASA's Hackathon SpaceApps 2014 the Group is born: 2nd Place, “Best Mission Concept”. ➔ 6th May '14: Global Award Finalists for the category “Best Mission Concept” ➔ October ‘14: First Chronos Knowledge Base and Xploration video-animation is realized ➔ ➔ Nov ‘14: first collaboration on Climate Change monitoring. Project Chronos partners with iSeeChange
  • 3. Our Mission Giving people new tools to research knowledge.
  • 4. Our Mission Everyone can be a scientist. A Citizen Scientist.
  • 5. What do we know about space? How do we look at it? Knowledge about the Universe relies mostly on the analysis of the electromagnetic radiation collected from Space. But also on particle detection, plasma investigation, soil analysis of the celestial bodies of the Solar System, and so on.
  • 6. What can a spacecraft see, and how? Spacecraft house the vast majority of detectors and sensors capable of performing measurements on physical entities in Space and around the Earth.
  • 7. An example Rosetta’s Orbiter Credits: Rosetta Blog
  • 8. Rosetta’s Orbiter and its instrumentation Spectrometer: detects visible & infrared light Working mechanism? Scientific Goals? Online datasets? Credits: ESA
  • 9. MAIN CHALLENGES IN IMPLEMENTATION HOW TO DESCRIBE AEROSPACE KNOWLEDGE: Agencies’ Open-data W3C Standards HOW TO COLLECT AND STORE DATA: No-SQL Python and libraries Crowd-sourcing (citizen science) “machine-learned crawling”?
  • 10. Semantic Web: Knowledge Base NASA-STI Open-Data: a hierarchical taxonomy containing terms and concepts for aerospace technical and scientific papers. Divisions > Subjects > Scopes > Keywords Chronos Domain: Missions, Data, URLs and News from Agencies websites. Chronos Sensors: A Taxonomy For Sensors
  • 11. AEROSPACE ACTIVITIES WITH W3C STANDARDS
  • 12. Semantic Web: Ontology Development Chronos Domain: Class and Relations designed for describing Missions, Objectives, Destinations, DBpedias’, etc. Chronos Sensors: Taxonomy of Sensors, to describe astrophysics and other scientific activities in space
  • 13. Semantic Web: Knowledge Base RDF (w3c) SKOS (w3c) OWL (w3c) SCHEMA.ORG (schema) TRIPLES-TURTLE (data format) XML (data format) JSON-LD (data format) MONGOdb (datacloud) PYTHON3.3 (scripting)
  • 14. Most meaningful semantic interactions (schema) keywords subjects
  • 15. Graph representation printed with NetworkX Python Library
  • 16. XplorationApp: Mining the Cloud and learn-by-playing https://www.youtube.com/watch?v=ZMFZv_yYCBI or look in YouTube for “XplorationApp Project Chronos”
  • 17. HELP! Come è possibile utilizzare le tecniche machine-learning per: creare delle procedure semi-automatizzate (crowd-sourcing dei beta-testers più algoritmi) per il crawling e l’ ampliamento della knowledge base? proposte per unsupervised machine-learning? migliorare le procedure semi-automatizzate per il riconoscimento semantico dei termini nelle pagine Web e loro automatico salvataggio nella knowledge base con le corrette relazioni? (natural language processing)
  • 18. >>>> lorenzo@projectchronos.org >>> Cerchiamo programmatori Python e Javascript Full-Stack, nonché data scientists che vogliano scommettere sul progetto e cresecere con noi >>>> cominceremo a breve un funding round per la fase di seeding. Potete richiedere di ricevere una copia del business plan. >>>> stiamo pensando anche ad una campagna di crowd-funding su una delle maggiori piattaforme a livello globale GRAZIE PER L’ATTENZIONE projectchronos.org >>>> jacopo@projectchronos.org