project chronos presentation - machine learning italy meetup in turin
TRANSCRIPT
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.
A Citizen Scientist.
Everyone can be a scientist.
Our Mission
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 OrbiterCredits: 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-dataW3C Standards
HOW TO COLLECT AND STORE DATA:
No-SQLPython 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”
creare delle procedure semi-automatizzate (crowd-sourcing dei beta-testers più algoritmi) per il crawling e l’ampliamento della knowledge base?
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)
HELP!
Come è possibile utilizzare le tecniche machine-learning per:
proposte per unsupervised machine-learning?
GRAZIE PER L’ATTENZIONE
>>>> 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
>>> Cerchiamo programmatori Python e Javascript Full-Stack, nonché data scientists che vogliano scommettere sul progetto e cresecere con noi
>>>> [email protected]
projectchronos.org
>>>> [email protected]