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Accelerate research, brainstorm and discovery.

Nifty.works is Google map for knowledge landscapes: find connections between topics.

Search for one topic to explore its context.
Search for multiple topics to explore connections between them.

What you can do
Sketch in seconds a whole research for brainstorming and knowledge discovery.

Edit connections to represent a subject and share the map on social networks to engage in learning.
Share maps with #ChainLetterKnowledge hastag to see where serendipity goes: your friends can expand them!

Technology

The works at Nifty are mind-maps of whole industrial domains.

Currently, Nifty.works platform exposes the mindmap of generalistic factual knowledge, known in English language.
This mindmap is a knowledge network (also called knowledge graph) where each topic maps its wikipedia article. Browsing through wikipedia hyperlinks will also show a visual map of how things are connected, enhancing knowledge discovery.

The main difference respect to Google Knowledge graph is that you can see search for connections shared in between of your queries, too: have insights to make brainstorming and multi-disciplinary research faster.

Webapp, backend and RESTful APIs are agnostic of the knowledge graph they expose.
If you want to apply the engine and/or data visualisation on your own data, please contact me with a short description.

Say hello to make a feature request. Share your comments and follow this project on facebook page nifty.works.4.learning.

Tell me more
My interest in this project is a first attempt to automate knowledge discovery to get directions tailored to your background.

Make sense of any subject you want or need to learn about - no matter of your background.
If you query the 5 million topics in this discovery engine by group of 5, you have 2.6 10^31 possible sub-graphs: quite a lot.
Next thing is autonomous learning to provide insights about what you aim to know respect to what you knew already: get a personal introduction to the subjects you want to explore.
Tecky people also call it "actualise new knowledge".

I would like to know more about unsupervised learning; crowd-sourcing, patrons or funding opportunities about edutech; new governance models for tech innovation aiming, at the same time, to exponential growth and while decentralizing property rights over network infrastructures enabling growth.

A collective memory
This knowledge graph is a sort of collective memory.
I see a collective memory as a structure that can evolve to spur on understanding how do we (creatively) produce new knowledge, knowledge that organize information and experience.

A next step will be a Q/A companion built upon a collective memory, so to uncover "premium" knowledge structures you struggle get into, and to accompany you based on your personal background.

At this stage, you can visualise knowledge graphs. Some examples: