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Project for the Data Visualization subteam during SP18. Visualizing the hierarchical relationship of knowledge on Wikipedia.
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FA18
pythonapp
viz
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README.md

Wikipedia

Current Members: Ziwei Gu (CS/Math '21), Xinqi Lyu (CS '20), Nikhil Saggi (CS '21), Eric Sun (CS/Stats '20), Debasmita Bhattacharya (CS '21), Ellen Chen (CS '20)

Past Members: Jim Li (M.Eng '18), Linnea May (CS '21)

Objective: To model the structure of knowledge on Wikipedia and provide recommendations for a path of learning based on a certain inputted topic.

When learning a new topic, there are two particular challenges one can face:

  1. Upstream knowledge: The user wants to learn a new topic, but doesn't know where to start. For example, a user might want to know how Principal Component Analysis (PCA) works, but they don't know what topics are prerequisite to their understanding.
  2. Downstream knowledge: The user knows the basics of a topic, but wants to learn more. For example, a student has finished their first Linear Algebra course and they want to discover ways they can apply their knowledge.

We aim to solve both these problems and provide a customized path of learning for any user by analyzing the network and similarities of Wikipedia articles and generating a new graph-based visualization.

Prototype Visualization

graph

Randomwalk Visualization

graph

https://cornelldatascience.github.io/Wikipedia/

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