Project vör : Open Knowledge modeling

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Synopsis

The project is initiated as a dirty hack for crawling and modeling a large volume of open knowledge out there in Wikipedia. Thus, we have a "nearly" complete graph of those knowledge, also obtain an ability to traverse the relations between knowledge topics.

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Infrastructure / Prerequisites

To build and run the knowledge graph engine with vör, you need the following software for the infrastructure.

• OrientDB
• MongoDB

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Setup

Install python 3.x requirements by:

  $ pip3 install -r -U requirements.txt

Install Node.js modules required by the graph visualiser. You may ignore these steps if you are not interested in visualisation.

  $ npm install

Other than registered NPM packages, you also need to install Sigma.js for visualisation. The module is not bundled within this repository.

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1) Download (crawl) wikipedia pages

Execute:

  $ python3 crawl_wiki.py --verbose 

The script continuously and endlessly crawls the knowledge topic from Wikipedia starting from the seeding page. You may change the initial topic within the script to what best suits you. To stop the process, just terminate is fine. It won't leave anything at dirty stat so you can re-execute the script again at any time.

[NOTE] The script keeps continuously crawling and downloading the related knowledge through link traveral. The script never ends unless you terminate it.

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2) Build the knowledge graph

Execute:

  $ python3 build_knowledge.py --verbose --root {PASSWORD} --limit {NUM}

Where {PASSWORD} represents your root password of OrientDB. And {NUM} represents the number of wikipedia topics to process.

What the script does is simply imports the entire raw hefty text knowledge from MongoDB to OrientDB as a big graph. The output graph in OrientDB is built from the following components:

• [1] Vertices : Represent topic / keyword
• [2] Edges : Represent relations between topic-keyword or keyword-keyword.

[NOTE] The script processes the entire data in the collection all the way to the end. This will definitely take large amount of time if you have large data in your collection.

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3) Visualise the knowledge graph

Execute:

  $ node visualise {PASSWORD}

Where {PASSWORD} is your OrientDB root's password. The script downloads the graph data from OrientDB, renders it with appropriate visual figure. After it's done, you can view the graphs as follows.

• [1] Universe of topics graph [html/graph-universe.html].
• [2] Index graph [html/graph-index.html.]

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4) Build Word2Vec model over the crawled data

Execute:

  $ python3 build_wordvec.py --limit {LIMIT} --out {PATH_TO_MODEL}

There should be sufficient amount of the downloaded wikipedia in MongoDB which is done by running crawl_wiki.py. The output is a binary file.

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5) Create topic index

Execute:

  $ python3 build_index.py --limit {LIMIT} --root {PASSWORD}

The script generates another OrientDB collection vorindex which contains all invert-index of the topics and their corresponding keywords. Weights of the edges are calculated by how frequent the word appear in each of the topics.

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Licence

The project is licenced under GNU 3 public licence. All third party libraries are redistributed under their own licences.

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