With related-work.net we introduce a scientific platform that is using neo4j to store an open citation graph of research articles and build a social networking application on top of it. We use Google Web Toolkit enhanced by the Model View Presenter framework GWTP.
Related-Work offers the following core functionality (http://www.rene-pickhardt.de/related-work-net-product-requirement-document-released/):
Convenient browsing of scientific literature using
recommendations (personalized) search functions ** (personalized) alerts for new publications
With related-work.net we introduce a scientific platform that is using neo4j to store an open citation graph of research articles and build a social networking application on top of it. We use Google Web Toolkit enhanced by the Model View Presenter framework GWTP.
Related-Work offers the following core functionality (http://www.rene-pickhardt.de/related-work-net-product-requirement-document-released/):
Convenient browsing of scientific literature using
recommendations (personalized) search functions ** (personalized) alerts for new publications.
Enhance scientific communication with Q&A features inspired by StackOverflow.
In this talk we show that graph databases are the natural technology to power not only a social networking application but are the best choice for a question and answer system.
Topics of the talk include:
Design and implementation of a discussion system on top of a social network application using a graph database. Why does it scale better than relational approaches? Why is it more flexible than a document store? Techniques for storing big graphs in neo4j and performing data mining tasks Benchmarks comparing neo4j’s core API to Cypher Query language. How to build personalized graph based search indices Performance boosts by smart client side caching for local graph queries. Currently only a preliminary python based demo is online at dev.related-work.net but we are working hard to get our +10’000 lines of code deployed in the beginning of 2013. That will already include a basic Q & A System as well as various data mining techniques on graphs, recommendation engines and personalized search and auto completion. The source code can be found at: https://github.com/renepickhardt/related-work.net
This is joint work with Heinrich Hartmann (Koblenz), David Shotton (Oxford) and Sun Xinruo (Shanghai Jiao Tong University).
Speakers: Rene Pickhardt