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Graph-Based-Literature-Review-Tool
This Network-graph based literature review tool uses the open-source version of Neo4j with Jupyter Notebooks written in Python to import academic literature metadata from a variety of sources including OpenAlex, arXiv, Sematic Scholar and Web of Science. Also incorporated are OpenAI vector embeddings using Neo4j's Vector Search Index capabilities.
#arxiv#embedding-vectors#embeddings#graph-database#neo4j#opeanai
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Graph-Based-Literature-Review-Tool
This Network-graph based literature review tool uses the open-source version of Neo4j with Jupyter Notebooks written in Python to import academic literature metadata from a variety of sources including OpenAlex, arXiv, Web of Science and more. Using a simple data model schema, literature metadata can be quickly imported, aggregated and normalized for analysis.
Jupyter Notebooks using Python and Neoj's Scripting language are available to import data from:
Machine Learning using vector embeddings generated by OpenAI is also available leveraging Neo4j's Vector Search Index capabilities in a simple Jupyter Notebook
This tool is described in the paper "A Network-Graph Based IT Artifact Aiding the Theory Building Process" published by the 2022 Hawaii International Conference on System Sciences (HICSS).
Modeling academic literature data as a network graph helps answer questions involving:
The Jupyter Notebooks are written to work with Neo4j version 5.x and higher with the APOC Library installed. To use Neo4j's Vector Search Index capabilities, Neo4j version 5.11 or higher is needed.