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tjkerby merged 3 commits into from
Apr 24, 2026
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Fix NSF grant number and format bibliography titles #47

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38776be
Update paper.md
kmoon3 Apr 8, 2026
2b965ba
Fix NSF grant number in acknowledgements
tjkerby Apr 15, 2026
fd89dcb
Format specific terms in bibliography titles
tjkerby Apr 24, 2026
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12 changes: 6 additions & 6 deletions paper/paper.bib
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Original file line number Diff line number Diff line change
Expand Up @@ -209,7 +209,7 @@ @inproceedings{yao2022react
}

@article{boettiger2015docker,
title = {An introduction to Docker for reproducible research},
title = {An introduction to {Docker} for reproducible research},
author = {Boettiger, Carl},
journal = {ACM SIGOPS Operating Systems Review},
volume = {49},
Expand All @@ -230,7 +230,7 @@ @misc{docker2024
}

@misc{fastapi2024,
title = {FastAPI: Modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints},
title = {FastAPI: Modern, fast (high-performance), web framework for building APIs with {Python} 3.6+ based on standard {Python} type hints},
author = {{FastAPI Contributors}},
year = {2024},
url = {https://fastapi.tiangolo.com/},
Expand All @@ -246,7 +246,7 @@ @misc{rabbitmq2024
}

@misc{ollama2024,
title = {Ollama: Get up and running with Llama 3.2, Mistral, Gemma 2, and other large language models},
title = {Ollama: Get up and running with {Llama} 3.2, {Mistral}, {Gemma} 2, and other large language models},
author = {{Ollama Team}},
year = {2024},
url = {https://ollama.com/},
Expand Down Expand Up @@ -284,7 +284,7 @@ @article{rothacher2023eleven
}

@article{merkel2014docker,
title = {Docker: lightweight linux containers for consistent development and deployment},
title = {Docker: lightweight {Linux} containers for consistent development and deployment},
author = {Merkel, Dirk},
journal = {Linux Journal},
volume = {2014},
Expand All @@ -310,7 +310,7 @@ @misc{llamaindex2026rag
}

@misc{haystack2026,
title = {Get Started (Haystack Documentation)},
title = {Get Started ({Haystack} Documentation)},
author = {{deepset Haystack}},
year = {2026},
url = {https://docs.haystack.deepset.ai/docs/get-started},
Expand Down Expand Up @@ -342,7 +342,7 @@ @misc{microsoft2026graphrag
}

@misc{neo4j2026graphrag,
title = {neo4j/neo4j-graphrag-python: Neo4j GraphRAG Package for Python},
title = {neo4j/neo4j-graphrag-python: Neo4j GraphRAG Package for {Python}},
author = {{Neo4j}},
year = {2026},
url = {https://github.com/neo4j/neo4j-graphrag-python},
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4 changes: 2 additions & 2 deletions paper/paper.md
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Expand Up @@ -26,7 +26,7 @@ bibliography: paper.bib

# Summary

Large language models (LLMs) are widely used in research workflows but struggle with hallucinations, short context windows, and weak reproducibility in literature reviews [@Ji2023; @Huang2025]. Nexarag is a modular, open‑source platform that lets researchers curate, visualize, and share custom knowledge graphs (KGs) from academic sources stored in Neo4j [@neo4j2024database]. Through native support for the Model Context Protocol (MCP), any MCP‑compatible LLM can access these curated KGs for controllable, reproducible context injection [@anthropic2024mcp; @mcp2024github]—including fully private, air‑gapped deployments via containers [@boettiger2015docker]—so teams can explore literature more deeply and transparently. Nexarag provides interactive graph/semantic visualizations using Cytoscape.js and D3 [@franz2023cytoscape; @bostock2011d3].
Large language models (LLMs) are widely used in research workflows but struggle with hallucinations, short context windows, and weak reproducibility in literature reviews [@Ji2023; @Huang2025]. Nexarag is a modular, open‑source platform that lets researchers curate, visualize, and share custom knowledge graphs (KGs) from academic sources stored in Neo4j [@neo4j2024database]. Through native support for the Model Context Protocol (MCP), any MCP‑compatible LLM can access these curated KGs for controllable, reproducible context injection [@anthropic2024mcp; @mcp2024github]—including fully private, air‑gapped deployments via containers [@boettiger2015docker]—so research teams can explore relevant literature more deeply and transparently. Nexarag provides interactive graph/semantic visualizations using Cytoscape.js and D3 [@franz2023cytoscape; @bostock2011d3].


# Statement of need
Expand Down Expand Up @@ -80,6 +80,6 @@ All AI-generated material was explicitly reviewed by at least one author, and al

# Acknowledgements

We acknowledge the open‑source ecosystems behind Neo4j, Cytoscape.js, D3.js, RabbitMQ, FastAPI, Ollama, and the Model Context Protocol, as well as contributors and users who provided feedback during development. This research was supported in part by the NSF under Grant 221235.
We acknowledge the open‑source ecosystems behind Neo4j, Cytoscape.js, D3.js, RabbitMQ, FastAPI, Ollama, and the Model Context Protocol, as well as contributors and users who provided feedback during development. This research was supported in part by the NSF under Grant 2212325.

# References
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