Knowledge Graphs for Academic & Independent Research

Researchers drown in PDFs. KnodeGraph reads your library of papers, extracts authors, methods, datasets, theories, and findings, and renders them as one interactive graph you can query and refine. Use it to spot citation clusters, find the missing study, or hand a reproducible map of a field to a co-author.

Why Research teams use KnodeGraph

  • PubMed indexes 35 million+ biomedical citations and adds roughly 1 million per year — manual literature review at that scale is impossible without structured extraction.
  • Semantic Scholar's 2024 corpus tops 200 million papers across all disciplines; even a focused subfield easily contains 5–10K relevant works.
  • Citation networks follow a power-law distribution (Price, 1965): 80% of citations land on ~20% of papers. KnodeGraph surfaces the high-degree nodes so you don't miss the seminal work.
  • Pro tier supports 50,000 nodes per graph — enough to map an entire dissertation literature review (typically 100–300 sources, 2K–6K extracted entities).
  • AI extraction runs via Claude with 100+ language support, so non-English sources (e.g., Arabic medical literature, German engineering reports) join the same graph.
  • Export to JSON or CSV means your graph is portable — drop it into Zotero, Obsidian, Neo4j, or a Python notebook for downstream stats.

How the workflow runs

1.Drop in a folder of PDFs

Upload 20–200 papers at once. KnodeGraph parses each, picks out cited authors, methods (e.g., 'randomised controlled trial', 'meta-analysis'), datasets, and key findings.

2.Review staged extractions

Every entity lands in a staging table first. You skim, merge duplicates ('S. Hawking' = 'Stephen Hawking'), and reject false positives before they hit the graph.

3.Filter by relationship type

Hide everything except 'cites', 'extends', or 'contradicts' edges to see the argumentative structure of the field rather than a hairball.

4.Find your gap

High-degree author nodes with no edges to your topic cluster = under-explored angles. Isolated method nodes = techniques nobody combined yet.

5.Export & cite

Drop the graph into your dissertation as a figure (PNG/SVG export), or hand a JSON dump to a collaborator who wants to extend the analysis.

Why KnodeGraph fits Research workflows

  • •No NLP pipeline to build — Claude does the extraction, you do the curation.
  • •Visual editing means a domain expert (you) can correct the AI's mistakes in seconds, not write Python.
  • •Per-graph isolation: one graph for your dissertation, one for the paper you're reviewing, one for a side project.
  • •Cypher under the hood (FalkorDB) — when you outgrow the GUI, you can run real graph queries against your own data.
  • •Free tier (3 graphs, 100 nodes) is enough to test the workflow on a single seminar's reading list before you commit.

Frequently Asked Questions

Can KnodeGraph handle non-English research papers?

Yes. Extraction runs through Claude, which is fluent in 100+ languages. We've tested it on Arabic medical literature, German engineering reports, and Mandarin computer science papers. The graph itself is language-neutral — entity labels keep their original script, and edges work the same way.

How does this compare to Connected Papers or ResearchRabbit?

Those tools build citation networks from existing metadata (Crossref, Semantic Scholar) — they show you what cites what, but not what's inside the paper. KnodeGraph reads the full text and pulls out methods, datasets, theories, and findings. Use Connected Papers to discover relevant work, then KnodeGraph to map what those papers actually say.

Will I have to clean up a lot of bad extractions?

Some, yes. Claude is good but not perfect — expect to merge name variants ('R. Feynman' / 'Richard Feynman') and drop the occasional hallucinated relation. The staging workflow exists exactly for this. In our pilot tests, a typical 50-paper batch needs about 20–30 minutes of curation before the graph is presentable.

Can I share the resulting graph with collaborators?

Yes — JSON and CSV export are included on every tier. For live collaboration, exporting to Neo4j Aura or hosting on a shared cloud notebook is the path most teams take. KnodeGraph itself is single-user per account today; multi-user shared graphs are on the roadmap.

What's the limit on document size?

Pro processes documents up to 5MB and ~150 pages per upload. For longer monographs, split by chapter — each section becomes its own document but lands in the same graph, so the linking stays intact.

Ready to graph your research work?

Start free with 3 graphs and 100 nodes. Upgrade to Pro for AI extraction, unlimited graphs, and 50K nodes.

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