Why we built the knowledge graph

Somewhere around the hundredth paper, most researchers stop being able to hold their reading in their head. You remember that a paper argued something relevant, that you wrote a note about it, and that a second paper seemed to disagree. What you can't easily tell is where each of those lives, how they relate, or which parts of your reading you never connected to anything at all.

The knowledge graph is our answer to that. It's a map of how the things in your library relate to each other, drawn from what you've already read and written. This post is about why we thought it was worth building, and what it's for.

The problem: connections live in your head

A reference manager knows your papers. A notes app knows your notes. Neither knows that the third paragraph of one note is really about page 214 of a particular paper, or that two of your notes are circling the same idea from different directions.

So the connections that make up a literature review, the "this builds on that" and "this is the evidence for that claim", stay in your memory. Memory is a poor place to keep them. It doesn't scale to a thesis-sized reading list, and it doesn't survive a three-month break.

Many of us used a notes app's graph view for exactly this reason. It was the closest thing to seeing how your thinking fits together. But it only sees the notes. It has no idea a note is really anchored to a passage in a PDF, or that the paper behind it cites five others in your library. We wanted the version where reading, notes and sources are all in one picture.

What it shows you

Open the graph from any paper or note and you get a map centred on that item, showing what's connected to it within a couple of steps: the papers it cites, the notes that link to it, the claims that cite it as evidence.

Different kinds of things look different, so you can read the map at a glance. Papers, concept notes, literature notes, highlight notes, claims, authors and publications each have their own shape and colour, and a key is always on screen.

Every connection also tells you where it came from. Links you made yourself look different from connections found by automatic extraction, and both look different from citations pulled from OpenAlex. That matters because you shouldn't have to trust a line you can't account for. If you only want to see your own thinking, hide the other kinds and the map redraws.

Why that's useful in practice

Seeing what a paper actually connects to. Before you write about a paper, open its map. Which of your notes already mention it? What does it cite that you've also read? Which claim in your notes rests on it? That's a much better starting point than a blank page and a vague memory.

Finding the gaps. The most useful thing on the map is often what's missing. The graph points out orphans: papers that nothing links to and nothing cites, and notes that don't point at any paper. Those are the things you read but never made use of, and the notes that float free of their sources. A thesis is easier to defend when you know where those loose ends are.

Moving through your reading by relationship. Folders force every paper into one place. A map doesn't. Click a paper to read it, a note to edit it, or any node to re-centre the map on it, and you can follow a thread of an argument across your whole library instead of through a folder tree.

Checking a claim against its evidence. If you build claims on your concept notes, the graph shows which papers support each one. Thin evidence is visible as a claim with few lines coming into it.

What it doesn't do

The graph is built from your own library: your papers, your notes, your links, and the citation data from OpenAlex. Opening it doesn't use AI credits, and it doesn't send your library to anyone. It also isn't a verdict. The map shows connections, and it's still your job to decide what they mean.

For very large libraries, the graph starts from a high-level view and lets you expand outwards rather than drawing everything at once, which keeps it usable instead of turning into a hairball.

Doing this in ScholarThread

Open any paper in the reader, or any note, and use the Knowledge graph button in the toolbar. The map opens in a workspace tab next to whatever you're reading, so you can keep the paper and its connections side by side. Click through to a paper, note or claim, hide the connection types you don't want, and check the orphan list for anything you've read but never used.

If you're new to how notes and claims fit together, your note templates are an ontology explains the structure the graph draws on, and the literature review note-taking workflow shows how the notes get written in the first place. The graph is only as rich as the links you and your sources give it, and the more of your reading you connect, the more it has to show you.