Graph-based knowledge synthesis starts with segmentation. You cannot build a usable graph from raw text without choosing the right unit of meaning. Too large, and you lose precision. Too small, and you lose context. The art of segmentation is deciding what a node should represent and how it should behave inside the graph.
Why Segmentation Matters
Every later step—embedding, clustering, querying, summarization—depends on the size and clarity of nodes. You need nodes that can be explained, linked, and recombined without distortion.
If nodes are too broad (full documents), your graph becomes a set of unhelpful monoliths. If nodes are too narrow (single words), you get noise. Most practical systems use intermediate units such as:
- Paragraphs
- Sentence clusters
- Concept segments (2–5 sentences that express one idea)
- Structured triples (subject–predicate–object), often used as a secondary layer
Concept segments often work best. They maintain context, avoid vague generality, and are small enough to link without confusion.
Segmenting for Meaning
You can segment by structure or by semantics.
Structural Segmentation
This uses the natural boundaries in text:- Paragraph breaks
- Headings and subheadings
- Bulleted lists
Structural segmentation is fast, predictable, and easy to reproduce. Its downside is that structure does not always match meaning. A paragraph can contain multiple ideas.
Semantic Segmentation
This tries to isolate a single idea per segment:- Split where topic changes
- Group sentences that explain one mechanism
- Combine sentences that rely on each other
Semantic segmentation can be done manually or with NLP models that detect coherence and topic shifts. The advantage is precision. The risk is inconsistency if segmentation rules are unclear.
Node Typing
Once segments exist, you can classify them:
- Concept nodes: definitions or core ideas
- Detail nodes: elaborations or technical specifics
- Example nodes: concrete cases
- Context nodes: background or framing
Typing makes the graph more interpretable. It also improves validation by preventing illogical edges (for example, an example node shouldn’t explain a concept node in the same way a detail node might).
Edge Design
Edges should describe how two nodes relate. Common edge types include:
- EXPANDS_TO: a broad node leads to a more specific one
- EXPLAINS: a detail clarifies a concept
- ILLUSTRATES: an example anchors an idea
- CONTRASTS: two nodes describe opposing views
- CAUSES: one node leads to another
You can also add edge properties:
- Weight (strength of connection)
- Confidence (how reliable the link is)
- Timestamp (when the link was introduced)
Building the Initial Graph
You can begin with a small set of edges:
- Sequential edges within a source
- Semantic similarity edges based on embeddings
- Explicit relationships extracted from the text
Then you refine. Some edges are merged, some removed, some re-labeled. This iterative approach is essential. A graph is rarely correct on the first pass.
Avoiding Misleading Bridges
If you connect nodes too liberally, you create misleading paths. This is common when nodes are too small or similarity thresholds are too low. A hybrid strategy works best:
- Use concept segments, not single sentences
- Require a minimum semantic similarity threshold
- Validate edges within local context
- Use manual or AI review for high-impact connections
Merging vs. Linking
When two nodes are similar, you can either merge them or link them. Merging reduces redundancy but risks losing nuance. Linking preserves nuance but adds complexity. Many systems do this:
- Link by default with a `SIMILAR_TO` edge
- Merge only when similarity is extremely high and confirmed
This keeps the graph flexible and reversible.
Graph Construction as a Pipeline
A practical pipeline often looks like this:
- Segment the text into nodes
- Generate embeddings for each node
- Create structural edges (sequence, hierarchy)
- Create semantic edges (similarity, clustering)
- Classify nodes and edges
- Refine with feedback and pruning
Each stage adds structure without locking you into a single representation. The graph remains adaptable as new data arrives.
Result: A Map You Can Traverse
When segmentation and graph construction are done well, the graph becomes navigable at multiple scales. You can ask for a high-level overview and drill down into the exact segment that anchors a specific claim. You are no longer stuck in the linear order of the original text.
In short, segmentation and construction are not preprocessing steps. They are the foundation. Get them right and the entire knowledge system becomes coherent. Get them wrong and you build an impressive but misleading web.