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# Knowledge Graph Evaluation: Test It Against Real Answers
- URL: https://streamient.com/blog/knowledge-graph-evaluation-test-it-against-real-answers/
- Published: 2026-09-27T06:01:20.000Z
- Updated: 2026-09-27T06:01:20.000Z
- Description: Evaluate a knowledge graph with real workflow questions, scoring both the assistant's response and the evidence it retrieves. Test time, permissions, and unanswerable cases, then track failures and rerun the same questions after changes.
- Author: Nitai

A knowledge graph can contain accurate facts and still give an AI the wrong answer. The system might retrieve an outdated decision, miss the source that explains a relationship, or surface information from the wrong project. To evaluate it, test the answers it produces against questions your team can verify.

A practical knowledge graph evaluation compares each answer with a known answer and checks the evidence behind it. It should also test whether the system retrieves the right facts for the right person and time. That is different from measuring how many records the graph contains or whether a search returned something related.

The framing in [Knowledge Graph Evaluation: Score It Against Your Own Answer](https://renezander.com/blog/knowledge-graph-evaluation/?ref=streamient.com) points toward a useful discipline: judge the context layer by what it helps an AI answer, not by the graph in isolation.

## Build a test set from real work

Start with questions that come up in actual team workflows. Avoid questions written to match the labels or structure of your graph. The test should reflect how people and AI tools ask for information in ordinary work.

For each question, record:

- **The expected answer:** What should a reliable assistant say?
- **The supporting evidence:** Which notes, URLs, emails, or Git records establish the answer?
- **The relevant time:** When was the answer true, and could it have changed?
- **The allowed scope:** Which project, team, or user should be able to retrieve it?
- **The expected uncertainty:** Should the assistant answer, qualify its answer, or say it cannot verify the fact?

For example, a team might ask, "Why did we switch the API to asynchronous processing?" The expected answer should include the decision and its rationale, not just a mention of the API. The evidence might be a decision note linked to a code review or project email. If the decision was later reversed, the test should identify which answer applies now and when the earlier one was valid.

Include questions with different levels of difficulty:

1. **Direct lookup:** "Which database did we choose for the billing service?"
2. **Relationship:** "Why does the billing service use that database?"
3. **Time-sensitive:** "Which database was in use before the migration?"
4. **Source-dependent:** "Where did the team agree to this change?"
5. **Scope-sensitive:** "What is the decision for Project A?" when similar records exist for Project B.
6. **Unanswerable:** "Who approved the change?" when the records do not identify an approver.

The last case matters. A system that confidently fills gaps can look strong on easy questions while being unsafe to rely on.

![Editorial photograph illustrating Score the answer and the retrieval separately  Use two passes. First, score what the assistant said. Then inspect what context the graph retrieved. This helps locate the cause of a](https://streamient.com/content/images/2026/09/6aae1b79ae83fcda1eaa03e4-2a58b4eb32af64f5-inline-1.webp)

## Score the answer and the retrieval separately

Use two passes. First, score what the assistant said. Then inspect what context the graph retrieved. This helps locate the cause of a failure: the graph may have returned the wrong evidence, or the model may have misread good evidence.

A simple answer score can use four levels:

- **0: Incorrect or unsupported.** The answer conflicts with the evidence or invents details.
- **1: Partly correct.** It includes a relevant fact but misses a key qualification, reason, or time boundary.
- **2: Correct but incomplete.** The main answer is right, but the response omits useful supporting context or attribution.
- **3: Correct and grounded.** It answers the question, reflects the evidence and timing, and does not claim more than the records support.

Then assess retrieval with a separate checklist:

| Evaluation check      | What to look for                                                            |
| --------------------- | --------------------------------------------------------------------------- |
| Evidence match        | Did retrieval include the records that support the expected answer?         |
| Provenance            | Can you identify the note, email, URL, or Git record behind each key claim? |
| Time validity         | Did the system distinguish current facts from superseded ones?              |
| Relationship coverage | Did it retrieve enough context to explain why facts are connected?          |
| Scope control         | Did it avoid records the user or project should not access?                 |
| Restraint             | Did it avoid treating missing evidence as proof?                            |

This separation is important. A correct-sounding response is not enough if it came from an unrelated record. Likewise, a good retrieval result does not guarantee that the model will use it correctly.

## Test time, sources, and permissions

Knowledge graphs are useful when they preserve relationships, but relationships can change. Test a fact at more than one point in its history: what was true before a decision, what changed, and what is considered current now? Keep superseded evidence available where appropriate, while making the change in status clear. Otherwise, an old record may compete with a newer one without explaining which applies.

For each time-sensitive test, check whether the answer:

- States the current fact when asked about the present.
- Identifies an earlier fact when asked about the past.
- Uses dates or other time markers when they affect the answer.
- Avoids blending two versions into a claim that was never true.

Provenance is just as important. If an answer says the team agreed to a change, the retrieved context should point to the record that supports that statement. A related code commit may show that a change happened; it does not necessarily prove who approved it or why.

Finally, run scope tests with accounts or roles that have different access. Ask questions about records that should not be visible to a given user and check what the retrieval layer returns. Do not rely only on the model's final wording: restricted information should not enter the retrieved context in the first place.

If your team's policy requires a person to approve new persistent facts, evaluate that write path separately. Check what can be added, who can approve it, and whether a correction or deletion is visible to connected AI tools. Do not assume that a good answer score proves the capture and update process is safe.

![Editorial photograph illustrating Track misses, not just average scores  An average can hide a serious failure. A system that scores well on routine lookups may still expose a restricted record or present a superse](https://streamient.com/content/images/2026/09/6aae1b79ae83fcda1eaa03e4-2a58b4eb32af64f5-inline-2.webp)

## Track misses, not just average scores

An average can hide a serious failure. A system that scores well on routine lookups may still expose a restricted record or present a superseded policy as current. Review each failure by type:

- **Missed evidence:** The supporting record was not retrieved.
- **Stale evidence:** An older fact was treated as current.
- **Missing relationship:** A fact appeared without the reason or decision behind it.
- **Wrong scope:** The answer drew on information outside the intended project or user's access.
- **Unsupported inference:** The assistant stated more than the sources establish.
- **Write-back problem:** A new or changed fact was saved incorrectly or without the required review.

Record the question, expected answer, retrieved sources, actual response, and failure type. After a graph or retrieval change, rerun the same questions. Keeping the test set stable makes it easier to see whether a change fixed one failure while creating another.

You can also add new cases as the team's work changes. New projects, changed policies, and retired decisions create new evaluation questions. Keep a small set of critical cases for every release, then rotate in broader examples from real work.

## Applying the scorecard to a shared AI memory layer

Streamient stores team knowledge across notes, memories, URLs, emails, and Git work in an editable library that MCP-compatible AI clients can access. That gives teams a practical place to test whether context carries across tools and whether answers can be checked against source records. See the [Streamient features](https://streamient.com/features/) for current capture and retrieval capabilities.

For an evaluation, add records that represent real decisions and their sources, then ask the same questions from the AI clients used in your workflow. Check whether the retrieved context fits the user's project and permissions, whether it reflects the relevant time period, and whether a teammate can inspect or correct the underlying record. The [temporal knowledge graph guide](https://streamient.com/blog/temporal-knowledge-graphs-for-ai-agents-with-graphiti/) explains why time and relationships matter; the [context engineering guide for teams](https://streamient.com/blog/context-engineering/) covers evaluation of broader AI workflows.

The goal is not a perfect benchmark score. It is a repeatable way to find where answers lose accuracy, evidence, or scope. If the graph helps an AI answer real questions with verifiable sources, respects time and access boundaries, and leaves room to correct mistakes, it is doing useful work.