Knowledge Graphs in Business — Relationships, Evidence and Practical Uses

A knowledge graph represents entities and the relationships between them. In a business setting, entities might include people, projects, products, documents and processes. The graph provides a way to follow connections that would otherwise be scattered across records.

The value comes from reliable relationships and useful questions. A graph does not automatically discover every connection, stay current or establish that a business is compliant.

Nodes, relationships and evidence

A node represents an entity. A relationship connects two entities and describes the connection. For example, a hypothetical project graph could record:

• Employee A works on Project B.
• Project B uses Technology C.
• Document D describes Project B.
• Team E owns the process described by Document D.

Store the source, effective date and confidence or review status of a relationship where relevant. A relationship extracted by a model should be distinguishable from one confirmed by an authoritative system or a human reviewer.

Define a schema for the questions you need to answer

A schema or ontology specifies entity types, allowed relationships and relevant attributes. Start with the business questions rather than attempt to model the entire organisation.

A project-assignment tool may need people, skills and projects. A research tool may need publications, claims, authors and evidence. A compliance register may connect requirements, controls, owners and evidence, while keeping the legal assessment with qualified reviewers.

Decide how to handle ambiguous names, duplicate entities, changing roles and contradictory evidence. Without those rules, a graph can make inaccurate connections look authoritative.

How information enters the graph

Possible inputs include structured databases, approved exports, documents and manually reviewed records. Integration work may involve parsing, entity extraction, identity matching and mapping to the schema.

Automatic extraction can propose entities and relationships, but quality varies with language, source material and the model. Check a sample against labelled examples and establish review rules for high-impact relationships.

Updates and deletions matter as much as ingestion. If a person changes role or a document is withdrawn, derived relationships may need to change too. Keep enough provenance to explain why a relationship exists.

Practical uses

These scenarios describe possible applications, not verified customer outcomes.

Research and development

A graph can link publications, concepts, authors and projects so a researcher can inspect related work. The supporting sources still need review before drawing scientific or commercial conclusions.

Finding expertise

A project team can look for people associated with relevant work or skills. Absence from the graph does not prove absence of expertise; participation and source coverage should be understood before the information is used in staffing decisions.

Understanding customer context

A graph can connect a customer with products, support cases and approved feedback records. Whether this improves service depends on data quality, access controls and the actions the team takes.

Tracing requirements

Requirements can be connected to processes, controls, documents and owners. This helps organise evidence and investigate changes. A complete-looking graph is not proof that every legal obligation has been identified or met.

Graphs and RAG

RAG retrieves information and supplies it to a language model. A graph can contribute structured context, such as which documents relate to the same project or which version of a policy applies.

Graph retrieval may help with questions that require following relationships. It adds modelling and maintenance work, however, and does not guarantee a complete or correct answer. Compare it with keyword, vector and hybrid retrieval on the same evaluation set.

For a hypothetical question about a system migration, a graph might connect the system to its data categories, owners and approved requirements. The answer must still cite the underlying evidence and distinguish established facts from assumptions.

Choosing a graph architecture

Consider query patterns, data volume, update frequency, access rules and operational skills. A graph database may be useful, but a smaller graph can sometimes be represented using simpler structures. A relational database may already support the questions effectively.

Plan for schema changes, backup and recovery, monitoring and the cost of refreshing derived relationships. Choosing a managed service transfers some infrastructure tasks to a provider; it does not remove responsibility for data, permissions and the business use.

Evaluate before claiming value

Assess entity matching, relationship accuracy, source coverage and how often users can complete their tasks. Include false connections and missing relationships in the review.

If the aim is faster research or onboarding, measure the existing workflow and compare it with a pilot. Account for time spent maintaining the graph and checking its results. The same implementation can be valuable for one question and unnecessary for another.

Budget and timeline depend on source access, schema complexity, integrations, review requirements and operation. They need an individual estimate; document count alone does not establish cost or payback.

BeHive’s research context

BeHive is QA10’s open-source research engine under the MIT licence. It supports source-based research, claim assessment and exploration of relationships between entities. Installation is available through pip install behive.

This research context should be distinguished from a custom enterprise graph with company-wide permissions, business-system integrations and operational commitments. Those requirements need to be specified and evaluated separately for the project.

FAQ

Does a graph replace the existing knowledge base?

It can provide another way to navigate information while documents remain in their original systems. The architecture and integrations determine how the graph relates to those sources; replacement is not inherent to the technology.

How long does a proof of concept take?

It depends on the questions, source access, schema and quality criteria. Agree a limited scope and what evidence would justify further work. No fixed duration or return is implied by choosing a graph tool.

Who maintains the graph?

Assign responsibility for sources, schema changes, extraction quality, access controls and infrastructure. The workload depends on the number and frequency of changes and the consequences of incorrect relationships.

Can a graph contain personal or confidential information?

Yes, which means access, lawful processing, retention and applicable rights must be designed for the actual data. Relationships and metadata can be sensitive even when full documents are not stored in the graph.

Can it use Polish documents?

It can, but extraction and entity matching should be tested on representative Polish material. Inflection, abbreviations and ambiguous names can affect quality. Do not assume that performance transfers unchanged from an English benchmark.

Discuss a specific question

Contact QA10 with the question you need to answer and the sources involved. A defined requirement can be scoped through Q-Dev; application development following an audit belongs to Q-Journey.