TL;DR
Knowledge graph RAG retrieves connected entities and relationships before an LLM writes its answer, instead of relying only on similar text. That lets it answer multi-hop questions that plain vector search misses. The ontology is the part that makes those connections trustworthy. It defines which entity types exist, how they may relate, and what business terms mean. Without it, an LLM-built graph can still link the wrong records or mix up two suppliers with similar names. Start with a small ontology for a few high-value questions, combine graph traversal with vector search, and measure the results against your current RAG baseline before you scale.
Key Takeaways Knowledge graph RAG grounds LLM answers in connected facts, which vector similarity alone cannot represent. An ontology is the schema of the graph, so it decides how reliable every traversal and answer will be. Ontology-driven graphs suit governed operational questions, while LLM-extracted graphs like Microsoft GraphRAG suit corpus-wide exploration. Hybrid retrieval, using graph traversal for structure and vector search for text evidence, beats either method alone on most enterprise questions. Freshness, entity resolution, and access control decide whether a graph RAG system survives production. Graph RAG is worth its cost only for multi-hop, relationship-heavy questions, so test it against a vector RAG baseline first. Watch on YouTube
Genie Ontology: Why Enterprise AI Needs Context
Kanerika explains why AI answers drift without shared business context, and how an ontology gives models the definitions and relationships they need to stay accurate.
The Question a Vector Index Cannot Answer Picture a quality lead at a pharmaceutical company typing one question into the internal AI assistant. “Which suppliers for Product X have contracts affected by the new EU packaging regulation?” The assistant returns three confident paragraphs, cites two policy documents, and names a supplier the company stopped using in 2024.
Nothing in that answer was invented from thin air. In fact, every passage it cited was real and semantically close to the question. The failure was structural, because the answer depended on how products, suppliers, contracts, and regulations connect, and a pile of similar text chunks does not hold those connections.
This is the gap knowledge graph RAG fills. This guide therefore explains how it works, why the ontology underneath the graph matters more than the graph database you pick, how to build one in eight steps, and when a plain retrieval augmented generation setup is the better choice.
What Is Knowledge Graph RAG? Knowledge graph RAG is a retrieval augmented generation approach that pulls connected entities, relationships, and supporting source passages from a knowledge graph, then hands that structured context to an LLM. The model answers from facts and their links rather than from text that merely sounds similar to the question. Many teams and vendors call the same idea GraphRAG.
A standard RAG pipeline splits documents into chunks, embeds them, and retrieves the nearest neighbors for each query. That works well, for instance, when one passage holds the answer. However, it struggles when the answer is spread across several records that only make sense together.
How a Knowledge Graph Changes the Retrieval Step A knowledge graph stores facts as nodes and edges. “Supplier A supplies Component B” is two nodes and one typed relationship, and that relationship can carry properties such as a start date or a contract ID.
When a question arrives, a graph RAG system usually runs five moves in order.
Identify the entities named or implied in the question, such as a product, a regulation, or a customer. Link those mentions to canonical nodes in the graph, so “J. Smith” and “Jane Smith” resolve to one person. Traverse the relationships that matter, filtered by type, direction, and date. Collect the source passages attached to each fact, so every claim can be cited. Assemble that context and pass it to the LLM with instructions to answer only from it. As a result, the model gets context it can reason over step by step. Equally important, it leaves an audit trail, because every hop in the answer maps to a stored relationship with a source.
Why Vector RAG Struggles With Connected Questions Vector search ranks text by semantic closeness. It has no built-in notion of identity, direction, or time, which are exactly the things enterprise questions depend on.
Four failure patterns show up again and again in production RAG systems.
Entity ambiguity. Two suppliers called “Apex Labs” and “Apex Laboratories Ltd” look identical to an embedding model, even when one is a subsidiary and the other is a competitor.Multi-hop questions. “Which customers bought a product that uses a recalled part?” needs three joined facts. No single chunk contains all three.Version conflicts. A 2025 policy and the 2023 policy it replaced read almost the same, so both get retrieved and the model may quote the old one.Corpus-wide questions. “What are the main risk themes across our audit reports?” is a summarization task over everything, not a lookup of the top five chunks.The last point is the one Microsoft Research built its approach around. Its GraphRAG paper notes that standard RAG “fails on global questions directed at an entire text corpus.” These gaps are a major source of the confident wrong answers described in Kanerika’s guide to LLM hallucination .
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RAG Development Services
Kanerika designs and builds enterprise RAG systems, from vector and hybrid graph retrieval to evaluation suites, access controls, and production monitoring.
Explore RAG Development Even so, none of this means vector search is obsolete. Techniques covered in advanced RAG , such as reranking and query rewriting, fix many single-document problems. Instead, the graph earns its place only when the question depends on relationships.
Why Ontology Is the Foundation of Trustworthy Knowledge Graph RAG A knowledge graph is only as reliable as the rules that shaped it. Those rules live in the ontology, and this is where most graph RAG projects either succeed or quietly fail.
Ontology vs Knowledge Graph The ontology is the schema. It defines classes such as Supplier, Product, Contract, and Regulation, the relationships allowed between them, and the constraints each must satisfy. The knowledge graph is the data, meaning the actual suppliers, products, and contracts stored as instances of those classes.
Put simply, the ontology says “a Contract must be signed with exactly one Supplier.” The graph says “Contract 4471 was signed with Apex Labs on March 3.” Kanerika’s guide to data ontology for AI agents covers this business-layer view in more depth.
Four Ontology Components That Improve Retrieval Classes define the allowed entity types, which tells the extraction step what to look for and stops it from inventing new categories.Typed relationships define direction, domain, and range. “SUPPLIES” can only run from a Supplier to a Component, so a traversal cannot wander into nonsense paths.Constraints set required identifiers, data types, and cardinality. A Contract without a Supplier fails validation instead of entering the graph.Business definitions pin down terms such as “active supplier” or “effective contract,” so the graph and the people asking questions mean the same thing.How Ontology Prevents Wrong Retrieval Paths Go back to the pharmaceutical question. Without an ontology, an LLM-extracted graph might hold an edge “Apex Labs, mentioned in, EU Regulation 2025/40” because both appeared in the same email. A traversal would treat that co-mention as a real dependency.
With an ontology, the only valid path is Product, then HAS_COMPONENT, then Component, then SUPPLIED_BY, then Supplier, then UNDER_CONTRACT, then Contract, then SUBJECT_TO, then Regulation. As a result, every hop is typed, every node has a canonical ID, and the “mentioned in” noise never becomes an answer.
Why an Ontology Alone Does Not Guarantee Correct Answers An ontology defines what may be true. It cannot confirm that an extracted fact is actually true, that a record is current, or that the person asking has the right to see it.
Therefore, the ontology has to sit alongside provenance, freshness checks, and access control. Also, teams that already run a semantic layer for BI will recognize the overlap, which Kanerika unpacks in ontology vs semantic layer . The diagram below shows how the three layers fit together.
Ontology-Driven vs LLM-Extracted GraphRAG Two broad ways exist to build a graph for retrieval. The choice shapes cost, accuracy, and how much governance you will need later.
Schema-First GraphRAG With a Defined Ontology Here the team designs the ontology first, then maps structured sources such as ERP and CRM tables directly onto it. LLMs extract entities from documents only into approved classes and relationship types, and every extracted fact must pass validation.
The upfront effort is higher. However, the graph then speaks the business’s language, survives audits, and gives repeatable answers to operational questions.
LLM-Extracted Graphs and Microsoft GraphRAG Microsoft GraphRAG takes a different route. An LLM reads the corpus, extracts entities and relationships, clusters them into communities with the Leiden algorithm, and writes a summary for each community. Queries then run as local search over specific entities, global search over community summaries, or DRIFT search, which blends the two.
The approach is not strictly schemaless, since you can tune the entity types it extracts. However, the structure emerges from the text rather than from an approved business model. In the original paper, community-based answers beat a vector RAG baseline with comprehensiveness win rates of 72 to 83 percent on podcast transcripts, which shows how strong it is for sensemaking across a large corpus.
Where Each Approach Performs Better Ontology-driven graphs win on governed, repeatable questions about real business entities. LLM-extracted graphs win on exploration, such as finding themes across ten thousand research notes, where nobody knows the right schema yet.
Combining Automatic Extraction With Ontology Validation Most mature enterprise builds end up in the middle. LLM extraction proposes entities and edges, a mapping step aligns them to the approved ontology, entity resolution merges duplicates, and anything that fails a constraint goes to a review queue instead of the graph.
Table 1: Ontology-driven vs LLM-extracted GraphRAG
Criteria Ontology-driven graph LLM-extracted graph (e.g. Microsoft GraphRAG) Schema Designed upfront and versioned Emerges from extraction, lightly tuned Business meaning Explicit, approved definitions Depends on what the LLM inferred Relationship types Controlled vocabulary with direction and range Free-text labels that vary by document Validation Rule-based (SHACL or database constraints) Needs added checks and human review Best question type Operational, multi-hop, audited Corpus-wide themes and discovery Upfront effort Higher (modeling workshops, mapping) Lower setup, higher LLM indexing spend Ongoing maintenance Ontology versioning and source mappings Re-extraction and community re-summarization
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Not Sure Which Graph Approach Fits Your Data?
Kanerika reviews your sources, question set, and current RAG baseline, then recommends ontology-driven, LLM-extracted, or hybrid graph retrieval with a clear cost estimate.
Schedule a Demo → RDF and OWL vs Labeled Property Graphs The graph model you pick affects how formally you can express the ontology, which query language your teams write, and how validation works. Neither model is universally better.
RDF, OWL, and SPARQL The W3C RDF standard stores every fact as a subject, predicate, object triple, with globally unique IRIs for each thing. OWL 2 adds formal class hierarchies and logical rules, so a reasoner can infer that a “Contract Manufacturer” is also a “Supplier” without anyone storing that edge.
Queries are then written in SPARQL. Because of this, the stack suits regulated industries, data sharing across organizations, and teams that want to reuse public vocabularies such as schema.org or FIBO in finance.
Labeled Property Graphs and Cypher Property graphs, used by Neo4j, Amazon Neptune, and others, store nodes and relationships that each carry their own key-value properties. Queries use pattern-matching languages such as Cypher , or the newer ISO standard GQL.
As a result, developers often find this model faster to learn and closer to how applications think. However, the trade-off is that the ontology lives partly in database constraints and partly in application code, rather than in one formal model.
How SHACL and Database Constraints Protect Data Quality The W3C SHACL specification lets you validate an RDF graph against shapes, such as “every Contract has exactly one supplier and a valid start date.” Similarly, property graph teams get protection from uniqueness constraints, required properties, and validation in the ingestion pipeline.
Also, keep validation and inference separate in your head. Validation rejects bad data, while inference creates new facts from rules, and confusing the two is a common source of surprise edges in production.
Table 2: RDF/OWL vs labeled property graphs for knowledge graph RAG
Dimension RDF / OWL Labeled property graph Data unit Triples with global IRIs Nodes and edges with properties Ontology support Native, formal (RDFS, OWL) Implicit, via labels and constraints Validation SHACL shapes Database constraints plus pipeline checks Query language SPARQL Cypher, GQL, Gremlin Inference Built in through reasoners Usually coded in queries or pipelines Interoperability Strong, open standards and shared vocabularies Vendor-specific, improving with GQL Typical fit Regulated, cross-organization, semantic-heavy Application-led, traversal-heavy, fast iteration
If your team already knows SQL and builds applications, a property graph usually ships faster. If you need formal reasoning or must exchange data with regulators and partners, RDF and OWL repay the steeper learning curve.
Knowledge Graph RAG Architecture From Source Data to Grounded Answers A production system has more moving parts than a notebook demo. The reference architecture below shows where the ontology, the graph, the vector index, and the LLM each do their job.
Source Systems and Ingestion Structured sources such as ERP, CRM, and product master data map straight onto ontology classes, which gives the graph a trustworthy backbone. Unstructured sources such as contracts, SOPs, tickets, and emails feed an extraction step that adds facts and evidence on top.
In addition, a governed lakehouse or catalog helps here. Teams on Microsoft Fabric can bind entity types to OneLake tables through the Fabric ontology item, and Databricks teams have a similar path through Databricks Genie ontology .
Ontology, Graph Store, and Vector Index The ontology holds the schema and business definitions. The graph store holds instances, relationships, and provenance pointers back to source records. A vector index, often in the same platform, holds embeddings of the source passages and sometimes of node descriptions.
In particular, keeping text chunks linked to graph nodes is the detail that makes hybrid retrieval work. Kanerika’s vector database comparison covers which stores also support graph or hybrid patterns.
Query Routing, Retrieval, and Answer Generation A router classifies each question and picks a retrieval method. It then checks the user’s permissions, runs graph and vector retrieval, assembles evidence with citations, and passes it to the LLM.
Meanwhile, access control, provenance, monitoring, and refresh run across every layer, not as an afterthought. This is where graph RAG overlaps with the broader discipline of context engineering , meaning getting the right facts into the model’s context window at the right moment.
How to Build Knowledge Graph RAG in 8 Steps The roadmap below takes a team from one high-value question to a governed production system. Each step has a clear exit check, so you never build more graph than the use case needs.
Step 1. Start With Questions That Need Connected Facts Collect 30 to 50 real questions from the business that today take hours of manual cross-referencing. Write the expected answer for each, then run them through your current vector RAG system to record a baseline score.
If the baseline already answers most of them correctly, stop here. In that case, a graph is not needed.
Step 2. Design a Small Domain Ontology Model only the classes and relationships those questions touch, often 10 to 40 entity and relationship types for a first release. Define keys, cardinality, and a plain-language definition for every term, and have a business owner sign off on each one.
Above all, resist the urge to model the whole enterprise. A small ontology that answers real questions beats a perfect one that never ships.
Step 3. Map the Ontology to Governed Sources Connect each class to its system of record, such as the supplier table in ERP or the contract repository. Use the catalog and data lineage you already have, so every node can be traced back to where it came from.
Step 4. Extract Entities With Schema-Guided Prompts For documents, prompt the LLM with the ontology’s classes and relationship types and ask it to extract only those. Then return structured output such as JSON, and record the source document, page, and character span for every extracted fact.
In fact, schema-guided extraction cuts noise sharply compared with asking a model to “find all entities.” It also makes the next step far easier.
Step 5. Resolve Entities and Keep Provenance Merge duplicates such as “Apex Labs,” “Apex Laboratories Ltd,” and vendor ID 20417 into one canonical node. Use deterministic keys first, fuzzy matching second, and human review for low-confidence merges.
Overall, entity resolution is where most graph quality is won or lost. Treat it as a data quality discipline with its own metrics, not a one-time cleanup.
Step 6. Combine Graph Traversal With Vector Retrieval Use the graph to find the right entities and relationships, then pull the supporting text through the vector index for those entities only. As a result, this narrows the search space and gives the LLM both structure and evidence.
Frameworks covered in LangChain vs LangGraph and LlamaIndex’s property graph index all support this hybrid pattern.
Step 7. Evaluate Against the Baseline Rerun the Step 1 questions and compare answer correctness, citation accuracy, and latency against vector RAG. Then keep the graph only for the question types where it wins by a measurable margin.
Step 8. Govern the Ontology and Plan Refreshes Version the ontology like code, with owners, change requests, and release notes. Schedule incremental refreshes for every source, and alert when a refresh fails or when validation rejections spike.
Four Retrieval Methods and When to Use Each Not every question needs a graph traversal. A good system routes each question to the cheapest method that can answer it well.
Vector Retrieval for Single-Passage Questions “What is our travel reimbursement limit?” lives in one paragraph of one policy. Therefore, plain vector search, with reranking, is fast and cheap here.
Local Graph Retrieval for Entity Questions “Who owns the Apex Labs relationship and which contracts are active?” starts from one known entity and walks its neighborhood. For this reason, a short Cypher or SPARQL query returns exact facts.
Hybrid Retrieval for Multi-Hop Questions The pharmaceutical supplier question needs four hops plus the regulation text itself. First, the graph finds the path, and then the vector index returns the clauses that explain why each contract is affected.
Global and Community Retrieval for Corpus-Wide Questions “What recurring root causes appear across 2025 deviation reports?” needs summaries over everything. Community summaries, as in Microsoft GraphRAG, or a map-reduce summarization pass handle this best.
Table 3: Retrieval method by question type
Question type Example Best method Relative cost Single fact in one document Reimbursement limit Vector search with reranking Low Facts about one entity Active contracts for a supplier Local graph query Low Chained facts across entities Suppliers affected by a regulation Hybrid graph plus vector Medium Themes across a corpus Recurring deviation root causes Community summaries or map-reduce High at index time Aggregates over structured data Spend by supplier last quarter Text-to-SQL or semantic model Low
The router itself can be a small classifier or an LLM call with a fixed set of labels. Agent frameworks extend the idea, letting a model choose and chain tools, which is the pattern behind agentic RAG .
Using Text-to-Cypher and Text-to-SPARQL For entity and multi-hop questions, many systems let the LLM write the graph query itself. The model gets the ontology as its schema, translates the user’s question into Cypher or SPARQL, runs it, and answers from the returned rows.
In this case, the ontology makes this far more reliable, because the model can only reference real labels, properties, and relationship types. In addition, few-shot examples of correct queries for common question patterns lift accuracy further.
Controlling Query Accuracy and Permissions Never run generated queries with write access. Use a read-only role, validate the query against the schema before execution, cap traversal depth and row counts, and log every query for review.
Equally, permissions must be enforced in the graph store or a policy layer, never in the prompt alone. Kanerika’s guide to data access governance covers row- and attribute-level patterns that carry over directly to graph data.
Case Study
43% Faster Document Retrieval for an Investment Bank
Kanerika gave a leading investment bank chat-based retrieval across documents and databases with role-based controls, delivering 43% faster information retrieval, 100% role-based compliance, and 35% higher workforce efficiency.
Read the Case Study → Keeping Knowledge Graph RAG Accurate as Data Changes A graph that was correct last quarter can be wrong today. Freshness is the failure mode that demos never show and production always finds.
Incremental Ingestion and Change Data Capture Rebuilding the whole graph every night does not scale and wastes LLM spend. Instead, use change data capture on structured sources and re-extract only the documents that changed, then update the affected nodes and edges.
Time-Aware Relationships Store valid-from and valid-to dates on relationships such as contracts, ownership, and policy versions. Consequently, the system can answer “as of” questions correctly and keep superseded facts from surfacing as current ones.
Ontology Change Management When the business redefines a term, the ontology changes, and so must every mapping and extraction prompt that depends on it. Treat these changes as releases with impact analysis, the same way data governance programs handle changes to critical data elements.
How to Evaluate Knowledge Graph RAG Evaluation has to cover three layers, the answer, the retrieval, and the graph itself. A system that scores well on one and poorly on another will still fail users.
Retrieval and Answer Quality Metrics Answer correctness against your Step 1 gold answers, judged by experts or a calibrated LLM judge.Faithfulness , meaning the share of claims in the answer that are supported by the retrieved context.Citation accuracy , meaning whether each cited source actually contains the claim.Context recall on multi-hop questions, which shows whether every needed fact was retrieved.Ontology and Graph Quality Metrics Constraint violation rate from SHACL or database checks.Duplicate entity rate after resolution, sampled and reviewed by data stewards.Extraction precision on a labeled sample of documents.Coverage of the entities needed by your question set.Operational Metrics Track latency per retrieval method, cost per answered question, refresh lag per source, and the share of questions routed to each method. For more depth, Kanerika’s guide to AI agent evaluation covers how to build the judge and test suite that keeps these numbers honest over time.
Cost, Trade-Offs, and When Not to Use Graph RAG Graph RAG is a real investment. For example, it adds modeling work, extraction spend, a new data store, and ongoing stewardship on top of a vector RAG stack.
What Drives Cost The biggest line items are LLM extraction and summarization at index time, ontology design and business sign-off, entity resolution, and graph infrastructure. Indexing cost also varies widely by method. Microsoft Research reported that its LazyGraphRAG variant has indexing costs “identical to vector RAG and 0.1% of the costs of full GraphRAG,” because it defers summarization to query time.
On the other hand, structured sources mapped straight onto an ontology need no LLM extraction at all, which is one more reason the schema-first approach often costs less to run than it does to design.
AI Assessment
Is Your Data Ready for Graph RAG?
Score your data foundation, governance, and use cases before investing in an ontology and knowledge graph, and get a prioritized plan for what to fix first.
Start Your AI Assessment → When Graph RAG Is Worth It Questions regularly span three or more connected entities. Entity identity matters, such as customers, suppliers, patients, or assets with many name variants. Answers must be explainable and auditable, path by path. The same entities are reused across many use cases, so the graph pays back more than once. When It Is the Wrong Choice Most questions are answered by one passage of one document. The content changes so fast that the graph would always be stale. No one in the business can own the ontology and its definitions. The real need is aggregation over tables, which a semantic model or text-to-SQL handles better. A Simple Decision Framework Start with vector RAG and measure it. Then add hybrid graph retrieval when multi-hop and identity questions fail, and invest in a full ontology-driven graph when those questions are frequent, high-impact, and shared across teams. Gartner warns that organizations will abandon 60 percent of AI projects unsupported by AI-ready data through 2026, and a governed ontology is one of the clearest ways to make data AI-ready.
Kanerika’s AI readiness assessment guide walks through how to score your data foundation before you commit to a graph.
Enterprise Knowledge Graph RAG Use Cases The strongest use cases share one trait. The answer lives in the relationships between records, not in any single record.
Regulatory and Compliance Impact Analysis Map regulations to the products, processes, sites, and contracts they affect. Then, when a rule changes, the system answers “what is impacted and who owns it” in minutes, with a cited path for every item.
Supply Chain and Supplier Risk Connect suppliers, components, plants, and shipments to spot single points of failure. For example, a question like “which finished products depend on a tier-two supplier in a flagged region” becomes a short traversal.
Financial Services Research and Due Diligence Link companies, executives, subsidiaries, filings, and news. As a result, analysts get answers about ownership chains and exposure that would take days of manual reading.
Enterprise Knowledge and Data Catalog Search Connect people, projects, documents, and datasets so employees can ask who knows what and which data feeds which report. This extends the patterns in Kanerika’s guide to AI knowledge management and the enterprise data catalog .
Customer Support and Expert Matching Link products, known issues, fixes, releases, and customers’ installed versions. Support agents and AI assistants can then answer “is this bug fixed in the version I should upgrade to” by following edges instead of guessing from similar tickets.
Datasheet
Microsoft Fabric Ontology by Kanerika
See how Kanerika models business entities, relationships, and KPI definitions as a governed ontology on Microsoft Fabric that AI agents and copilots can query.
View the Datasheet → Knowledge Graph RAG Tools and Frameworks The tooling has matured quickly, and most teams combine a graph store, an orchestration framework, and an LLM platform.
Graph stores. Neo4j, Amazon Neptune, Stardog, GraphDB, and TigerGraph cover property graph and RDF needs, and several now include vector indexes.GraphRAG libraries. Microsoft GraphRAG and LazyGraphRAG for community-based retrieval, and LightRAG for lighter-weight graph indexing.Orchestration. LangChain, LangGraph, and LlamaIndex provide graph retrievers, text-to-Cypher chains, and hybrid retrieval components.Platform ontologies. Microsoft Fabric IQ and Databricks Genie ontology bind business entities to governed lakehouse data, which can serve as the ontology layer for retrieval.Meanwhile, research is moving fast too. The KG2RAG framework , for example, uses knowledge graph links to expand and organize retrieved chunks so the context holds together. Kanerika’s roundup of RAG tools compares the wider tool market.
How Kanerika Builds Ontology-Grounded Knowledge Graph RAG Kanerika treats the ontology as the product and the graph as its output. As a result, engagements start from the business questions, not the database, and move through five stages.
Assess. Collect the questions that fail today, score the existing RAG baseline, and confirm the graph is justified.Model. Run short workshops with business owners to define the minimum ontology, its terms, and its rules.Build. Map governed sources onto the ontology, add schema-guided extraction for documents, and resolve entities with provenance.Govern. Version the ontology, enforce access controls in the data layer, and set refresh and validation alerts.Enable. Connect the graph to assistants and agents with hybrid retrieval, then measure answer quality against the baseline every release.In addition, this approach is grounded in delivered work. For a global pharmaceutical enterprise, Kanerika built a governed ontology on Microsoft Fabric that standardized more than 85 KPIs and modeled 40+ entities and relationships, cutting investigation and reconciliation time by 70 percent. For a leading investment bank, Kanerika delivered chat-based retrieval across documents and databases with role-based controls, reaching 43 percent faster information retrieval and full role-based compliance.
Similarly, the pitfalls Kanerika’s teams watch for are consistent across industries. Modeling too much too early, skipping entity resolution, enforcing permissions only in the prompt, and measuring demos instead of real questions cause most stalled projects.
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Business Ontology on Microsoft Fabric: Global Pharma Case Study
Watch how Kanerika built one governed ontology on Microsoft Fabric that standardized 85+ KPIs and cut investigation time by 70% for a global pharmaceutical enterprise.
Kanerika’s RAG development services and AI governance practice pair these steps with the platform skills of a Microsoft Solutions Partner for Data and AI and a Databricks Consulting Partner.
Wrapping Up Knowledge graph RAG closes the gap between text that sounds right and answers that are right. The graph supplies connections, and the ontology decides whether those connections can be trusted.
Start with the questions your current assistant gets wrong, model only the meaning those questions need, and keep vector search for what it does well. Finally, measure every step against a baseline, and the graph will earn its place one use case at a time.
Frequently Asked Questions
What is knowledge graph RAG? Knowledge graph RAG is a retrieval augmented generation approach that retrieves connected entities, relationships, and their source passages from a knowledge graph before an LLM answers. It grounds responses in linked facts instead of only semantically similar text, which helps with multi-hop, identity-sensitive, and auditable enterprise questions. It is often called GraphRAG.
What is the difference between knowledge graph RAG and traditional RAG? Traditional RAG embeds document chunks and retrieves the most similar ones for each question. Knowledge graph RAG also stores entities and typed relationships, so it can follow connections across records, resolve duplicate names, and respect dates. Most production systems combine both, using the graph for structure and vector search for supporting text.
Why is an ontology important for knowledge graph RAG? The ontology is the schema of the knowledge graph. It defines entity classes, allowed relationships, constraints, and business definitions, so extraction stays consistent and traversals follow valid paths. Without one, LLM-built graphs drift, link unrelated records through co-mentions, and give answers that are hard to audit or repeat.
Can you build knowledge graph RAG without an ontology? Yes. Approaches like Microsoft GraphRAG let an LLM extract entities and relationships directly from text and cluster them into communities. That works well for exploring themes across a large corpus. For governed operational questions about real business entities, an approved ontology gives more consistent, explainable, and maintainable results.
How is Microsoft GraphRAG different from ontology-driven GraphRAG? Microsoft GraphRAG builds its graph from text with an LLM, groups entities into communities, and summarizes them for local, global, and DRIFT search. Ontology-driven GraphRAG starts from an approved business schema and maps governed sources onto it. The first excels at corpus-wide sensemaking, the second at repeatable, audited enterprise answers.
Should I use RDF and OWL or a property graph like Neo4j for knowledge graph RAG? Choose RDF and OWL when you need formal reasoning, open standards, and data exchange with partners or regulators. Choose a labeled property graph such as Neo4j or Amazon Neptune when your team builds applications and wants fast iteration with Cypher or GQL. Both support hybrid vector and graph retrieval.
How does knowledge graph RAG handle multi-hop questions? It identifies the entities in a question, links them to canonical nodes, and traverses typed relationships across several hops, such as product to supplier to contract to regulation. It then retrieves the source passages for each fact, so the LLM answers from a connected chain of evidence instead of unrelated chunks.
Can knowledge graph RAG work with a vector database? Yes, and it usually should. A common pattern uses the graph to find the right entities and relationships, then queries a vector index for text linked to those entities only. Many graph databases now include vector indexes, and frameworks like LangChain and LlamaIndex support this hybrid retrieval directly.
How do you measure knowledge graph RAG accuracy? Measure answer correctness against expert gold answers, faithfulness to retrieved context, citation accuracy, and context recall on multi-hop questions. Track graph quality too, including constraint violations, duplicate entities, and extraction precision. Always compare these scores with your existing vector RAG baseline on the same question set.
When should an enterprise avoid knowledge graph RAG? Avoid it when most questions are answered by a single passage, when content changes too fast to keep a graph fresh, when no one can own the ontology, or when the real need is aggregation over tables. In those cases vector RAG, a semantic model, or text-to-SQL is cheaper and simpler.
What is the difference between a knowledge graph and an ontology? An ontology is the model that defines what kinds of things exist, how they can relate, and what rules they follow. A knowledge graph is the data that follows that model, meaning actual customers, products, contracts, and the links between them. The ontology is the schema, the graph is its instances.
How much does knowledge graph RAG cost compared with vector RAG? It costs more because it adds ontology design, entity resolution, a graph store, and ongoing stewardship. LLM extraction and summarization at index time are often the largest variable cost, though methods like LazyGraphRAG reduce it sharply. Mapping structured sources directly onto an ontology avoids extraction spend for that data.
