Knowledge Graph

What Is a Knowledge Graph?

A knowledge graph is a structured representation of real-world entities and the relationships between them, stored as a network of nodes (entities) and directed, labeled edges (relationships). In formal terms, a knowledge graph is a set of semantic triples in the form (subject, predicate, object), such as (Order #12345, has_status, Shipped) or (Customer, purchased, Product_X). This graph structure allows machines to traverse connections, infer new facts, and answer complex queries that flat databases and keyword search cannot handle.

In customer service, knowledge graphs connect products, policies, customer records, troubleshooting steps, and documentation into a unified web of information that AI Agents can query in real time. Instead of searching for keywords in a knowledge base article, an AI Agent can traverse the graph to find that a specific product has a known defect, which triggers a specific return policy, which requires a specific form.

How Knowledge Graphs Work

A knowledge graph begins with an ontology: a schema that defines the types of entities (customers, products, tickets, policies) and the types of relationships between them (purchased, filed_ticket_about, resolved_by). Data from multiple sources, such as CRMs, help desks, product databases, and documentation, is then ingested and mapped to this ontology, creating a connected web of triples.

Once built, the graph supports several operations. Graph traversal follows edges to answer multi-hop questions ("What warranty covers the product this customer bought last month?"). Graph inference applies rules or machine learning to derive facts not explicitly stored. Graph queries use languages like SPARQL or Cypher to retrieve specific subgraphs. According to Stanford's AI Lab, this combination of explicit structure and inferential power is what makes knowledge graphs uniquely suited to enterprise AI.

Microsoft Research (2025) found that combining knowledge graph retrieval with RAG improved factual accuracy by approximately 23% and achieved 89% user satisfaction in e-commerce customer support, compared to text-only retrieval baselines.

Key Components of a Knowledge Graph

Entities and nodes: The objects represented in the graph, such as customers, products, support articles, agents, and policies. Each node has a type defined by the ontology and can carry attributes (name, date, status).

Relationships and edges: Directed, labeled connections between entities. The label describes the nature of the relationship ("reported_issue," "resolved_by," "related_to"). Relationships give the graph its power by encoding context that flat records cannot capture.

Ontology and schema: The formal vocabulary that defines what types of entities and relationships exist. A well-designed ontology ensures consistency across data sources and enables meaningful queries. Standards like RDF and OWL provide formal frameworks for ontology definition.

Ingestion and linking: The process of extracting entities from source systems (CRM, ticketing, product catalog), resolving duplicates, and connecting them with edges. Entity resolution is one of the hardest engineering challenges in graph construction.

Reasoning engine: The layer that applies rules and machine learning to infer new relationships or validate existing ones, such as suggesting a replacement when a product is discontinued.

Why Knowledge Graphs Matter for Customer Experience

Customer service queries are inherently relational. A customer does not ask an isolated question; they ask about their order, their account, their product. Answering accurately requires connecting information across systems. Knowledge graphs provide that connective tissue. Research published by LinkedIn (2025) reported a 28.6% reduction in median issue resolution time after deploying knowledge graph-enhanced retrieval for support workflows.

Knowledge graphs also reduce AI hallucination by grounding LLM outputs in verified, structured facts. When an AI Agent retrieves a subgraph of connected entities rather than a block of unstructured text, it has a reliable factual foundation for its response. This is why knowledge graphs are becoming the semantic backbone of enterprise RAG architectures.

The Maven Advantage

Maven AGI's platform ingests and connects data from 100+ enterprise systems, building a rich contextual layer that functions as a knowledge graph for every customer interaction. When a customer contacts support, Maven's AI Agent traverses these connections to pull the right context: account history, product details, prior interactions, and relevant policies. This structured retrieval is why Maven customers see consistently high resolution rates.

Enumerate, a property management platform, achieved a 91% resolution rate with Maven AGI by connecting their complex web of property data, tenant records, and maintenance workflows into a unified AI-accessible knowledge layer.

For a deeper technical foundation, explore Google AI Blog on Knowledge Graphs or read about how AI-powered knowledge bases are evolving to support modern support operations.

Frequently Asked Questions

What is the difference between a knowledge graph and a knowledge base?

A knowledge base is typically a collection of articles, documents, or FAQ entries stored as flat or lightly structured content. A knowledge graph goes further by representing the entities within that content and the relationships between them as a structured graph. This structure allows AI systems to reason across connected facts rather than simply searching for keyword matches within individual articles.

How do knowledge graphs reduce AI hallucination?

Knowledge graphs ground AI outputs in verified, structured data. When an conversational AI system retrieves a subgraph of connected entities (product > warranty > return_policy), the LLM generates responses based on factual triples rather than probabilistic text completion. This retrieval-then-generate pattern significantly reduces the likelihood of fabricated or inaccurate responses.

Are knowledge graphs hard to build and maintain?

Building a production knowledge graph requires intentional ontology design, reliable data pipelines, and ongoing entity resolution. The initial investment is significant, but the payoff in AI accuracy and cross-system connectivity compounds over time. Enterprise platforms like Maven AGI abstract much of this complexity by automatically ingesting and connecting data from integrated systems.

Can knowledge graphs work with unstructured data?

Yes. NLP techniques like named entity recognition and relation extraction can parse unstructured text (support tickets, emails, chat logs) and extract entities and relationships to populate a knowledge graph. This hybrid approach combines the flexibility of unstructured data with the precision of structured graph queries.

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