What is Knowledge Graph?

What is a Knowledge Graph?

A Knowledge Graph is a structured data model that represents knowledge as a network of entities (things) and the relationships (connections) between them. Each entity — a person, company, product, location, or concept — is a node in the graph, and each relationship — "works for," "located in," "manufactures," "competes with" — is an edge connecting two nodes. This structure mirrors how knowledge naturally exists: not as isolated facts in tables, but as an interconnected web of related information.

Google popularized the term when it launched the Google Knowledge Graph in 2012 to enhance search results with structured information. When you search for a company and see a sidebar with its headquarters, CEO, founding date, and related companies, that information comes from a knowledge graph.

For businesses, knowledge graphs provide a way to organize, connect, and query information that is scattered across databases, documents, and systems — turning fragmented data into connected intelligence.

How Knowledge Graphs Work

Structure: Triples

The fundamental building block of a knowledge graph is the triple: subject-predicate-object. Each triple represents a single fact:

• (Grab, headquartered_in, Singapore)
• (Grab, acquired, Jaya Grocer)
• (Anthony Tan, CEO_of, Grab)
• (Grab, operates_in, Southeast Asia)

Thousands or millions of these triples create a rich, interconnected network of knowledge.

Ontology and Schema

Knowledge graphs use an ontology — a formal definition of the types of entities and relationships that exist in the domain. The ontology specifies that "Company" is an entity type with properties like "founding_year" and "headquarters," and that companies can have relationships like "acquired" and "competes_with." This structure ensures consistency and enables sophisticated querying.

Building Knowledge Graphs

Knowledge graphs are populated through several methods:

• Manual curation — Domain experts define entities and relationships directly, ensuring high accuracy but limiting scale
• Automated extraction — NLP techniques (named entity recognition, relation extraction) automatically extract entities and relationships from text documents
• Data integration — Structured data from databases, APIs, and existing systems is mapped into the graph format
• Crowdsourcing — Platforms like Wikidata demonstrate how distributed human contribution can build massive knowledge graphs

Querying Knowledge Graphs

Knowledge graphs are queried using graph query languages like SPARQL or Cypher, which enable questions that would be difficult or impossible with traditional databases:

• "Which companies in Southeast Asia were acquired by technology firms in the past two years?"
• "What products are manufactured by subsidiaries of our top three competitors?"
• "Which of our suppliers share directors with companies on the sanctions list?"

These queries traverse relationships across the graph, connecting dots that would require complex joins across multiple traditional database tables.

Business Applications of Knowledge Graphs

Enterprise Knowledge Management

Large organizations struggle with knowledge fragmented across databases, documents, emails, and employee expertise. A knowledge graph connects all of this information — linking projects to people, people to skills, skills to departments, departments to clients, and clients to contracts. This enables intelligent search and discovery across the entire organizational knowledge base.

Customer 360 Views

Knowledge graphs unify customer data from multiple sources — CRM, support tickets, purchase history, social media, and communications — into a connected profile that shows not just what a customer bought, but why, how they use it, who they interact with, and what they need next. This holistic view powers personalized service and proactive engagement.

Supply Chain Intelligence

Manufacturing and logistics companies use knowledge graphs to model their supply chains as networks of suppliers, materials, factories, logistics routes, and customers. This enables complex analyses like identifying all products affected by a specific supplier disruption or finding alternative sourcing paths for critical components.

Fraud Detection and Compliance

Financial institutions use knowledge graphs to detect fraud patterns that are invisible in tabular data. A fraudulent network might involve seemingly unrelated accounts that are connected through shared addresses, phone numbers, or transaction patterns — connections that only emerge when data is modeled as a graph.

Recommendation Systems

E-commerce and content platforms use knowledge graphs to power recommendations based on rich relationship data. Instead of recommending products based only on purchase history, the graph enables recommendations based on product relationships, user similarity, brand connections, and contextual factors.

Competitive Intelligence

A knowledge graph of the competitive landscape connects companies to their products, markets, partnerships, leadership, funding, and patents. This structured view enables strategic analysis that goes beyond simple monitoring to reveal competitive dynamics and market evolution.

Knowledge Graphs and NLP

NLP plays a critical role in building and using knowledge graphs:

Building Graphs from Text

Named entity recognition and relation extraction — NLP techniques covered elsewhere in this glossary — automatically populate knowledge graphs by extracting entities and relationships from documents. This enables continuous graph expansion as new content is processed.

Enhancing NLP with Graphs

Knowledge graphs improve NLP applications by providing structured context:

• Question answering systems use knowledge graphs to find answers by traversing entity relationships
• Chatbots and virtual assistants use knowledge graphs to provide accurate, contextual responses
• Text generation systems use knowledge graphs to ensure generated content is factually grounded

Entity Linking

NLP systems use knowledge graphs to resolve ambiguous entity mentions in text. When an article mentions "Apple," entity linking determines whether it refers to Apple Inc. or the fruit by checking the knowledge graph for context clues.

Knowledge Graphs for Southeast Asian Businesses

Knowledge graphs offer specific value in ASEAN markets:

• Complex ownership structures — Southeast Asian business groups often have intricate ownership and partnership networks that are naturally modeled as graphs
• Multilingual entity resolution — The same entity may be referenced differently in different ASEAN languages; knowledge graphs provide a canonical reference that links all variations
• Cross-border operations — Businesses operating across multiple ASEAN countries can use knowledge graphs to connect regulatory requirements, market data, and operational information across jurisdictions
• Emerging market intelligence — As ASEAN economies grow rapidly, knowledge graphs help track the evolving competitive landscape, new market entrants, and shifting business relationships

Building an Enterprise Knowledge Graph

Phase 1: Define Scope and Ontology

Start with a specific business domain rather than trying to model everything. Define the entity types and relationships most relevant to your use case. A competitive intelligence graph might focus on companies, products, and markets. A customer intelligence graph might focus on customers, interactions, and products.

Phase 2: Integrate Data Sources

Identify and map data from existing systems — databases, CRMs, document repositories — into the graph structure. This often reveals data quality issues and inconsistencies that must be resolved.

Phase 3: Enrich with NLP

Use NLP to extract additional entities and relationships from unstructured text sources — news, reports, emails, and documents — that are not captured in structured databases.

Phase 4: Deploy and Query

Make the knowledge graph accessible through query interfaces, APIs, and applications. Build dashboards, search tools, and analytical applications that leverage the graph's connected structure.

Phase 5: Maintain and Evolve

Knowledge graphs require ongoing maintenance — adding new entities, updating relationships, verifying accuracy, and expanding the ontology as business needs evolve.

The Strategic Value of Connected Knowledge

Knowledge graphs represent a fundamental shift from storing data in disconnected tables to modeling knowledge as an interconnected network. For businesses, this shift enables questions and analyses that are impossible with traditional data structures. The ability to traverse relationships — to find connections, patterns, and insights across linked data — provides a strategic intelligence advantage that grows more valuable as the graph expands.