Executive summary:
A knowledge graph is a data model that represents information as a network of entities and their relationships. By structuring data in this way, knowledge graphs enable machines to interpret complex information, mirroring how humans make connections between concepts.
In business, knowledge graphs enhance decision-making by integrating data from diverse sources, revealing hidden relationships, and improving data-driven processes like enterprise search, customer insights, and fraud detection. They also support scalability, better search capabilities, and real-time decision-making. However, they require significant setup, maintenance, and computational resources, especially as they grow more complex.
Knowledge graphs are instrumental in fields like supply chain management, product recommendations, and regulatory compliance, and are evolving as key components in AI-driven solutions across industries.
At Talbot West, we specialize in implementing knowledge graphs and integrating them with AI ensembles.
If you’re looking to enhance your organization's capabilities with knowledge graphs—whether for improving data integration, decision-making, or operational efficiency—contact us for a free consultation to explore how they can align with your strategic goals.
A knowledge graph helps machines understand and retrieve information by structuring data in a way that mimics human logic.
A knowledge graph is a structured model that organizes information as a network of entities (nodes) and their relationships (edges). Nodes represent people, places, or objects, while edges define how they are connected.
This model mirrors real-world connections, like linking a customer to a product through a purchase. Knowledge graphs excel at integrating both structured data from databases and unstructured data from sources like documents, providing a unified view of an organization's information.
Knowledge graphs transform data usage to create smarter and more efficient systems. Here are three major applications:
Knowledge graphs connect data points to provide context, which sharpens AI systems’ understanding of relationships between entities. This structured approach improves AI’s ability to process natural language, deliver accurate recommendations, and support decision-making with clear insights.
Data often exists in separate systems and formats. Knowledge graphs unify data from databases and documents into a cohesive structure. This approach strengthens information access and ensures organizations work with a complete, organized view of their data.
Search engines use knowledge graphs to map content to real-world entities, improving search visibility. Structuring website content within a knowledge graph boosts relevance in search rankings, helping users quickly find the most relevant information. We expect knowledge graphs to continue to be relevant in the world of generative engine optimization (GEO).
Knowledge graphs rely on semantic relationships to model data. This means they use context, meaning, and relationships between entities to organize complex and diverse information. Here's how:
Knowledge graphs are used in business to enhance data-driven decision-making and improve operational efficiency. They help companies integrate and organize massive amounts of data for smarter searches, personalized recommendations, and advanced analytics.
Launched in 2012, Google’s Knowledge Graph brought the concept into the mainstream by enhancing search with a structured database of interconnected facts about people, places, and things.
While Google has not released many technical details about its exact structure or size, the graph is central to powering features such as Google search suggestions and rich snippets. External access to this knowledge graph is limited, as it’s primarily used within Google’s ecosystem.
DBpedia extracts structured data from Wikipedia’s infoboxes to create a vast knowledge graph with over 4.5 million entities. It spans many topics, including people, places, organizations, and species.
As a key project in the Open Linked Data movement, DBpedia has become a valuable resource for building internal knowledge graphs by providing access to crowdsourced, encyclopedic data. Its comprehensive ontology helps organizations enrich their data with globally recognized entities.
GeoNames offers a rich dataset of over 25 million geographical entities and features and is one of the most comprehensive geographic knowledge graphs available.
Accessible under a Creative Commons license, GeoNames provides detailed information on countries, cities, landmarks, and other geographical features, supporting applications such as mapping, logistics, and geographic data analysis.
WordNet is a large lexical database for the English language that groups words into sets of synonyms and provides definitions, examples, and their relationships.
This knowledge graph is commonly used in natural language processing (NLP) and search applications to improve text understanding and boost query accuracy by supplying rich semantic connections between words.
Developed by Ontotext, FactForge aggregates Linked Open Data from different sources, including DBpedia, GeoNames, and specialized ontologies such as the Financial Industry Business Ontology.
It integrates these data sets with news articles and is a powerful resource for organizations that need real-time information about people, organizations, and locations. FactForge is widely used in industries such as finance and publishing to power knowledge-driven applications.
Cognitive hive AI (CHAI) can use knowledge graphs to shape a dynamic, adaptable ontology for data-driven decisions. Within CHAI’s dynamic architecture, these graphs evolve continuously, reflecting new data and relationships as they emerge.
CHAI applications span many industries, including financial risk assessment, healthcare diagnostics, and defense operations. CHAI modifies information in real time for current and transparent decision-making. Knowledge graphs record decision paths, which reduces the "black box" nature of AI systems. Businesses see how conclusions form, which increases trust in AI outputs.
CHAI's modular architecture combines knowledge graphs with other AI systems, such as neural networks and machine learning models. This collaboration adjusts to each organization's specific needs. Knowledge graphs supply structured intelligence, while AI models add emergent insights—the combination results in robust operational intelligence.
At Talbot West, we specialize in implementing CHAI’s adaptable AI solutions. Whether you're exploring AI for your business or need a feasibility study, pilot project, or tool assessment, we’re here to help. Schedule a free consultation to discover how CHAI can meet your strategic needs, and visit our services page for the full scope of our offerings.
The largest knowledge graphs are Google's Knowledge Graph, Microsoft's LinkedIn Graph, and Facebook’s Entity Graph. These are vast graph databases that cover billions of real-world entities, relationships, and concepts from public sources, continuously updated to stay current.
Google Search uses a knowledge graph but isn't one itself. The graph supports search functions by providing structured information about entities and their relationships. This data appears in knowledge panels and enriches search query results with contextual information.
A knowledge graph represents a limited application of an ontology. A knowledge graph represents data with entities and relationships, while an ontology defines the formal semantics—rules, classes, and structures—underpinning that graph. Ontologies often guide the construction of semantic networks.
Knowledge graphs and vector databases are different. Knowledge graphs represent relationships between entities, while vector databases store vectors as mathematical relationships in a high-dimensional space. Knowledge graphs focus on semantic representations of data, whereas vector databases excel at similarity searches and semantic search capabilities.
Large language models typically don't use knowledge graphs directly. They learn from text data during training. With the CHAI paradigm, a knowledge graph and an LLM (or multiple) can interact and dynamically inform one another.
Node embeddings transform nodes in a graph into dense vector representations. These vectors capture the structural and semantic properties of nodes in a lower-dimensional space. Knowledge graph embeddings facilitate tasks such as link prediction, node classification, and integration with machine learning models.
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