Your organization has data everywhere: customer databases, financial systems, HR records, project files, and emails. But when you need to answer “Which customers bought Product X and also had support tickets last month?” you’re stuck searching multiple systems, copying data to Excel, and hoping you didn’t miss anything.
This data chaos costs companies millions. McKinsey reports that employees spend 20% of their time searching for information. Knowledge graphs solve this by connecting all your data dots automatically1 .
Use Cases
Search Engines and Information Retrieval
Knowledge graphs are utilized in recommendation systems to deliver more personalized and relevant recommendations. For example, in e-commerce, a knowledge graph can connect products with user preferences, purchase history, and other users’ behaviors to recommend items that a customer is likely to buy.
Recommendation Systems
Knowledge graphs are utilized in recommendation systems to deliver more personalized and relevant recommendations. For example, in e-commerce, a knowledge graph can connect products with user preferences, purchase history, and other users’ behaviors to recommend items that a customer is likely to buy.
Healthcare and Biomedical Research
In healthcare, knowledge graphs can integrate patient data, medical research, and clinical guidelines to provide a comprehensive view of a patient’s health. This integration can help in identifying potential diagnoses, suggesting treatments, and predicting health outcomes. For instance, a knowledge graph might connect symptoms, diseases, and treatment protocols, allowing doctors to make informed decisions.
Enterprise Knowledge Management
Organizations utilize knowledge graphs to manage and organize their extensive internal data, including documents, employee expertise, and project information. This facilitates knowledge discovery, enabling employees to locate and utilize relevant information throughout the organization easily.
Natural Language Processing (NLP)
Knowledge graphs enhance NLP applications by providing context and understanding relationships between words. For instance, in chatbots and virtual assistants, knowledge graphs enable the system to understand user queries in context, thereby improving the accuracy and relevance of responses.
Knowledge Graphs with Retrieval-Augmented Generation (RAG)
Knowledge Graphs enhance the ability of AI models by combining the structured context of knowledge graphs with the generative capabilities of large language models (LLMs). In RAG applications, knowledge graphs store relationships between entities, allowing for more relevant and context-rich information retrieval.
By incorporating background knowledge and context from knowledge graphs, NLP models can perform more effectively and accurately across a range of tasks such as:
- Entity recognition
- Relation extraction
- Text summarization
Figure 4. Semantic triples for NLP
Source: Accenture
Fraud Detection
- Financial institutions utilize knowledge graphs to identify fraudulent activities by mapping and analyzing relationships between entities, including bank accounts, transactions, and individuals.
By analyzing these relationships and patterns in the knowledge graph, it becomes easier to spot potential fraud or security threats, such as:
- Suspicious transactions
- Fake accounts
- Abnormal user behavior
Supply Chain Optimization
In supply chain management, it can connect data from different stages of the supply chain, from raw materials to finished products. This interconnected view enables companies to optimize logistics, reduce costs, and enhance efficiency by identifying bottlenecks and predicting potential disruptions.
Customer Relationship Management (CRM)
Knowledge graphs in CRM systems connect customer interactions, purchase history, and demographic information, providing a comprehensive 360-degree view of each customer. This holistic understanding enables more effective personalization and enhances customer satisfaction.
Legal and Compliance Management
Law firms and compliance departments utilize knowledge graphs to manage and link legal documents, case law, regulations, and client information. This enables more efficient legal research, risk assessment, and compliance management by understanding the interconnections between legal entities and concepts.
Intelligent Digital Assistants
Virtual assistants, such as Siri, Alexa, and Google Assistant, utilize knowledge graphs to comprehend and respond to user queries. By mapping out the relationships between different pieces of information, these systems can provide more accurate and context-aware responses.
Assistant Capabilities:
- Contextual understanding of queries
- Multi-turn conversation handling
- Personalized responses based on user history
- Integration with external knowledge sources
Examples of Knowledge Graph Implementations
1.Google Knowledge Graph:
Google’s Knowledge Graph is a vast database that connects billions of facts about people, places, and things. It enhances search results by providing users with summaries, related information, and direct answers to their queries, rather than just a list of web pages.
Scale and Impact:
- Billions of entities and relationships
- Supports multiple languages
- Powers Google Search, Assistant, and other services
- Continuously updated from web crawling and user feedback
Figure 6. Google knowledge panel
2.Facebook’s Social Graph
Facebook utilizes a social graph to map the relationships between its users, encompassing friendships, likes, posts, and interactions. This graph helps personalize the user experience by displaying relevant content and advertisements based on connections within the network.
Social Graph Features:
- User relationship mapping
- Interest and behavior tracking
- Content personalization algorithms
- Targeted advertising capabilities
3.Netflix Content Recommendation Engine
Netflix uses knowledge graphs to recommend shows to new subscribers by analyzing thematic similarities, even without prior viewing history. The semantic richness of these graphs uncovers nuanced patterns in user preferences, leading to highly personalized suggestions. This system powers approximately 80% of Netflix’s content consumption through intelligent recommendations.
4.Amazon Product Knowledge Graph
Amazon uses a knowledge graph to connect products, customer reviews, and purchasing behaviors. This enables Amazon to offer personalized recommendations and enhance search results, making it easier for customers to find products that align with their interests.
E-commerce Applications:
- Product recommendation engines
- Enhanced search functionality
- Cross-selling and upselling optimization
- Inventory and pricing optimization
5.LinkedIn’s Economic Graph
LinkedIn’s Economic Graph maps the relationships between people, jobs, companies, skills, and educational institutions. This graph is used to connect professionals with job opportunities, suggest potential connections, and provide insights into industry trends.
Professional Network Benefits:
- Job matching and recommendations
- Skills gap analysis
- Industry trend identification
- Professional networking suggestions
6.DBpedia
DBpedia is a large-scale, open-source knowledge base derived from the structured information available in Wikipedia.It aims to make Wikipedia’s content more accessible, machine-readable, and useful for various applications and research purposes. Launched in 2007, it acquires structured data from Wikipedia infoboxes, categories, links, and other elements, converting it into a standardized format by DBpedia ontology.
DBpedia Features:
- Over 6 million entities
- 130 languages supported
- Open-source and freely available
- Used in academic research and commercial applications
Figure 7. DBpedia ontology overview with classes and instances in each class
Source: DBpedia
7. Microsoft Semantic Layer Implementations
Microsoft continues developing semantic layer technologies that incorporate knowledge graph principles across Azure AI services and Microsoft 365, enabling enterprise-wide knowledge discovery and AI-powered insights.
Enterprise Applications:
- Microsoft Graph API
- Power BI semantic models
- SharePoint knowledge management
- Teams collaboration insights
What is a knowledge graph?
Figure 1. A simple knowledge graph representation
Source: Wikipedia
A knowledge graph is a structured representation of information and knowledge in the form of a graph. It consists of nodes (real-world entities or concepts) connected by edges (relationships or associations).
The purpose of a knowledge graph is to model, store, and organize complex information in a way that makes it easy for both humans and machines to understand, navigate, and use the knowledge it contains.
Powered by machine learning algorithms, they employ natural language processing (NLP) to create an extensive representation of nodes, edges, and labels via a technique known as semantic enrichment. As data is ingested, this method enables knowledge graphs to recognize distinct entities and comprehend the connections between various entities.
How does a knowledge graph work?
Figure 2. Steps involved in the construction of a graph
Source: ResearchGate
Creating a knowledge graph involves several steps that help in organizing and representing the information effectively:
Benefits
- Enhanced Data Integration: It can integrate diverse data sources, both structured and unstructured, into a unified view. This integration allows for richer insights and better decision-making since all relevant data points are interconnected.
- Improved Search and Discovery: By understanding the relationships between entities, they enable more accurate and context-aware search results. Users can find information not only based on keywords but also based on the relationships between different concepts.
- Semantic Understanding:They add a layer of semantic understanding to data, enabling systems to interpret the meaning of the data rather than just its format. This semantic understanding is crucial for tasks such as natural language processing, where the context of words plays a significant role.
- Facilitation of Complex Queries: Traditional databases often struggle with complex queries that involve multiple relationships between entities. Knowledge graphs, with their graph-based structure, are naturally suited to handle such queries efficiently.
- Real-Time Decision-Making: They enable real-time analytics by facilitating quick connections between various data points. This is particularly useful in domains like finance and healthcare, where timely decisions are critical.
- Interoperability and Reusability: Knowledge graphs promote interoperability across systems by providing a standard framework for representing data. This makes it easier to reuse and share data across different applications and platforms.
Should Your Company Build a Knowledge Graph?
Green Light Signals:
- You have multiple data sources that need connecting
- Users spend >30 minutes daily searching for information
- You need 360-degree customer/product views
- Complex regulatory reporting requirements
- Budget >$500K for data initiatives
Red Light Signals:
- Data quality issues across >50% of sources
- No dedicated data team
- Expecting results in <6 months
- Single-source data problems
- Budget <$100K total
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