What is Knowledge Graph? All You Need To Know

Sa Wang
|
Software Engineer
|
December 7, 2024
What is Knowledge Graph? All You Need To Know

A knowledge graph is a powerful way to organize and connect data by representing real-world entities—such as people, places, or concepts—and the relationships between them. Unlike traditional databases that store isolated pieces of information, knowledge graphs provide a framework to understand how different pieces of data are connected, offering a more meaningful, contextual view of the information.

In this article, we will explore the fundamentals of knowledge graphs, including their structure and components, their wide range of applications, and the challenges involved in building and maintaining them. We’ll also highlight best practices for creating effective knowledge graphs that can drive smarter decision-making and AI applications.

Key takeaways:

  • Knowledge graphs organize complex data by representing entities and their relationships, offering a deeper understanding of information.
  • They are used in many applications, including search engines, recommendations, customer service, and AI.
  • Building and maintaining knowledge graphs can be challenging, with issues like incomplete data, privacy concerns, and scalability.
  • Best practices for implementation involve careful data management, regular updates, and validation to ensure the graph's accuracy and usefulness.

By the end of this article, you’ll have a solid understanding of what knowledge graphs are, how they work, and how to build and maintain them effectively.

What is a knowledge graph?

A knowledge graph is a graphical data model used to represent and organize structured knowledge. By representing real-world entities and their relationships in the form of a graph, it constructs a large-scale, multi-domain knowledge base. This provides machines with an understandable and inferable knowledge foundation, enabling more intelligent information retrieval, data analysis, and decision support.

The roots of the knowledge graph can be traced back to Tim Berners-Lee’s vision of the Semantic Web in the 1990s. The goal was to create a web of linked data with meaning, enabling machines to interpret and process data seamlessly. In May 2012, Google launched the Knowledge Graph to enhance search results with semantic information. Instead of simply displaying links, Google aimed to provide direct answers and contextual data about entities. For example, searching for "Leonardo da Vinci" would not only return web pages but also show a panel with structured data: who he was, his works, related entities, and more.

Figure: Knowledge panel data about Albert Einstein displayed on Google Search, as of November 2024.

Composition of a knowledge graph

A knowledge graph is built from the following core components:

Entities

Entities represent objects or concepts in the real world, such as people, places, organizations, or items. They are the core nodes in a knowledge graph, used to describe tangible objects or abstract ideas.

Relationships

Relationships define the connections between entities, such as "works at" (between a person and a company) or "located in" (between a place and a city). Relationships not only link entities but also convey the semantic meaning of their association.

Attributes

Attributes describe the characteristics of entities or relationships. For example:

  • An entity like "Person" can have attributes such as "Name," "Age," and "Nationality."
  • A relationship like "works at" can have attributes such as "Start Date" and "Position."

By integrating entities, relationships, and attributes, a knowledge graph can represent complex information and semantic associations in a structured way.

Figure: an example knowledge graph of social network

For the tech-savvy, interacting with a knowledge graph means using APIs or query languages like Cypher, Gremlin, GQL, or SPARQL. For data scientists, these graphs serve as a treasure trove for machine learning, allowing the computation of structural features that enhance model accuracy. And for the non-technical users, they may interact with knowledge graphs through visual interfaces or gain from them indirectly through enhanced services or products.

What is a knowledge graph used for?

Knowledge graphs are all about making connections and understanding relationships. They allow us to move beyond simple facts and delve into the deeper meaning behind them. By understanding how different pieces of information relate to each other, we can gain a more holistic and nuanced understanding of the world.

Think of it like this: instead of just knowing that "the sky is blue" and "birds can fly", a knowledge graph allows us to connect those facts and understand that "birds fly in the blue sky". This may seem simple, but when applied to vast amounts of information, it allows for incredibly powerful insights and deductions.

In essence, knowledge graphs are a way of organizing and structuring information in a way that mirrors human understanding. They allow us to:

  • Connecting disparate information: Knowledge graphs act as a universal connector, linking data from diverse sources like databases, documents, and sensor readings. This creates a unified view of information that would otherwise remain fragmented. Imagine assembling a puzzle where each piece comes from a different box – knowledge graphs put it all together.

  • Uncovering hidden patterns: By analyzing the intricate web of connections within the data, knowledge graphs reveal hidden patterns and relationships. This is like connecting the dots in a constellation to reveal a meaningful shape. These insights can be used for prediction, identifying trends, or discovering new knowledge.

  • Reasoning intelligently: Knowledge graphs empower logical reasoning and inference. They don't just store facts; they allow you to deduce new information based on existing relationships. Think of it like a detective using clues to solve a case. This capability is crucial for decision-making, problem-solving, and building more intelligent AI systems.

  • Building sophisticated AI: Knowledge graphs provide crucial context for AI, enabling it to understand the meaning behind data and make more informed decisions. This moves beyond simple pattern recognition towards true understanding and reasoning. Imagine teaching a child about the world – knowledge graphs provide that foundational knowledge for AI. Read how PuppyGraph helps LLMs increase the output accuracy with GraphRAG
Credit: A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Types of knowledge graphs

Knowledge graphs can be classified into different categories based on their construction method, data sources, and application domains. Each classification provides insight into how knowledge graphs are built and how they can be applied in various fields.

Construction method

  • Bottom-up Construction: This approach extracts entities and relationships from open data sources and merges information using techniques like entity linking. It starts with raw data and builds the graph incrementally.
  • Top-down Construction: Here, a predefined ontology or schema is first established to define concepts and relationships. The graph is then populated with specific instances and attributes based on this structure.
  • Hybrid Construction: This combines both bottom-up and top-down approaches. A core ontology is built first, and then additional data is integrated to expand the graph.

Data sources

  • Structured Data Knowledge Graphs: These are created from well-organized data like databases or XML files. The data is easy to process and integrate directly into the graph.
  • Semi-structured Data Knowledge Graphs: These come from data formats like JSON or web content. They require extra steps for parsing and extracting useful information.
  • Unstructured Data Knowledge Graphs: These are built from unorganized data sources, such as text, images, or audio. Advanced techniques like natural language processing (NLP) and machine learning are used to process and extract knowledge.

Application domain

  • General knowledge graphs: These cover a wide range of domains, like Wikipedia. They provide general knowledge and common facts.
  • Domain-specific knowledge graphs: These focus on a specific field, like healthcare, finance, or education, providing in-depth knowledge and industry-specific terms.
  • Enterprise knowledge graphs: These are tailored to individual businesses, capturing organizational structure, product data, and customer information to support internal operations.

Building and populating knowledge graphs

Building a knowledge graph involves key steps: knowledge extraction, knowledge fusion, knowledge processing, and knowledge updating. Each step ensures that the graph is structured, accurate, and up to date.

Knowledge extraction

Knowledge extraction transforms unstructured or semi-structured data into structured knowledge units, typically in the form of entity-relation-entity triples. The primary tasks are extracting entities and the relationships between them from various data sources. This forms the foundational structure of the knowledge graph.

Knowledge fusion

Knowledge fusion integrates data from multiple sources into a unified graph. It involves aligning entities across sources and merging relationships to ensure consistency. This step is critical for ensuring that the knowledge graph represents a coherent and complete view of the domain.

Knowledge processing

Knowledge processing improves the graph’s quality by validating the extracted knowledge, inferring new relationships, and completing missing information. This step enhances the graph’s ability to support complex queries and reasoning.

Knowledge updating

To maintain the graph's relevance, knowledge updating ensures that new data is incorporated as it becomes available. This includes incremental updates (only changing parts of the graph that are affected), evaluating the quality of the graph regularly, and managing versions to track changes over time.

Applications of knowledge graphs

Knowledge graphs have a wide range of practical applications across different industries. Below are some key areas where knowledge graphs are making a significant impact.

Search and information retrieval

Knowledge graphs play a critical role in enhancing search engines and improving information retrieval. They help systems understand the context of a query beyond simple keyword matching. By representing entities (such as people, locations, or concepts) and their relationships, a knowledge graph enables search engines to deliver more relevant and contextual results. For example, Google uses a knowledge graph to enhance its search engine results by connecting related terms, disambiguating ambiguous queries, and delivering precise answers directly in the search results. This allows users to find information more efficiently and improves the overall user experience.

Recommendation systems

In recommendation systems, knowledge graphs are used to improve suggestions by mapping the relationships between users, products, and preferences. These graphs help identify patterns and connections in user behavior, enabling more accurate and personalized recommendations. For instance, in e-commerce, a knowledge graph can help recommend products based on a user’s past behavior, similar user preferences, and contextual factors like trends or seasonality. By understanding the entities involved and how they relate to one another, the system can suggest items that are contextually relevant and more likely to resonate with the user.

Credit: Movie recommendation and search using an IMDB knowledge graph by AWS ML blog

Customer relationship management (CRM)

In Customer Relationship Management (CRM) systems, knowledge graphs enable businesses to map and understand the relationships between customers, sales, service interactions, and other key entities. This holistic view helps improve customer service, personalization, and support by providing a deeper understanding of customer needs and behaviors. By linking various data points (e.g., customer history, preferences, interactions), businesses can tailor their outreach, improve customer retention, and increase satisfaction. The knowledge graph also helps uncover insights and identify opportunities for upselling, cross-selling, and enhancing customer engagement.

Credit: How Knowledge Graphs Enable Master Data Management and Customer 360 Systems by Daniel Crowe

Fraud detection

In fraud detection, knowledge graphs help identify suspicious activities by mapping relationships between entities such as individuals, transactions, and accounts. By analyzing patterns and detecting anomalies within these relationships, knowledge graphs can highlight potentially fraudulent behaviors. For example, in banking, a knowledge graph can help detect unusual patterns like multiple accounts linked to the same phone number or address, or a sudden spike in transactions between specific entities. By understanding how entities are connected, the system can raise alerts on potentially fraudulent activities, enabling quicker responses and more accurate risk assessment.

Credit: A knowledge graph showing BTC transaction chain by Coinbase

AI and machine learning

In AI and machine learning, knowledge graphs enhance the performance of algorithms by providing a structured, interconnected representation of data. They offer a rich source of information that can be used to train models, improve reasoning, and enhance decision-making. By incorporating relationships between entities, knowledge graphs allow AI systems to make inferences and predictions based on a deeper understanding of the data. For instance, in healthcare, a knowledge graph could help an AI system recognize patterns between diseases, symptoms, and treatments, leading to more accurate predictions or diagnoses.

Figure: Graph-based RAG (read more on PuppyGraph GraphRAG)

Techniques for knowledge graph analysis

Knowledge graph analysis involves using various computational techniques to extract insights and make predictions from graph-structured data. The methods employed encompass operations research, statistics, and machine learning. Below are some key techniques used for analyzing knowledge graphs.

Graph traversal algorithms

Graph traversal methods like Breadth-First Search (BFS) and Depth-First Search (DFS) help explore graphs to find paths or detect patterns. BFS finds the shortest path by exploring level by level, while DFS explores deep into branches. Dijkstra’s Algorithm finds the shortest path in weighted graphs, and A Search* speeds this up using heuristics.

Centrality measures

Centrality measures help identify important nodes in a graph. Degree centrality counts the connections a node has. Betweenness centrality shows how often a node is on the shortest path between others. Closeness centrality measures how quickly a node can reach all other nodes. Eigenvector centrality considers the importance of neighbors, while PageRank ranks nodes based on the importance of their connections.

Graph clustering

Graph clustering groups nodes into communities based on their connections. Modularity-based clustering identifies communities with dense internal connections. Spectral clustering uses the graph’s properties to detect clusters, while K-means clustering groups nodes based on both their attributes and relationships.

Graph embedding techniques

Graph embedding techniques map graph nodes into vectors to capture their relationships. Node2Vec uses random walks to learn node similarities, GraphSAGE aggregates information from neighbors, and TransE represents entities and relationships as vectors to predict missing links.

Link prediction

Link prediction predicts future connections between nodes. Common neighbors and the Jaccard Index predict links based on shared neighbors. Matrix factorization techniques like SVD uncover hidden patterns and are used in recommendation systems to predict missing relationships.

Graph neural networks (GNNs)

Graph Neural Networks (GNNs) are machine learning models that process graph data. GCNs aggregate information from neighboring nodes, GATs use attention to focus on important neighbors, and GINs are specialized for distinguishing different graph structures. GNNs are useful for node classification, link prediction, and graph classification.

Ontology alignment and matching

Ontology alignment and matching connect different knowledge graphs or ontologies. Semantic matching compares entities based on their meanings, while graph matching algorithms (like Graph Edit Distance) find optimal mappings between nodes and edges to integrate data from multiple sources.

Graph visualization

Graph visualization helps display the structure and relationships in graphs. Force-directed layouts arrange nodes based on attractive and repulsive forces for clear structure. Node-Link Diagrams show nodes and connections, while Circular and Hierarchical layouts are used for specific graph types like hierarchical or cyclic graphs.

Figure:The PuppyGraph Graph-Native Dashboard

Real-time graph analytics

Real-time graph analytics processes data as it is updated, ideal for applications like fraud detection or social media monitoring. Streaming graph algorithms adjust the analysis as new data arrives, using techniques like incremental clustering to update insights quickly.

Challenges and Considerations

Building and maintaining knowledge graphs comes with several key challenges that must be addressed to ensure their effectiveness. Here are some of the most important ones:

Data sparsity

Knowledge graphs can suffer from data sparsity, meaning they may not always have enough information to represent all entities and their relationships. This can lead to incomplete or inaccurate graphs. To solve this, methods like data augmentation (gathering more data), relation prediction (predicting missing relationships), and information fusion (combining data from multiple sources) can help fill in the gaps.

Knowledge updates and maintenance

Keeping a knowledge graph up to date is essential for its accuracy. As new information becomes available, it’s important to regularly update the graph. Challenges include the complexity of updates (as they may involve multiple changes), data consistency (avoiding contradictions), and the cost of frequent updates. Strategies like automated updates, incremental updates (only changing what's necessary), and version control can help address these issues.

Data privacy and security

Since knowledge graphs often store sensitive information, data privacy and security are major concerns. Risks include data breaches and misuse of data. To protect privacy, organizations can use data encryption, implement access controls, and apply privacy-preserving techniques like differential privacy.

Scalability

As knowledge graphs grow, they face challenges in terms of data storage, query performance, and system resources. To ensure scalability, techniques like distributed storage, indexing and caching for faster queries, and horizontal scaling (adding more resources as needed) can help manage large-scale graphs effectively.

Addressing these challenges is crucial for ensuring that knowledge graphs remain accurate, secure, and useful as they scale.

Best practices for knowledge graph implementations

Effective knowledge graph implementation requires careful attention to best practices at each stage of the building process as we talked about previously. These practices ensure the resulting graph is reliable, maintainable, and valuable for its intended purposes.

For knowledge extraction, establish rigorous data preprocessing workflows to ensure quality input. Implement named entity recognition systems with high accuracy thresholds, and develop clear rules for relationship extraction. Create validation mechanisms to verify the accuracy of extracted triples before they enter the graph. Consider implementing machine learning models that can learn from human corrections to improve extraction accuracy over time.

During knowledge fusion, maintain detailed source attribution for all integrated data to enable traceability and quality assessment. Develop robust entity resolution systems to accurately identify and merge duplicate entities across sources. Establish clear conflict resolution protocols for when different sources provide contradicting information. Create mapping rules to standardize relationship types and entity categories across various data sources.

Knowledge processing requires implementing verification mechanisms to validate new inferences before adding them to the graph. Develop clear criteria for what constitutes high-quality relationships and establish thresholds for confidence scores. Create systematic approaches for handling uncertainty and incomplete information. Regular audits of inferred relationships help maintain graph quality and identify areas needing improvement.

For knowledge updating, implement change tracking systems to monitor modifications to the graph over time. Develop clear protocols for managing schema evolution as domain understanding grows. Create efficient incremental update mechanisms that minimize disruption to existing graph structures. Establish regular quality assessment cycles to evaluate the graph's completeness, accuracy, and consistency.

The success of a knowledge graph implementation depends on maintaining consistent quality standards across all these stages while ensuring the graph remains flexible enough to evolve with changing needs. Regular monitoring, documentation of processes, and continuous refinement of practices ensure the long-term value and sustainability of the knowledge graph.

Conclusion

Knowledge graphs have reshaped how we organize and analyze information, enabling organizations to uncover connections within complex data. As the volume and complexity of information grow, knowledge graphs will play an increasingly important role in helping businesses make sense of their data and gain valuable insights.

If you’re ready to build a knowledge graph, download PuppyGraph’s free developer edition and start in just 10 minutes. PuppyGraph is the first graph query engine that runs analytics directly on relational databases without requiring ETL.

Watch a 3-minute demo to see how PuppyGraph integrates with PostgreSQL, define a graph schema, and run your first query. [Download now](insert link) and explore the possibilities with your data.

Sa Wang is a Software Engineer with exceptional math abilities and strong coding skills. He earned his Bachelor's degree in Computer Science from Fudan University and has been studying Mathematical Logic in the Philosophy Department at Fudan University, expecting to receive his Master's degree in Philosophy in June this year. He and his team won a gold medal in the Jilin regional competition of the China Collegiate Programming Contest and received a first-class award in the Shanghai regional competition of the National Student Math Competition.

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  • Available via AWS AMI & Docker install
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