The size of the worldwide knowledge graph market is expected to increase at a Compound Annual Growth Rate (CAGR) of 36.6% from USD 1,068.4 million in 2024 to USD 6,938.4 million by 2030. Through transparent data management procedures, companies must fulfill high compliance standards across multimodal databases due to the stringent data protection obligations imposed by laws like the CCPA and GDPR. Knowledge graph engines offer a structured, networked structure that enhances accountability and traceability. The process of mapping data linkages between different systems facilitates the effective monitoring of data movements and the administration of personal data and consent. Businesses are able to react swiftly to regulatory assessments and access requests, which improves consumer confidence and compliance.
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“By vertical, the BFSI segment to hold the largest market size during the forecast period.”
The knowledge graphs serve as a strong foundation for relating customer data, transactions history, credit scores, and risk profiles within the BFSI (Banking, Financial Services, and Insurance) sector, allowing the exact relationship mapping and insights. These are also employed in fraud detection through real-time identification of hidden patterns and for regulatory compliance with standards such as AML (Anti Money Laundering) and KYC (know Your Customer), where data can be traced and is transparent. In banking, knowledge graphs facilitate credit risk analysis which makes the process of loan approval more efficient, in insurance by linking policies, claims data, and fraud indicators thus optimizing claims processing. All these will, when combined with other data points, produce AI-powered applications: personalized advice-based solutions on finances and intelligent virtual assistants, which will create operational efficiency and improved customer experience in BFSI.
“Virtual assistants, self-service data, and digital asset discovery segment to have the highest growth during the forecast period.”
Knowledge graphs are essential for building virtual assistants, self-service data platforms, and even digital asset discovery, for they build interconnected data networks that help in enhancing the searchability and insights. Virtual assistants use knowledge graphs to provide context-sensitive responses that improve user interactions and provide tailored recommendations. Self-service data platforms use knowledge graphs to allow business users to access and analyze complex datasets without technical help, which helps them to make better decisions. They make the identification and classification of digital resources, such as documents or media, easier through linking metadata and content relationships for the discovery of digital assets. This capability enables effective resource management, innovation, and improvement in user experience in areas such as content creation, research, and enterprise workflows.
“Asia Pacific is expected to witness the highest market growth rate during the forecast period.”
The knowledge graph landscape is rapidly evolving in Asia Pacific, with initiatives across various domains. In December 2022, the National Library Board (NLB), Singapore, launched a Linked Data-based Semantic Knowledge Graph to merge resources from libraries and archives using BIBFRAME and Schema.org vocabularies for seamless updating and improved data quality. HydroKG in Australia merges hydrologic data from resources such as GeoFabric and HydroATLAS that allow for pinpoint queries on water bodies and river networks, enabling better environmental management. Japan uses knowledge graphs in manufacturing for supply chain optimization and South Korea uses it in telecommunications to enhance the customer experience through personalized AI.
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Unique Features in the Knowledge Graph Market
Knowledge graphs encode not just data but meaning: entities, classes, relationships and constraints are defined in ontologies. That semantic layer lets systems understand context (e.g., “author-of” vs “works-with”) and enables more accurate linking and richer queries than tabular data alone.
Unlike rigid relational schemas, knowledge graphs support flexible, extensible schemas that evolve over time. New entity types or relationships can be added without expensive migrations, making them ideal for rapidly changing domains.
A core capability is deduplicating and canonicalizing real-world entities across diverse sources (e.g., multiple spellings, IDs, or aliases). Sophisticated matching improves data quality and creates a single, reliable view of people, products, locations, and concepts.
Knowledge graphs excel at fusing heterogeneous data — structured, semi-structured, and unstructured — into a unified graph. They preserve provenance and reconcile schema differences, enabling integrated insights across systems that previously didn’t speak the same language.
Major Highlights of the Knowledge Graph Market
The Knowledge Graph market is witnessing strong adoption across sectors such as IT & telecommunications, healthcare, e-commerce, BFSI, manufacturing, and government. Organizations are leveraging knowledge graphs to unify siloed data, enhance search accuracy, enable personalized recommendations, and improve operational efficiency.
Knowledge graphs are increasingly being integrated with AI and ML systems to provide contextual understanding and explainability. They serve as foundational structures for large language models (LLMs) and generative AI applications, enhancing reasoning, data enrichment, and decision-making accuracy.
Enterprises are turning to knowledge graphs to achieve a unified view of entities such as customers, products, and assets. By linking disparate data sources and maintaining semantic consistency, they enable deeper insights and more accurate business intelligence.
Vendors such as Neo4j, AWS Neptune, Microsoft Azure Cosmos DB, and Ontotext are driving innovation with scalable, graph-native storage and query capabilities. These platforms are expanding their ecosystems with advanced visualization, analytics, and API support to serve large-scale enterprise deployments.
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Top Companies in the Knowledge Graph Market
The major vendors covered in the Knowledge graph market are IBM Corporation (US), Oracle (US), Microsoft Corporation (US), AWS (US), Neo4j (US), Progress Software (US), TigerGraph (US), Stardog (US), Franz Inc (US), Openlink Software (US), Graphwise (US), Altair (US), Bitnine ( South Korea), ArangoDB (US), Fluree (US), Memgraph (UK), GraphBase (Australia), Metaphacts (Germany), RelationalAI (US), Wisecube (US), Smabbler (Poland), Onlim (Austria), Graphaware (UK), Diffbot (US), Eccenca (Germany), Conversight (US), ESRI (US). These players have adopted various growth strategies, such as partnerships, agreements and collaborations, new product launches, enhancements, and acquisitions to expand their footprint in the Knowledge graph market.
Neo4j
Neo4j is a leading knowledge graph provider, notable for its graph database technology that enables organizations to model, store, and analyze many different types of interconnected data. Offerings include Neo4j AuraDB, a fully managed cloud graph database, and Neo4j Graph Data Science, which allows advanced analytics and machine learning on graph data stored on multimodal databases. A recent strategic collaboration was formed by Neo4j with Snowflake to integrate Neo4j’s graph technology into Snowflake AI Data Cloud. This development aims to enhance data analytics capabilities by combining Snowflake’s scalable data platform with Neo4j’s graph-based insights, to help customers uncover deeper relationships within their data. The partnership reinforces Neo4j’s commitment to innovation, embedding the graph technology deeper in enterprise applications, specifically AI and data-driven decision-making.
Graphwise
Graphwise is a major market player when it comes to knowledge graphs, specializing in the effective handling and utilization of interconnected information to gain actionable insights and meet compliance standards. Graphwise is a new venture formed by the recent merger between Ontotext and Semantic Web Company, combining Ontotext’s strength in large-scale enterprise knowledge graph platforms with Semantic Web Company’s capability in semantic web technology and enterprise knowledge graphs. The merger strengthens Graphwise’s position as a leader in the industry, enabling it to deliver comprehensive tools for data integration, semantic search, and AI-driven analytics. These solutions address a wide range of industries, helping organizations improve data transparency, traceability, and decision-making in healthcare, finance and publishing sectors. The unified organization is set to drive innovation in knowledge graph technologies at a faster pace to meet increasing demand for regulatory compliance and data-driven intelligence.
Progress Software
Progress Software stands as a significant player within the knowledge graph industry by providing its MarkLogic Enterprise Knowledge Graph Platform among its offerings. Organizations can integrate and control intricately interconnected data from various sources while analyzing it through this solution to achieve a unified perspective that boosts decision-making processes. MarkLogic’s knowledge graph capabilities prove essential for applications such as data harmonization, semantic search, and regulatory compliance which makes it a favored option across healthcare, finance, and government sectors. Through its multimodal database architecture, Progress Software enables businesses to detect concealed connections while enhancing data traceability and generating actionable insights. Progress Software’s dedication to providing scalable, secure, and interoperable knowledge graph technologies is demonstrated through the MarkLogic solution which meets enterprise demands for data-driven intelligence and transparency.
Franz Inc
Franz Inc. is a provider of Graph Database technology; it specializes in developing and deploying Knowledge Graph and Data Fabric solutions. It offers AllegroGraph, a high-performance, transactional, and highly scalable Graph Database, which provides the solid storage layer for powerful enterprise grade Knowledge Graph and AI applications. Franz Inc’s competitive edge lies in its emphasis on scalability, resilience, and its adeptness in managing complex data analytics tasks. While consistently meeting the complex needs of global businesses, Franz’s focus remains on forming long-term relationships through personalized services and expertise.
Bitnine
Bitnine is a South Korean database management company specializing in graph database solutions. They are known for developing AgensGraph, a multi-model database that integrates relational and graph data models, enabling efficient handling of complex data relationships. AgensGraph is designed to support various applications, including fraud detection, social network analysis, and recommendation systems. Bitnine focuses on providing scalable and high-performance database solutions to meet the needs of enterprises dealing with large-scale data. These companies are at the forefront of leveraging advanced technologies such as AI, graph databases, and simulation to provide innovative solutions across various industries.
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