Knowledge Graph Market

According to a research report "Knowledge Graph Market by Solution (Enterprise Knowledge Graph Platform, Graph Database Engine, Knowledge Management Toolset), Model Type (Resource Description Framework (RDF) Triple Stores, Labeled Property Graph) - Global Forecast to 2030", published by MarketsandMarkets, the Knowledge graph market is expected to reach USD 6,938.4 million by 2030 from USD 1,068.4 million in 2024, at a Compound Annual Growth Rate (CAGR) of 36.6 % from 2024–2030. The knowledge graphs ensure enterprise knowledge management through the rebuilding of complex data with interconnected nodes and relationships by providing a simpler way to navigate and retrieve information. It helps businesses build a fully comprehensive knowledge graph uniting disparate data sources, enables complex semantic search, context-aware recommendations, and data discovery. Knowledge graphs support better decision-making, foster innovation, and improve cooperation across teams by mapping relationships between organizational knowledge. They are particularly useful for large organizations, which depend on accessing and utilizing vast amounts of structured and unstructured data to be productive and competitive.

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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.

Market Players

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), Ontotext (Bulgaria), Openlink Software (US), Graphwise (US), Altair (US), Bitnine ( South Korea) ArangoDB (US), Fluree (US), Memgraph UK), GraphBase (Australia), Metaphacts (Germany), Relational AI (US), Wisecube (US), Smabbler (Poland), Onlim (Austria), Graphaware (UK), Diffbot (US), Eccenca (Germany), Conversight (US), Semantic Web Company (Austria), ESRI (US), Datavid (UK), and SAP (Germany). 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.

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