Dublin, Jan. 31, 2025 (GLOBE NEWSWIRE) — The “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” report has been added to ResearchAndMarkets.com’s offering.
The Knowledge Graph market is estimated at USD 1.06 billion in 2024 to USD 6.93 billion by 2030, at a Compound Annual Growth Rate (CAGR) of 36.6%
The report will help the market leaders/new entrants with information on the closest approximations of the global Knowledge Graph market’s revenue numbers and subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. Moreover, the report will provide insights for stakeholders to understand the market’s pulse and provide them with information on key market drivers, restraints, challenges, and opportunities.
The construction of intelligent knowledge graphs through AI is expected to change how organizations deal with large datasets. The effort of human intervention is drastically reduced when it comes to identifying and extricating relationships between different data points. The automation includes the processes carried out by most types of AI-driven tools such as natural language processing (NLP), machine learning algorithms, etc., to automatically interpret, unstructured or structured data, identify relevant patterns, and correlate such relevant information.
This automation speeds up the construction of the graphs and at the same time increases accuracy, ensuring that the relationships represented in it are as relevant and up to date as possible to an end user.
By solution, Graph Database Engine segment to hold the largest market size during the forecast period
Graph Database Engine is a specialized type of database, designed specifically for the efficient storage, management and retrieval of graph data entities (nodes) related by graph relationships (edges). Graph databases do not organize data in tables as in traditional relational systems, but rather as relationships, making them useful in application scenarios where data relationships are paramount, such as social networks, recommendation engines, and fraud detection.
It allows high-speed querying and traversing complex and heavily linked datasets, thus enables a more natural, intuitive, and flexible mechanism of data querying. It further supports graph-specific query languages such as SPARQL and Cypher, which are optimized for querying relationships, thus affording better performance and scalability for graph applications.
The services segment to register the fastest growth rate during the forecast period
Knowledge graph services encompass professional and managed services to an organization for deploying, enhancing, and maintaining knowledge graph solutions. Professional services consist of consulting on the design and development of a strategy, integration of the data, and the creation of a custom-built knowledge graph relevant to a business.
On the other hand, managed services offer support maintenance, and monitoring of the knowledge graph platform for performance, scalability, and security. These services, in their own way, assist clients in sourcing knowledge graphs to their advantage in terms of getting better data, decision intelligence, and AI, and without the burden of their internal management, which is a resource-intensive and cumbersome process.
Asia Pacific to witness the highest market growth rate during the forecast period
In Asia Pacific, the landscape is characterized by initiatives and innovations that try to help adopt and apply graph technologies across the region. In 2021, Neo4j launched Graphs4APAC initiative, which provides free training, materials, and tools to professionals across Asia Pacific to develop and improve their knowledge and skills in graph technology.
This open-source initiative encourages collaborative and local adaptation, and has been successfully implemented in, Indonesia and Singapore. Fujitsu, also, strives to expand the frameworks of knowledge graphs fed by artificial intelligence in the Generative AI Accelerator Challenge (GENIAC) program that focuses on producing dedicated large language models (LLMs) that generate knowledge graphs and allow for inferring such graphs. These are emerging indicators that are significant in portraying how much the region has begun to pay attention to applying knowledge graphs across innovative platforms and data-driven solutions.
The report provides insights on the following pointers:
- Analysis of key drivers (rising demand for AI/generative AI solutions, rapid growth in data volume and complexity, growing demand for semantic search), restraints (data quality and Integration challenges, scalability Issues) opportunities (data unification and rapid proliferation of knowledge graphs, increasing adoption in healthcare and life sciences), and challenges (lack of expertise and awareness, standardization and interoperability) influencing the growth of the Knowledge Graph market.
- Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the Knowledge Graph market.
- Market Development: The report provides comprehensive information about lucrative markets and analyses the Knowledge Graph market across various regions.
- Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the Knowledge Graph market.
- Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading include include 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).
Key Attributes:
| Report Attribute | Details |
| No. of Pages | 360 |
| Forecast Period | 2024 – 2030 |
| Estimated Market Value (USD) in 2024 | $1.06 Billion |
| Forecasted Market Value (USD) by 2030 | $6.93 Billion |
| Compound Annual Growth Rate | 36.6% |
| Regions Covered | Global |
Companies Featured
- Neo4J
- Amazon Web Services, Inc.
- Tigergraph
- Graphwise
- Relationalai
- IBM
- Microsoft
- SAP
- Oracle
- Stardog
- Ontotext
- Franz Inc.
- Altair
- Progress Software Corporation
- Esri
- Semantic Web Company
- Openlink Software
- Datavid
- Graphbase
- Conversight
- Eccenca
- Arangodb
- Fluree
- Diffbot
- Bitnine
- Memgraph
- Graphaware
- Onlim
- Smabbler
- Wisecube
- Metaphacts
For more information about this report visit https://www.researchandmarkets.com/r/vkqyaj
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