Knowledge Graph Market by Offering (Solutions, Services), By Data Source (Structured, Unstructured, Semi-structured), Industry (BFSI, IT & ITeS, Telecom, Healthcare), Model Type, Application, Type and Region - Global Forecast to 2028
Knowledge Graph Market Share, Forecast & Growth Analysis
[242 Pages Report] The Knowledge Graph Market size in terms of revenue was reasonably estimated at $0.9 billion in 2023. It is anticipated to grow at a Compound Annual Growth Rate (CAGR) of 21.8%. The revenue forecast for 2028 is set to enjoy a valuation of $2.4 billion. The base year considered for estimation is 2022 and the historical data span from 2023 to 2028.
The increasing volume, velocity, and variety of big data have led to the need for efficient data processing and analysis. Knowledge graphs enable real-time data processing, helping organizations make quick, data-driven decisions based on the most up-to-date information available. Also, with advancements in NLP, there is a growing need for more sophisticated data models that can understand and process human language effectively. Knowledge graphs play a crucial role in enhancing NLP capabilities by enabling machines to comprehend the context and relationships between words and phrases.
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Knowledge Graph Market Growth Dynamics
Driver: AI and ML to drive market growth
The explosion of data generated by businesses and individuals, combined with advanced AI (Artificial Intelligence) and ML (Machine Learning) algorithms, has become the cornerstone of knowledge graph applications. Furthermore, the availability of high-performance computing resources and the prevalence of cloud computing platforms have made it easier to process vast amounts of data and deploy complex AI models. The Internet of Things (IoT) has added to this momentum, as AI/ML enables the extraction of valuable insights from IoT data to enrich knowledge graphs. Additionally, Natural Language Processing (NLP) technologies have improved the ability to understand and extract information from textual data, enhancing knowledge graph construction. Across various industries, AI and ML are being adopted to automate tasks, ensure regulatory compliance, and create personalized experiences, all contributing to market growth. Moreover, the recent pandemic accelerated digital transformation efforts, emphasizing the need for AI and ML solutions in knowledge graph applications to meet evolving user expectations.
Restraint: Cost of development and maintenance
The cost of developing and maintaining a knowledge graph for the market can vary significantly based on several factors such as complexity of the domain, the scale of the knowledge graph, the technology stack used, and the ongoing maintenance requirements. The scope and complexity of the knowledge graph plays a pivotal role, more extensive and intricate graphs tend to incur higher development costs. Data acquisition is another cost consideration, as obtaining high-quality data sources may require purchasing data or developing data collection tools. The choice of technology stack, including licensing fees and operational costs for cloud-based solutions, can impact expenses. Skilled development teams, ontology and schema design, and ongoing maintenance all contribute to the overall cost. Scalability, security, and compliance requirements further add to expenses.
Opportunity: NLP to boost knowledge graph market
The integration of Natural Language Processing (NLP) techniques into the knowledge graph market presents a wealth of opportunities for data enrichment and enhanced user experiences. NLP enables the extraction of entities, relations, and facts from unstructured text data, enriching the knowledge graph with valuable information. It allows for sentiment analysis, contextual understanding, and the ability to process natural language queries, making knowledge graphs more accessible and user-friendly. NLP can also improve data quality, aid in personalization, and facilitate trend analysis. Overall, the synergy between NLP and knowledge graphs empowers organizations to unlock deeper insights from their data, promote efficient data integration, and provide more meaningful interactions with their knowledge graph-based systems across various domains.
Challenge: Data quality and integration
Data quality and integration are indeed significant challenges when it comes to building and maintaining knowledge graphs. Achieving the full potential of knowledge graphs relies on the accuracy and reliability of the underlying data. Inaccuracies and inconsistencies can lead to erroneous insights, emphasizing the need for rigorous data quality measures. Furthermore, integrating diverse data sources, each with their own formats and structures, requires intricate schema mapping and transformation processes. Semantic interoperability and entity resolution are additional hurdles to overcome to ensure meaningful connections within the knowledge graph. Scalability, performance optimization, and adherence to data security and privacy standards are crucial for sustained success.
Knowledge graph market Ecosystem
Prominent companies in this market include a well-established, financially stable provider of the knowledge graph market. These companies have innovated their offerings and possess a diversified product portfolio, state-of-the-art technologies, and marketing networks. Prominent companies in this market include IBM (US), Microsoft (US), AWS (US), Neo4j (US), TigerGraph (US), SAP (Germany), Oracle (US), Stardog (US), Franz Inc (US), Ontotext (Bulgaria).
By data source, the structured data segment is expected to grow with the highest CAGR during the forecast period
Integrating structured data sources within the knowledge graph market fundamentally transforms how businesses process and utilize information. By seamlessly merging various data repositories, such as databases and organized datasets, into the knowledge graph framework, companies can now establish a comprehensive understanding of complex relationships and interconnections between different entities. This integration also facilitates the semantic enrichment of the knowledge graph, imbuing it with contextual depth and meaning. Additionally, structured data sources aid in efficient entity resolution, ensuring data accuracy and consistency by identifying and consolidating similar entities. Moreover, the utilization of structured data serves as the backbone for creating a structured knowledge representation within the knowledge graph, enabling businesses to grasp intricate knowledge domains and make informed decisions based on reliable insights.
By vertical, the BFSI segment to hold the largest market size during the forecast period
In recent years, the Banking, Financial Services, and Insurance (BFSI) sector has increasingly embraced the transformative power of knowledge graphs. These sophisticated tools have proven instrumental in managing the complex web of data inherent in the industry. By integrating disparate data sources, organizations can comprehensively understand their operations and customer interactions. Furthermore, knowledge graphs are vital in risk management and compliance, enabling institutions to identify, assess, and mitigate various risks while ensuring adherence to regulatory standards. In fraud detection and prevention, these graphs excel at identifying anomalies and suspicious patterns in real-time. With data security and privacy becoming increasingly critical, knowledge graphs play a crucial role in enhancing data security measures and privacy controls.
Based on region, North America hold the largest market size during the forecast period
The knowledge graph market in North America has been experiencing significant growth and development. Large enterprises across various sectors have been actively adopting knowledge graphs to enhance data integration, knowledge management, and decision-making processes. Increased investment and innovation have fueled the advancement of knowledge graph technologies, making them more adaptable and scalable to different industries and use cases. Integration with AI and machine learning has enabled more sophisticated data analysis and predictive modeling, leading to better-informed decision-making and improved operational efficiencies. Data governance and compliance have also been key focus areas, with knowledge graphs aiding in ensuring data quality, integrity, and security.
Market Players:
The major players in the Knowledge graph market are IBM (US), Microsoft (US), AWS (US), SAP (US), Neo4j (US), and Oracle (US). These players have adopted various growth strategies, such as partnerships, agreements and collaborations, new product launches, product enhancements, and acquisitions to expand their footprint in the knowledge graph market.
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Report Metrics |
Details |
Market size available for years |
2018-2028 |
Base year considered |
2022 |
Forecast period |
2023–2028 |
Forecast units |
Value (USD) Million/Billion |
Segments Covered |
Offering (Solutions and Services), Model Type (RDF Graph, Conceptual Graph, and Semantic Graph), Data Source (Structured Data, Unstructured Data, and Semi-structured Data), Application (Semantic Search, Question Answering, Recommendation Systems, Enterprise Knowledge Management, Other Applications), Type (Context-rich Knowledge Graphs, External-sensing Knowledge Graphs, NLP Knowledge Graphs), Vertical, and Region |
Region covered |
North America, Europe, Asia Pacific, Middle East & Africa, Latin America |
Companies covered |
IBM (US), Microsoft (US), AWS (US), Neo4j (US), TigerGraph (US), SAP (Germany), Oracle (US), Stardog (US), Franz Inc (US), Ontotext (Bulgaria), Semantic Web Company (Austria), OpenLink Software (US), MarkLogic (US), Datavid (UK), GraphBase (Australia), Cambridge Semantics (US), CoverSight (US), Eccena Gmbh (Germany), ArangoDB (US), Fluree (US), DiffBot (US), Bitnine (US), Memgraph (England), GraphAware (UK), Onlim (Austria) |
This research report categorizes the knowledge graph market based on offering, model type, data source, application, type, vertical, and region.
Based on the Offering:
- Solutions
-
Services
- Professional Services
- Managed Services
Based on the Model Type:
- RDF Graph
- Conceptual Graph
- Semantic Graph
Based on Data Source
- Structured Data
- Unstructured Data
- Semi-structured Data
Based on the Application:
- Semantic Search
- Question Answering
- Recommendation Systems
- Enterprise Knowledge Management
- Other Applications
Based on the Type:
- Context-rich Knowledge Graphs
- External-sensing Knowledge Graphs
- NLP Knowledge Graphs
Based on the Vertical:
- BFSI
- IT & ITES
- Retail and E-commerce
- Travel and Hospitality
- Healthcare
- Media and Entertainment
- Transportation and Logistics
- Other Verticals
Based on the region:
-
North America
- US
- Canada
-
Europe
- UK
- Germany
- France
- Spain
- Italy
- Rest of Europe
-
Asia Pacific
- China
- Japan
- India
- Australia and New Zealand (ANZ)
- Rest of Asia Pacific
-
Middle East & Africa
- GCC
- South Africa
- Rest of Middle East & Africa
-
Latin America
- Brazil
- Mexico
- Rest of Latin America
Recent Developments
- In February 2023, IBM acquired StepZen, which developed a GraphQL server with a unique architecture that helps developers build GraphQL APIs quickly and with less code. StepZen was also designed to be highly flexible. It is compatible with other API approaches and is available Software-as-a-Service (SaaS) while supporting deployments in private clouds and on-premises data centers.
- In May 2023, AWS partnered with Neo4j , which defined the graph space and open-source standards. Neo4j holds the AWS Data and Analytics Competency.
- In April 2023, Neo4j announced a partnership with Imperium Solutions to fulfill the growing demand for graph technology in Singapore. Imperium Solutions will ensure customers can gain maximum value from the world’s leading graph database provider, Neo4j, which helps solve complex, enterprise-level problems and efficiently uncovers relationships and patterns in expansive datasets.
- In May 2023, Accenture has made a strategic investment through Accenture Ventures in Stardog, a leading enterprise knowledge graph platform enabling organizations to do more with and achieve greater value from their data in this age of generative artificial intelligence (AI). Stardog Enterprise Knowledge Graphs, with their ability to make real-world context machine-understandable, are used by companies to facilitate better enterprise data integration and unification. Instead of integrating data by combining tables, data is unified using a knowledge graph’s ability to endlessly link concepts without changing the underlying data.
Frequently Asked Questions (FAQ):
What is the definition of the knowledge graph market?
Knowledge graphs are networks of interconnected data that describe real-world entities and their relationships. They are more than just static databases of facts; they can be used to generate new knowledge and insights.
Unlike traditional databases, which typically store data in a flat structure, knowledge graphs use a graph database model to represent data as nodes and edges. Nodes represent entities, such as people, places, and things. Edges represent relationships between entities.
What is the market size of the knowledge graph market?
The knowledge graph market size is projected to grow from USD 0.9 billion in 2023 to USD 2.4 billion by 2028, at a CAGR of 21.8% during the forecast period.
What are the major drivers in the knowledge graph market?
The major drivers of the knowledge graph market are swift increase in the volume & complexity of data, technologies like AI, ML to drive market growth, semantic web & linked data initiatives to boost the market.
Who are the key players operating in the knowledge graph market?
The major players in the knowledge graph market are IBM (US), Microsoft (US), AWS (US), Neo4j (US), TigerGraph (US), SAP (Germany), Oracle (US), Stardog (US), Franz Inc (US), Ontotext (Bulgaria), Semantic Web Company (Austria), OpenLink Software (US), MarkLogic (US), Datavid (UK), GraphBase (Australia), Cambridge Semantics (US), CoverSight (US), Eccena Gmbh (Germany), ArangoDB (US), Fluree (US), DiffBot (US), Bitnine (US), Memgraph (England), GraphAware (UK), Onlim (Austria).
What are the opportunities for new market entrants in the knowledge graph market?
The major opportunities of the knowledge graph market are NLP to boost knowledge graph market, and Increasing adoption in healthcare and life sciences.
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The study involved four major activities in estimating the current size of the global knowledge graph market. Exhaustive secondary research was done to collect information on the market, peer market, and parent market. The next step was to validate these findings, assumptions, and sizing with industry experts across the value chain through primary research. Both top-down and bottom-up approaches were employed to estimate the total knowledge graph market size. After that, the market breakup and data triangulation techniques were used to estimate the market size of segments and subsegments.
Secondary Research
In the secondary research process, various secondary sources, such as Bloomberg and BusinessWeek, have been referred to identify and collect information for this study. The secondary sources included annual reports, press releases, and investor presentations of companies; white papers; and journals, such as Linux Journal and Container Journal, and articles from recognized authors, directories, and databases.
Primary Research
Various primary sources from both supply and demand sides were interviewed to obtain qualitative and quantitative information for this report. The primary sources from the supply side included industry experts, such as Chief Executive Officers (CEOs), Chief Marketing Officers (CMO), Vice Presidents (VPs), Managing Directors (MDs), technology and innovation directors, and related key executives from various key companies and organizations operating in the knowledge graph market along with the associated service providers, and system integrators operating in the targeted regions. All possible parameters that affect the market covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data. Following is the breakup of primary respondents.
Company Name |
Designation |
Neo4j |
Senior Manager |
Stardog |
VP |
IBM |
Business Executive |
Market Size Estimation
For making market estimates and forecasting the knowledge graph market, and other dependent submarkets, the top-down and bottom-up approaches were used. The bottom-up procedure was used to arrive at the overall market size of the global knowledge graph market using key companies’ revenue and their offerings in the market. The research methodology used to estimate the market size includes the following:
- The key players in the knowledge graph market have been identified through extensive secondary research.
- The market size, in terms of value, has been determined through primary and secondary research processes.
- All percentage shares, splits, and breakups have been determined using secondary sources and verified through primary sources.
Knowledge graph market Size: Bottom-Up Approach
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Knowledge graph market Size: Top-Down Approach
Data Triangulation
With data triangulation and validation through primary interviews, the exact value of the overall parent market size was determined and confirmed using this study. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.
Unlike traditional databases, which typically store data in a flat structure, knowledge graphs use a graph database model to represent data as nodes and edges. Nodes represent entities, such as people, places, and things. Edges represent relationships between entities.
Market Definition
Knowledge graphs are networks of interconnected data that describe real-world entities and their relationships. They are more than just static databases of facts; they can be used to generate new knowledge and insights.
Key Stakeholders
- Knowledge Graph Solution Providers
- Independent Software Vendors (ISVs)
- Investors and Venture Capitalists (VCs)
- Managed Service Providers
- Support and Maintenance Service Providers
- System Integrators (SIs)/Migration Service Providers
- Value-Added Resellers (VARs) and Distributors
Report Objectives
- To determine, segment, and forecast the global knowledge graph market by offering, model type, application, type, vertical, and region in terms of value.
- To forecast the size of the market segments to five main regions: North America, Europe, Asia Pacific, Middle East & Africa, and Latin America
- To provide detailed information about the major factors (drivers, opportunities, threats, and challenges) influencing the growth of the market
- To study the complete value chain and related industry segments and perform a value chain analysis of the market landscape.
- To strategically analyze the macro and micro markets to individual growth trends, prospects, and contributions to the total market
- To analyze the industry trends, pricing data, patents, and innovations related to the market.
- To analyze the opportunities for stakeholders by identifying the high-growth segments of the knowledge graph market
- To profile the key players in the market and comprehensively analyze their market share/ranking and core competencies.
- To track and analyze competitive developments, such as mergers & acquisitions, product launches & developments, partnerships, agreements, collaborations, business expansions, and Research & Development (R&D) activities.
Available Customizations
With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:
Company Information
- Detailed analysis and profiling of an additional two market players
Growth opportunities and latent adjacency in Knowledge Graph Market