Knowledge Graph Market

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.

To know about the assumptions considered for the study, Request for Free Sample Report

To know about the assumptions considered for the study, download the pdf brochure

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.

Want to explore hidden markets that can drive new revenue in Knowledge Graph Market?

Unlock Insights

Click on image to enlarge

Scope of the Report

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):

To speak to our analyst for a discussion on the above findings, click Speak to Analyst