Summary

The Knowledge Graph market is estimated at USD 1,068.4 million in 2024 to USD 6,938.4 million by 2030, at a Compound Annual Growth Rate (CAGR) of 36.6%. 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.

In-depth interviews have been conducted with chief executive officers (CEOs), Directors, and other executives from various key organizations operating in the Knowledge Graph market.

• By Company Type: Tier 1 – 40%, Tier 2 – 35%, and Tier 3 – 25%
• By Designation: C-level –40%, D-level – 35%, and Others – 25%
• By Region: North America – 35%, Europe – 40%, Asia Pacific – 20, RoW-5%
The major players in the Knowledge Graph market 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), Datavid (UK), and SAP (Germany), 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). These players have adopted various growth strategies, such as partnerships, agreements and collaborations, new product launches, enhancements, and acquisitions to expand their Knowledge Graph market footprint.

Research Coverage
The market study covers the Knowledge Graph market size across different segments. It aims at estimating the market size and the growth potential across various segments, including by offering (solutions (enterprise knowledge graph platform, graph database engine, knowledge management toolset), services ( professional services, managed services), by model type (Resource Description Framework (RDF) Triple Stores, Labeled Property Graph (LPG)), by applications (data governance and master data management, data analytics and business intelligence, knowledge and content management , virtual assistants, self-service data and digital asset discovery, product and configuration management, infrastructure and asset management, process optimization and resource management, risk management, compliance, regulatory reporting, market and customer intelligence, sales optimization, other applications), by vertical (Banking, Financial Services, and Insurance (BFSI), retail and eCommerce, healthcare, life sciences, and pharmaceuticals telecom and technology, government, manufacturing and automotive, media & entertainment, energy, utilities and infrastructure, travel and hospitality, transportation and logistics, other vertical), and Region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The study includes an in-depth competitive analysis of the leading market players, their company profiles, key observations related to product and business offerings, recent developments, and market strategies.

Key Benefits of Buying the Report
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 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).

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Table of Contents

1 INTRODUCTION 40
1.1 STUDY OBJECTIVES 40
1.2 MARKET DEFINITION 40
1.2.1 INCLUSIONS AND EXCLUSIONS 41
1.3 STUDY SCOPE 42
1.3.1 MARKET SEGMENTATION 42
1.3.2 YEARS CONSIDERED 43
1.4 CURRENCY CONSIDERED 43
1.5 STAKEHOLDERS 44
1.6 SUMMARY OF CHANGES 44
2 RESEARCH METHODOLOGY 45
2.1 RESEARCH DATA 45
2.1.1 SECONDARY DATA 46
2.1.1.1 Key data from secondary sources 46
2.1.2 PRIMARY DATA 47
2.1.2.1 Primary interviews with experts 47
2.1.2.2 Breakdown of primary interviews 47
2.1.2.3 Key insights from industry experts 48
2.2 MARKET SIZE ESTIMATION 48
2.2.1 TOP-DOWN APPROACH 48
2.2.1.1 Supply-side analysis 49
2.2.2 BOTTOM-UP APPROACH 50
2.2.2.1 Demand-side analysis 50
2.3 DATA TRIANGULATION 52
2.4 RESEARCH ASSUMPTIONS 53
2.5 RESEARCH LIMITATIONS 54
2.6 RISK ASSESSMENT 54
3 EXECUTIVE SUMMARY 55
4 PREMIUM INSIGHTS 58
4.1 ATTRACTIVE OPPORTUNITIES FOR KEY PLAYERS IN KNOWLEDGE GRAPH MARKET 58
4.2 KNOWLEDGE GRAPH MARKET, BY OFFERING 58
4.3 KNOWLEDGE GRAPH MARKET, BY SERVICE 59
4.4 KNOWLEDGE GRAPH MARKET, BY MODEL TYPE 59
4.5 KNOWLEDGE GRAPH MARKET, BY APPLICATION 60 
4.6 KNOWLEDGE GRAPH MARKET, BY VERTICAL 60
4.7 NORTH AMERICA: KNOWLEDGE GRAPH MARKET, SOLUTIONS AND SERVICES 61
5 MARKET OVERVIEW AND INDUSTRY TRENDS 62
5.1 INTRODUCTION 62
5.2 MARKET DYNAMICS 62
5.2.1 DRIVERS 63
5.2.1.1 Rising demand for AI/generative AI solutions 63
5.2.1.2 Rapid growth in data volume and complexity 63
5.2.1.3 Growing demand for semantic search 63
5.2.2 RESTRAINTS 63
5.2.2.1 Data quality and integration challenges 63
5.2.2.2 Navigation of saturated data management tool landscape 64
5.2.2.3 Scalability issues 64
5.2.3 OPPORTUNITIES 64
5.2.3.1 Leveraging LLMs to reduce knowledge graph construction costs 64
5.2.3.2 Data unification and rapid proliferation of knowledge graphs 65
5.2.3.3 Increasing adoption in healthcare and life sciences to revolutionize data management and enhance patient outcomes 65
5.2.4 CHALLENGES 65
5.2.4.1 Lack of expertise and awareness 65
5.2.4.2 Standardization and interoperability 66
5.2.4.3 Difficulty in demonstrating full value of knowledge graphs through single use cases 66
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 66
5.4 PRICING ANALYSIS 67
5.4.1 PRICE TREND OF KEY PLAYERS, BY SOLUTION 67
5.4.2 INDICATIVE PRICING ANALYSIS OF KEY PLAYERS 68
5.5 SUPPLY CHAIN ANALYSIS 69
5.6 ECOSYSTEM 71
5.7 TECHNOLOGY ANALYSIS 73
5.7.1 KEY TECHNOLOGIES 73
5.7.1.1 Graph Databases (GDB) 73
5.7.1.2 Semantic web technologies 73
5.7.1.3 Generative AI and Natural Language Processing (NLP) 73
5.7.1.4 GraphRAG 74
5.7.2 COMPLEMENTARY TECHNOLOGIES 74
5.7.2.1 Artificial Intelligence (AI) and Machine Learning (ML) 74
5.7.2.2 Big data 74
5.7.2.3 Graph Neural Networks (GNNS) 74
5.7.2.4 Cloud computing 75
5.7.2.5 Vector databases and Full-Text Search Engines (FTS) 75
5.7.2.6 Multi-model databases 75
5.7.3 ADJACENT TECHNOLOGIES 76
5.7.3.1 Digital twin 76
5.7.3.2 Internet of Things (IoT) 76
5.7.3.3 Blockchain 76
5.7.3.4 Edge computing 76
5.8 PATENT ANALYSIS 77
5.8.1 METHODOLOGY 77
5.8.1.1 List of major patents 78
5.9 KEY CONFERENCES AND EVENTS, 2024–2025 80
5.10 REGULATORY LANDSCAPE 81
5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 81
5.10.2 KEY REGULATIONS 85
5.10.2.1 North America 85
5.10.2.1.1 SCR 17: Artificial Intelligence Bill (California) 85
5.10.2.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut) 85
5.10.2.1.3 National Artificial Intelligence Initiative Act (NAIIA) 85
5.10.2.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada 85
5.10.2.2 Europe 86
5.10.2.2.1 The European Union (EU) - Artificial Intelligence Act (AIA) 86
5.10.2.2.2 EU Data Governance Act 86
5.10.2.2.3 General Data Protection Regulation (Europe) 86
5.10.2.3 Asia Pacific 87
5.10.2.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China) 87
5.10.2.3.2 The National AI Strategy (Singapore) 88
5.10.2.3.3 The Hiroshima AI Process Comprehensive Policy Framework (Japan) 88
5.10.2.4 Middle East & Africa 88
5.10.2.4.1 The National Strategy for Artificial Intelligence (UAE) 88
5.10.2.4.2 The National Artificial Intelligence Strategy (Qatar) 89
5.10.2.4.3 The AI Ethics Principles and Guidelines (Dubai) 89
5.10.2.5 Latin America 90
5.10.2.5.1 The Santiago Declaration (Chile) 90
5.10.2.5.2 The Brazilian Artificial Intelligence Strategy (EBIA) 90
5.11 PORTER’S FIVE FORCES ANALYSIS 91
5.11.1 THREAT OF NEW ENTRANTS 92
5.11.2 THREAT OF SUBSTITUTES 92
5.11.3 BARGAINING POWER OF BUYERS 92
5.11.4 BARGAINING POWER OF SUPPLIERS 92
5.11.5 INTENSITY OF COMPETITIVE RIVALRY 92 
5.12 KEY STAKEHOLDERS & BUYING CRITERIA 93
5.12.1 KEY STAKEHOLDERS IN BUYING PROCESS 93
5.12.2 BUYING CRITERIA 93
5.13 BRIEF HISTORY OF KNOWLEDGE GRAPH 94
5.14 STEPS TO BUILD KNOWLEDGE GRAPH 95
5.14.1 DEFINE OBJECTIVES 95
5.14.2 ENGAGE STAKEHOLDERS 95
5.14.3 IDENTIFY KNOWLEDGE DOMAIN 95
5.14.4 GATHER AND ANALYZE DATA 96
5.14.5 CLEAN AND PREPROCESS DATA 96
5.14.6 CREATE SEMANTIC DATA MODEL 96
5.14.7 SCHEMA DEFINITION 96
5.14.8 DATA INTEGRATION 96
5.14.9 HARMONIZATION OF DATA 96
5.14.10 BUILD KNOWLEDGE GRAPH 96
5.14.11 AUGMENT GRAPH 96
5.14.12 TESTING AND VALIDATION 96
5.14.13 MAXIMIZE USABILITY 96
5.14.14 CONTINUOUS MAINTENANCE AND EVOLUTION 96
5.15 IMPACT OF AI/GENERATIVE AI ON KNOWLEDGE GRAPH MARKET 97
5.15.1 USE CASES OF GENERATIVE KNOWLEDGE GRAPH 97
5.16 INVESTMENT AND FUNDING SCENARIO 99
5.17 CASE STUDY ANALYSIS 99
5.17.1 TRANSMISSION SYSTEM OPERATOR LEVERAGED ONTOTEXT’S SOLUTIONS TO MODERNIZE ASSET MANAGEMENT 99
5.17.2 BOSTON SCIENTIFIC STREAMLINED MEDICAL SUPPLY CHAIN USING NEO4J’S GRAPH DATA SCIENCE SOLUTION 100
5.17.3 NATIONAL RETAIL CHAIN FROM UK ENHANCED OPERATIONAL EFFICIENCY USING TIGERGRAPHS’S SOLUTION 101
5.17.4 SCHNEIDER ELECTRIC USED STARDOG TO LEAD SMART BUILDING TRANSFORMATION 101
5.17.5 MEDIA ORGANIZATION USED PROGRESS SEMAPHORE TO CLASSIFY CONTENT FOR BETTER AUDIENCE ENGAGEMENT 102
5.17.6 YAHOO7 REPRESENTED CONTENT WITHIN KNOWLEDGE GRAPH WITH ASSISTANCE OF BLAZEGRAPH 103
5.17.7 DATABASE GROUP HELPED SPRINGERMATERIALS ACCELERATE RESEARCH WITH SEMANTIC SEARCH 103
5.17.8 RFS OPTIMIZED ITS GLOBAL PRODUCT AND INVENTORY MANAGEMENT BY USING ECCENCA’S SOLUTION 104 
6 KNOWLEDGE GRAPH MARKET, BY OFFERING 106
6.1 INTRODUCTION 107
6.1.1 OFFERINGS: KNOWLEDGE GRAPH MARKET DRIVERS 107
6.2 SOLUTIONS 108
6.2.1 SPIKE IN DEMAND FOR SOPHISTICATED DATA MANAGEMENT AND ANALYSIS TO DRIVE MARKET 108
6.2.2 ENTERPRISE KNOWLEDGE GRAPH PLATFORM 110
6.2.2.1 Need to improve discovery of data, promote better decision-making, and enable real-time insights using semantic technologies to propel market 110
6.2.3 GRAPH DATABASE ENGINE 111
6.2.3.1 Features like parallel query execution and AI-driven insights in graph database engines to accelerate market growth 111
6.2.4 KNOWLEDGE MANAGEMENT TOOLSET 112
6.2.4.1 Knowledge management toolsets to enhance operational efficiency by enabling seamless access to organizational knowledge 112
6.3 SERVICES 113
6.3.1 PROFESSIONAL SERVICES 115
6.3.2 MANAGED SERVICES 116
7 KNOWLEDGE GRAPH MARKET, BY MODEL TYPE 117
7.1 INTRODUCTION 118
7.1.1 MODEL TYPES: KNOWLEDGE GRAPH MARKET DRIVERS 118
7.2 RESOURCE DESCRIPTION FRAMEWORK (RDF) TRIPLE STORES 119
7.2.1 RDF-BASED KNOWLEDGE GRAPHS TO FACILITATE APPLICATIONS REQUIRING SEMANTIC INTEROPERABILITY 119
7.3 LABELED PROPERTY GRAPH (LPG) 120
7.3.1 LOGICAL INFERENCE, KNOWLEDGE DISCOVERY, AND STRUCTURED REPRESENTATION OF DATA TO BOOST MARKET GROWTH 120
8 KNOWLEDGE GRAPH MARKET, BY APPLICATION 122
8.1 INTRODUCTION 123
8.1.1 APPLICATIONS: KNOWLEDGE GRAPH MARKET DRIVERS 123
8.2 DATA GOVERNANCE AND MASTER DATA MANAGEMENT 125
8.2.1 NEED FOR ENHANCED SEARCH FUNCTIONALITIES TO BOLSTER MARKET GROWTH 125
8.3 DATA ANALYTICS & BUSINESS INTELLIGENCE 126
8.3.1 INTEGRATION OF KNOWLEDGE FROM SEVERAL DISCIPLINES AND OFFERING PERSONALIZED RECOMMENDATIONS TO BOOST MARKET GROWTH 126
8.4 KNOWLEDGE & CONTENT MANAGEMENT 127
8.4.1 WIDESPREAD KNOWLEDGE OF INTRICATE IDEAS THROUGH CROSS-DOMAIN INFORMATION INTEGRATION TO BOOST MARKET 127
8.5 VIRTUAL ASSISTANTS, SELF-SERVICE DATA, AND DIGITAL ASSET DISCOVERY 128
8.5.1 STREAMLINING OF TEAMWORK AND KNOWLEDGE EXCHANGE TO ACCELERATE MARKET GROWTH 128
8.6 PRODUCT & CONFIGURATION MANAGEMENT 129
8.6.1 NEED TO ENSURE ACCURACY AND REDUCES TIME-TO-MARKET ENHANCING CUSTOMER SATISFACTION TO FUEL MARKET GROWTH 129
8.7 INFRASTRUCTURE & ASSET MANAGEMENT 130
8.7.1 INFRASTRUCTURE AND ASSET MANAGEMENT TO REDUCE DOWNTIME AND EXTEND ASSET LIFECYCLES THROUGH INFORMED DECISION-MAKING PROCESSES 130
8.8 PROCESS OPTIMIZATION & RESOURCE MANAGEMENT 131
8.8.1 NEED FOR REAL-TIME RESOURCE UTILIZATION MONITORING ACROSS DIFFERENT PROJECTS OR DEPARTMENTS TO PROPEL MARKET 131
8.9 RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING 132
8.9.1 RISK MANAGEMENT, COMPLIANCE, AND REGULATORY REPORTING TO HELP MAP DATA FLOWS, RELATIONSHIPS, AND CONTROLS TO IDENTIFY VULNERABILITIES AND ENSURE COMPLIANCE 132
8.10 MARKET & CUSTOMER INTELLIGENCE AND SALES OPTIMIZATION 133
8.10.1 NEED TO IDENTIFY TRENDS INFORMING TARGETED MARKETING STRATEGIES TO DRIVE MARKET 133
8.11 OTHER APPLICATIONS 134
9 KNOWLEDGE GRAPH MARKET, BY VERTICAL 135
9.1 INTRODUCTION 136
9.1.1 VERTICALS: KNOWLEDGE GRAPH MARKET DRIVERS 136
9.2 BFSI 138
9.2.1 INCREASING NEED TO MANAGE COMPLEX DATA TO SUPPORT MARKET GROWTH 138
9.2.2 CASE STUDY 139
9.2.2.1 Аnti-money laundering (AML) 139
9.2.2.1.1 Major US Financial Institutions enhanced anti-money laundering capabilities with TigerGraph 139
9.2.2.2 Fraud detection & risk management 140
9.2.2.2.1 BNP Paribas Personal Finance achieved 20% fraud reduction with Neo4j Graph Database 140
9.2.2.3 Identity & access management 140
9.2.2.3.1 Intuit safeguarded data of 100 million customers with Neo4j 140
9.2.2.4 Risk management 140
9.2.2.4.1 Global bank enhanced trade surveillance for risk management in BFSI 140
9.2.2.5 Data integration & governance 141
9.2.2.5.1 Optimizing data integration and governance for real-time risk management and compliance 141
9.2.2.6 Operational resilience for bank IT systems 141
9.2.2.6.1 Basel Institute on Governance enhanced asset recovery and financial intelligence with knowledge graphs for global financial institutions with Onto text 141 
9.2.2.7 Regulatory compliance 141
9.2.2.7.1 Multinational auditing company enhanced regulatory compliance and operational efficiency with knowledge graphs of Ontotext 141
9.2.2.8 Customer 360° view 142
9.2.2.8.1 Intuit enhanced security and data protection using Neo4j knowledge graph for customer data 142
9.2.2.9 Know Your Customer (KYC) processes 142
9.2.2.9.1 AI-powered knowledge graphs streamlined KYC compliance and adverse media analysis in financial services 142
9.2.2.10 Market analysis and trend detection 143
9.2.2.10.1 Leading investment bank enhanced investment insights through comprehensive company knowledge graph 143
9.2.2.11 Policy impact analysis 143
9.2.2.11.1 Delinian enhanced content production and analysis with semantic publishing platform 143
9.2.2.12 Customer support 143
9.2.2.12.1 Banks and insurance companies improved AI-powered knowledge graphs to revolutionize customer support in BFSI 143
9.2.2.13 Self-service data & digital asset discovery and data integration & governance 144
9.2.2.13.1 HSBC revolutionized data governance with knowledge graphs in BFSI 144
9.3 RETAIL & ECOMMERCE 144
9.3.1 NEED TO OPTIMIZE INVENTORY MANAGEMENT FACILITATED BY KNOWLEDGE GRAPHS TO DRIVE MARKET 144
9.3.2 CASE STUDY 145
9.3.2.1 Fraud detection in eCommerce 145
9.3.2.1.1 PayPal enhanced fraud detection with knowledge graphs 145
9.3.2.2 Dynamic pricing optimization 145
9.3.2.2.1 Belgian company revolutionized new product development with food pairing knowledge graph 145
9.3.2.3 Personalized recommendations 146
9.3.2.3.1 Xandr created industry-leading identity graph for personalized advertising with TigerGraph 146
9.3.2.4 Market basket analysis 146
9.3.2.4.1 eCommerce giants boosted retail sales with knowledge graph-powered market basket analysis 146
9.3.2.5 Customer experience enhancement 146
9.3.2.5.1 Retailers improved store operations and increased customer satisfaction using TigerGraph 146
9.3.2.5.2 Edamam enhanced food knowledge and user experience with knowledge graphs 147
9.3.2.6 Social media influence on buying behavior 147
9.3.2.6.1 Leveraging knowledge graphs to track social media influence on buying behavior at Coca-Cola 147
9.3.2.7 Churn prediction & prevention 147
9.3.2.7.1 Reduction of customer churn with knowledge graphs 147
9.3.2.8 Product configuration & recommendation 147
9.3.2.8.1 Leading automotive manufacturer personalized customer experience with knowledge graphs for product configuration 147
9.3.2.9 Customer segmentation & targeting 148
9.3.2.9.1 Xbox enhanced user experience with TigerGraph for better customer insights and loyalty 148
9.3.2.10 Customer 360° view 148
9.3.2.10.1 Technology giant enhanced customer engagement with TigerGraph for personalized experiences 148
9.3.2.11 Review & reputation management 149
9.3.2.11.1 Neo4j managed brand reputation with knowledge graphs at TripAdvisor 149
9.3.2.12 Customer support 149
9.3.2.12.1 Retailer enhanced operations and customer satisfaction with TigerGraph for root cause analysis 149
9.4 HEALTHCARE, LIFE SCIENCES, AND PHARMACEUTICALS 149
9.4.1 NEED TO REVOLUTIONIZE HEALTHCARE PRACTICES TO PROPEL ADOPTION OF KNOWLEDGE GRAPHS 149
9.4.2 CASE STUDY 150
9.4.2.1 Drug discovery & development 150
9.4.2.1.1 Early Drug R&D center accelerated cancer research with Ontotext’s target discovery 150
9.4.2.1.2 Ontotext's Target Discovery accelerated Alzheimer’s breakthroughs with knowledge graphs 151
9.4.2.2 Clinical trial management 151
9.4.2.2.1 NuMedii streamlined clinical trial management with AI-powered knowledge graphs with Ontotext 151
9.4.2.3 Medical claim processing 152
9.4.2.3.1 UnitedHealth Group revolutionized medical claim processing with TigerGraph 152
9.4.2.4 Clinical intelligence 152
9.4.2.4.1 Leading US Children’s Hospital gained deeper insights into impact of its faculty research 152
9.4.2.5 Healthcare provider network analysis 152
9.4.2.5.1 Amgen improved quality of healthcare by identifying influencers and referral networks using TigerGraph 152
9.4.2.6 Customer support 153
9.4.2.6.1 Exact Sciences Corporation revolutionized customer support in healthcare with a knowledge graph-powered 360° View 153
9.4.2.7 Patient journey & care pathway analysis 153
9.4.2.7.1 Care-for-Rare Foundation at Dr. von Hauner Children’s Hospital transformed pediatric care pathways with Neo4j’s clinical knowledge graph 153 
9.4.2.8 Self-service data & digital asset discovery 153
9.4.2.8.1 Boehringer Ingelheim accelerating pharmaceutical innovation with Stardog Knowledge Graph 153
9.5 TELECOM & TECHNOLOGY 154
9.5.1 NEED TO OPTIMIZE INTRICATE NETWORK INFRASTRUCTURE AND CUSTOMIZED SERVICE OFFERINGS TO FUEL MARKET GROWTH 154
9.5.2 CASE STUDY 155
9.5.2.1 Network optimization & management 155
9.5.2.1.1 Cyber resilience leader scaled next-generation cybersecurity with TigerGraph to combat evolving threats 155
9.5.2.2 Network security analysis 155
9.5.2.2.1 Multinational cybersecurity and defense company accelerated risk identification in cybersecurity with knowledge graphs with Ontotext 155
9.5.2.3 Identity & access management 155
9.5.2.3.1 Technology giant improved customer experiences with TigerGraph 155
9.5.2.4 IT asset management 156
9.5.2.4.1 Orange used Thing’in to build digital twin platform 156
9.5.2.5 IoT device management & connectivity 156
9.5.2.5.1 AWS enhanced IoT device management with Amazon Neptune's scalable graph database solutions 156
9.5.2.6 Metadata enrichment 156
9.5.2.6.1 Cisco utilized Neo4j to enhance and assign metadata to its vast document collection 156
9.5.2.7 Data integration & governance 157
9.5.2.7.1 Dun & Bradstreet enhanced compliance with Neo4j's graph technology 157
9.5.2.8 Self-service data & digital asset discovery 157
9.5.2.8.1 Telecom provider optimized telecom operations with Neo4j's self-service data and digital asset discovery 157
9.5.2.9 Service incident management 157
9.5.2.9.1 BT Group revolutionizing telecom inventory management with Neo4j knowledge graph 157
9.6 GOVERNMENT 157
9.6.1 SPEEDY DATA INTEGRATION AND INTEROPERABILITY TO BOOST MARKET GROWTH 157
9.6.2 CASE STUDY 158
9.6.2.1 Government service optimization 158
9.6.2.1.1 LODAC Museum project, initiated by Japan's National Institute of Informatics (NII), enhanced academic access to cultural heritage data through Linked Open Data 158
9.6.2.2 Legislative & regulatory analysis 159
9.6.2.2.1 Inter-American Development Bank (IDB) enhanced knowledge discovery with knowledge graphs at the IDB 159 
9.6.2.3 Crisis management & disaster response planning 159
9.6.2.3.1 Knowledge graphs enhanced crisis response for real-time decision-making 159
9.6.2.4 Environmental impact analysis and ESG 159
9.6.2.4.1 Vienna University of Technology transformed architectural design with ECOLOPES knowledge graph 159
9.6.2.5 Social network analysis for security & law enforcement 160
9.6.2.5.1 Social Network Analysis strengthened security via knowledge graphs 160
9.6.2.6 Policy Impact Analysis 160
9.6.2.6.1 Governments leveraged knowledge graphs for effective policy impact analysis 160
9.6.2.7 Knowledge management 160
9.6.2.7.1 Ellas leveraged Graphdb's knowledge graphs to bridge gender gaps in STEM leadership 160
9.6.2.8 Data integration & governance 160
9.6.2.8.1 Government agency took digital and print library services to next level partnering with metaphacts and Ontotext 160
9.7 MANUFACTURING & AUTOMOTIVE 161
9.7.1 EASY PREDICTIVE MAINTENANCE AND DECREASE IN DOWNTIME TO SUPPORT MARKET GROWTH 161
9.7.2 CASE STUDY 162
9.7.2.1 Equipment maintenance and predictive maintenance 162
9.7.2.1.1 Ford Motor Company enhanced production efficiency with TigerGraph for predictive maintenance 162
9.7.2.2 Product lifecycle management 162
9.7.2.2.1 Leading European manufacturer of electrical components enhanced product discoverability through semantic knowledge graphs 162
9.7.2.3 Manufacturing process optimization 163
9.7.2.3.1 Production streamlined efficiency with knowledge graphs 163
9.7.2.4 Enhance vehicle safety & reliability 163
9.7.2.4.1 Knowledge graphs improved vehicle safety with predictive maintenance 163
9.7.2.5 Optimization of industrial processes 163
9.7.2.5.1 Leading manufacturer of Building Automation Systems (BAS) graphs improved vehicle safety with Ontotext’s GraphDB 163
9.7.2.6 Root cause analysis 164
9.7.2.6.1 Root Cause Analysis uncovered process failures with using knowledge graphs 164
9.7.2.7 Inventory management & demand forecasting 164
9.7.2.7.1 Knowledge graphs optimized inventory and demand forecasting with knowledge graphs 164
9.7.2.8 Service incident management 164
9.7.2.8.1 Knowledge graphs accelerated service incident resolution with knowledge graphs 164
9.7.2.9 Staff & resource allocation 164
9.7.2.9.1 Knowledge graphs optimized staff and resource allocation with knowledge graphs 164
9.7.2.10 Product configuration & recommendation 165
9.7.2.10.1 Leading Building Automation Systems (BAS) manufacturers used Brick schema to represent BAS components and their complex interactions 165
9.8 MEDIA & ENTERTAINMENT 165
9.8.1 NEED TO IMPROVE CONTENT MANAGEMENT PROCEDURES AND BETTER DATA-DRIVEN DECISIONS TO FOSTER MARKET GROWTH 165
9.8.2 CASE STUDY 166
9.8.2.1 Content recommendation & personalization 166
9.8.2.1.1 Leading television broadcaster streamlined data management and improved search efficiency with knowledge graphs 166
9.8.2.2 Audience segmentation & targeting 166
9.8.2.2.1 KT Corporation enhanced IPTV Content Discovery with semantic search for better audience targeting 166
9.8.2.3 Social media influence analysis 167
9.8.2.3.1 Myntelligence used TigerGraph’s advanced graph analytics to analyze relationships and interactions 167
9.8.2.4 Copyright & licensing management 167
9.8.2.4.1 British Museum and Europeana leveraged knowledge graphs for efficient content management and licensing in cultural heritage 167
9.8.2.5 Self-service data & digital asset discovery 167
9.8.2.5.1 BBC transformed content management with semantic publishing for enhanced user experience 167
9.8.2.6 Content recommendation systems 168
9.8.2.6.1 STM publisher leveraged knowledge platform for enhanced content recommendation 168
9.8.2.7 User engagement analysis 168
9.8.2.7.1 Bulgarian media company leveraged Ontotext's knowledge graphs for enhanced user engagement and ad targeting 168
9.8.2.8 Knowledge management 169
9.8.2.8.1 Rappler empowered transparent elections with first Philippine Politics Knowledge Graph 169
9.8.2.8.2 Perfect Memory and Ontotext developed custom data program platform based on knowledge graph solution to streamline data management 169
9.9 ENERGY, UTILITIES, AND INFRASTRUCTURE 169
9.9.1 DEVELOPMENT OF INNOVATIVE TECHNOLOGIES TO DRIVE DEMAND FOR KNOWLEDGE GRAPH SOLUTIONS 169
9.9.2 CASE STUDY 170
9.9.2.1 Grid management 170
9.9.2.1.1 Transmission Systems Operator (TSO) modernized asset management with knowledge graphs for enhanced grid reliability 170
9.9.2.2 Energy trading optimization 171
9.9.2.2.1 Global energy and commodities markets information provider gained enhanced operational efficiencies with semantic information extraction 171
9.9.2.3 Renewable energy integration & optimization 171
9.9.2.3.1 State Grid Corporation of China created speedy energy management system with assistance of TigerGraph 171
9.9.2.4 Public infrastructure management 171
9.9.2.4.1 Knowledge graphs enhanced infrastructure management for better decision-making 171
9.9.2.5 Customer engagement & billing 172
9.9.2.5.1 Knowledge graphs streamlined customer engagement and billing 172
9.9.2.6 Environmental impact analysis & ESG 172
9.9.2.6.1 Improved environmental impact analysis with knowledge graphs for ESG reporting 172
9.9.2.7 Service incident management 172
9.9.2.7.1 Enxchange transformed service incident management in energy with graph-based digital twins 172
9.9.2.8 Staff & resource allocation 172
9.9.2.8.1 Knowledge graphs optimized staff and resource allocation for efficient operations 172
9.9.2.9 Railway asset management 173
9.9.2.9.1 Railway asset management with graph databases enhanced connectivity and efficiency 173
9.10 TRAVEL & HOSPITALITY 173
9.10.1 NEED FOR KNOWLEDGE GRAPHS TO HELP DEVELOP INNOVATIVE TECHNOLOGIES TO DRIVE MARKET 173
9.10.2 CASE STUDY 174
9.10.2.1 Personalized travel recommendations 174
9.10.2.1.1 Travel personalization with knowledge graphs for tailored recommendations 174
9.10.2.2 Dynamic pricing optimization 174
9.10.2.2.1 Marriott International implemented knowledge graph technology for dynamic pricing and revenue optimization 174
9.10.2.3 Customer journey mapping 174
9.10.2.3.1 Knowledge graphs mapped customer journey for enhanced travel experiences 174
9.10.2.4 Booking & reservation optimization 175
9.10.2.4.1 WestJet Airlines transformed flight scheduling into a seamless, customer-friendly experience with Neo4j 175
9.10.2.5 Customer experience enhancement 175
9.10.2.5.1 Airbnb transformed customer experience with unified data and actionable insights with Neo4j graph database 175
9.10.2.6 Product configuration and recommendation 175
9.10.2.6.1 Knowledge graphs streamlined product configuration and recommendations 175
9.11 TRANSPORTATION & LOGISTICS 176
9.11.1 NEED FOR DEVELOPMENT OF INNOVATIVE TECHNOLOGIES TO BOLSTER MARKET GROWTH 176
9.11.2 CASE STUDY 177
9.11.2.1 Route optimization & fleet management 177
9.11.2.1.1 Transport for London (TfL) optimized route management and incident response with digital twin 177
9.11.2.2 Supply chain visibility 177
9.11.2.2.1 Knowledge graphs enhanced supply chain visibility with real-time insights 177
9.11.2.3 Equipment maintenance & predictive maintenance 177
9.11.2.3.1 Knowledge graphs optimized equipment maintenance with predictive insights via knowledge graphs 177
9.11.2.4 Supply chain management 177
9.11.2.4.1 Knowledge graphs streamlined supply chain management for better coordination 177
9.11.2.5 Vendor & supplier analysis 178
9.11.2.5.1 Vendor and supplier analysis with knowledge graphs for smarter sourcing 178
9.11.2.6 Operational efficiency & decision making 178
9.11.2.6.1 Careem improved operational efficiency through fraud detection 178
9.12 OTHER VERTICALS 178
10 KNOWLEDGE GRAPH MARKET, BY REGION 180
10.1 INTRODUCTION 181
10.2 NORTH AMERICA 182
10.2.1 NORTH AMERICA: MACROECONOMIC OUTLOOK 182
10.2.2 US 188
10.2.2.1 Increasing need for structured data analytics and interoperability to drive market 188
10.2.3 CANADA 193
10.2.3.1 Increasing complexity of data and demand for efficient data to propel market 193
10.3 EUROPE 193
10.3.1 EUROPE: MACROECONOMIC OUTLOOK 194
10.3.2 UK 199
10.3.2.1 Increasing complexity of data and demand for advanced data integration solutions to fuel market growth 199
10.3.3 GERMANY 204
10.3.3.1 Focus on Industry 4.0 to drive demand for knowledge graph 204
10.3.4 FRANCE 204
10.3.4.1 Focus on technological innovation, robust digital infrastructure, and supportive regulatory environment to foster market growth 204
10.3.5 ITALY 204
10.3.5.1 Increasing adoption of semantic technologies and government commitment to fostering innovation to drive market 204
10.3.6 SPAIN 209
10.3.6.1 Strategic initiatives in AI development sector and implementation of Spain's 2024 Artificial Intelligence Strategy to accelerate market 209
10.3.7 NORDIC COUNTRIES 210
10.3.7.1 High digital literacy, advanced AI readiness, and robust public-private partnerships to bolster market growth 210
10.3.8 REST OF EUROPE 210
10.4 ASIA PACIFIC 211
10.4.1 ASIA PACIFIC: MACROECONOMIC OUTLOOK 211
10.4.2 CHINA 217
10.4.2.1 Rapid technological advancements, government initiatives, and strategic focus on integrating AI to boost market 217
10.4.3 JAPAN 222
10.4.3.1 Advancements in robotics and a strong focus on AI technologies under the government’s “Society 5.0” initiative to drive market 222
10.4.4 INDIA 222
10.4.4.1 Focus on promoting advanced technology usage through government initiatives to foster market growth 222
10.4.5 SOUTH KOREA 227
10.4.5.1 Strong focus on developing and enhancing public-private partnerships to drive market 227
10.4.6 AUSTRALIA & NEW ZEALAND 227
10.4.6.1 Strategic collaborations for development in new age technologies to drive market 227
10.4.7 REST OF ASIA PACIFIC 227
10.5 MIDDLE EAST & AFRICA 228
10.5.1 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK 228
10.5.2 GCC COUNTRIES 233
10.5.2.1 Increasing investment in AI technologies for development to fuel market growth 233
10.5.2.2 UAE 238
10.5.2.2.1 Rising government support for AI and digital transformation initiatives to foster market growth 238
10.5.2.3 KSA 239
10.5.2.3.1 Government initiatives and investments in digital infrastructure to propel market 239
10.5.2.4 Rest of GCC countries 243
10.5.3 SOUTH AFRICA 243
10.5.3.1 Growing focus on digital transformation and innovation to accelerate market growth 243
10.5.4 REST OF MIDDLE EAST & AFRICA 244 
10.6 LATIN AMERICA 244
10.6.1 LATIN AMERICA: MACROECONOMIC OUTLOOK 244
10.6.2 BRAZIL 250
10.6.2.1 Increasing demand for personalized customer interactions and advancements in AI technologies to propel market 250
10.6.3 MEXICO 254
10.6.3.1 Focus on advancing digital infrastructure to boost market growth 254
10.6.4 ARGENTINA 255
10.6.4.1 Focus on digital transformation initiatives to drive market 255
10.6.5 REST OF LATIN AMERICA 255
11 COMPETITIVE LANDSCAPE 256
11.1 INTRODUCTION 256
11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 256
11.3 REVENUE ANALYSIS 257
11.4 MARKET SHARE ANALYSIS 258
11.5 MARKET RANKING ANALYSIS 259
11.6 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 260
11.6.1 STARS 260
11.6.2 EMERGING LEADERS 260
11.6.3 PERVASIVE PLAYERS 260
11.6.4 PARTICIPANTS 261
11.6.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024 262
11.6.5.1 Company footprint 262
11.6.5.2 Vertical footprint 263
11.6.5.3 Offering footprint 264
11.6.5.4 Regional footprint 265
11.7 COMPANY EVALUATION MATRIX: START-UPS/SMES, 2024 265
11.7.1 PROGRESSIVE COMPANIES 265
11.7.2 RESPONSIVE COMPANIES 265
11.7.3 DYNAMIC COMPANIES 266
11.7.4 STARTING BLOCKS 266
11.7.5 COMPETITIVE BENCHMARKING: START-UPS/SMES, 2024 267
11.7.5.1 Key start-ups/SMEs 267
11.7.5.2 Competitive benchmarking of key start-ups/SMEs 268
11.8 COMPETITIVE SCENARIOS AND TRENDS 269
11.8.1 PRODUCT LAUNCHES & ENHANCEMENTS 269
11.8.2 DEALS 272
11.9 BRAND/PRODUCT COMPARISON 274
11.10 COMPANY VALUATION AND FINANCIAL METRICS OF KEY KNOWLEDGE GRAPH SOLUTION PROVIDERS 275 
12 COMPANY PROFILES 276
12.1 KEY PLAYERS 276
12.1.1 NEO4J 276
12.1.1.1 Business overview 276
12.1.1.2 Products/Solutions/Services offered 277
12.1.1.3 Recent developments 278
12.1.1.3.1 Product enhancements 278
12.1.1.3.2 Deals 278
12.1.1.4 MnM view 279
12.1.1.4.1 Right to win 279
12.1.1.4.2 Strategic choices 279
12.1.1.4.3 Weaknesses and competitive threats 279
12.1.2 AMAZON WEB SERVICES, INC 280
12.1.2.1 Business overview 280
12.1.2.2 Products/Solutions/Services offered 281
12.1.2.3 Recent developments 281
12.1.2.3.1 Product enhancements 281
12.1.2.4 MnM view 282
12.1.2.4.1 Right to win 282
12.1.2.4.2 Strategic choices 282
12.1.2.4.3 Weaknesses and competitive threats 282
12.1.3 TIGERGRAPH 283
12.1.3.1 Business overview 283
12.1.3.2 Products/Solutions/Services offered 283
12.1.3.3 Recent developments 284
12.1.3.3.1 Product enhancements 284
12.1.3.3.2 Deals 284
12.1.3.4 MnM view 285
12.1.3.4.1 Right to win 285
12.1.3.4.2 Strategic choices 285
12.1.3.4.3 Weaknesses and competitive threats 285
12.1.4 GRAPHWISE 286
12.1.4.1 Business overview 286
12.1.4.2 Products/Solutions/Services offered 286
12.1.4.3 Recent developments 287
12.1.4.3.1 Product enhancements 287
12.1.4.4 MnM view 287
12.1.4.4.1 Right to win 287
12.1.4.4.2 Strategic choices 287
12.1.4.4.3 Weaknesses and competitive threats 287 
12.1.5 RELATIONALAI 288
12.1.5.1 Business overview 288
12.1.5.2 Products/Solutions/Services offered 288
12.1.5.3 Recent developments 289
12.1.5.3.1 Product launches 289
12.1.5.4 MnM view 289
12.1.5.4.1 Right to win 289
12.1.5.4.2 Strategic choices 289
12.1.5.4.3 Weaknesses and competitive threats 289
12.1.6 IBM 290
12.1.6.1 Business overview 290
12.1.6.2 Products/Solutions/Services offered 291
12.1.6.3 Recent developments 292
12.1.6.3.1 Product enhancements 292
12.1.6.3.2 Deals 292
12.1.7 MICROSOFT 293
12.1.7.1 Business overview 293
12.1.7.2 Products/Solutions/Services offered 295
12.1.7.3 Recent developments 296
12.1.7.3.1 Product enhancements 296
12.1.7.3.2 Deals 296
12.1.8 SAP 297
12.1.8.1 Business overview 297
12.1.8.2 Products/Solutions/Services offered 298
12.1.8.3 Recent developments 299
12.1.8.3.1 Product enhancements 299
12.1.9 ORACLE 300
12.1.9.1 Business overview 300
12.1.9.2 Products/Solutions/Services offered 301
12.1.9.3 Recent developments 302
12.1.9.3.1 Product enhancements 302
12.1.10 STARDOG 303
12.1.10.1 Business overview 303
12.1.10.2 Products/Solutions/Services offered 304
12.1.10.3 Recent developments 304
12.1.10.3.1 Product enhancements 304
12.1.10.3.2 Deals 305 
12.1.11 ONTOTEXT 306
12.1.11.1 Business overview 306
12.1.11.2 Products/Solutions/Services offered 306
12.1.11.3 Recent developments 307
12.1.11.3.1 Product enhancements 307
12.1.11.3.2 Deals 308
12.1.12 FRANZ INC. 309
12.1.12.1 Business overview 309
12.1.12.2 Products/Solutions/Services offered 309
12.1.12.3 Recent developments 310
12.1.12.3.1 Product enhancements 310
12.1.13 ALTAIR 311
12.1.13.1 Business overview 311
12.1.13.2 Products/Solutions/Services offered 312
12.1.13.3 Recent developments 313
12.1.13.3.1 Product enhancements 313
12.1.13.3.2 Deals 313
12.1.14 PROGRESS SOFTWARE CORPORATION 314
12.1.15 ESRI 315
12.1.16 SEMANTIC WEB COMPANY 316
12.1.17 OPENLINK SOFTWARE 317
12.2 SMES/START-UPS 318
12.2.1 DATAVID 318
12.2.2 GRAPHBASE 319
12.2.3 CONVERSIGHT 320
12.2.4 ECCENCA 321
12.2.5 ARANGODB 322
12.2.6 FLUREE 323
12.2.7 DIFFBOT 324
12.2.8 BITNINE 325
12.2.9 MEMGRAPH 326
12.2.10 GRAPHAWARE 327
12.2.11 ONLIM 328
12.2.12 SMABBLER 329
12.2.13 WISECUBE 330
12.2.14 METAPHACTS 331