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市场调查报告书
商品编码
1766086

知识图谱市场：2032 年全球预测 - 按组件、部署方法、组织规模、应用、最终用户和地区进行分析

Knowledge Graph Market Forecasts to 2032 - Global Analysis By Component (Solutions and Services), Deployment Mode, Organization Size, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球知识图谱市场预计在 2025 年达到 15.4 亿美元，到 2032 年将达到 42.4 亿美元，预测期内的复合年增长率为 15.5%。

知识图谱是一种有序的图，由节点（实体）和边（关係）组成，用于表示现实世界中的实体及其关係。它透过整合来自多个来源的资料来创建上下文和含义，使机器能够像人类一样分析资料。知识图谱为人工智慧、搜寻引擎和资料分析中的资讯搜寻、语意搜寻和决策提供支援。知识图谱有助于推理、查询和发现复杂资料集中的隐藏模式。作为智慧应用和系统的企业级知识模型，Google知识图谱就是其中的杰出代表。

对人工智慧和语义搜寻功能的需求不断增长

企业越来越多地使用人工智慧从海量非结构化资料中提取有价值的洞察。透过理解意图和上下文，语义搜寻可以提升使用者体验并提高搜寻结果的准确性。知识图谱透过让机器人处理资料关係，为建议引擎和聊天机器人等智慧应用提供支援。企业正在将知识图谱与人工智慧解决方案结合，以实现自动化和更智慧的决策。这一趋势正在加速电子商务、医疗保健和金融等行业的市场扩张。

复杂性高，缺乏熟练的专业人员

本体设计和资料建模的复杂性常常令现有IT团队不堪负荷。此外，与旧有系统的整合增加了技术负担，并减缓了采用速度。缺乏RDF、SPARQL和OWL等知识图谱技术的合格专家是一大障碍。人才短缺限制了企业级解决方案的采用和扩充性。因此，许多企业不愿在知识图谱领域投入大量资金。

工业 4.0 和日益增长的数位化应用

组织使用知识图谱来连接不同的资料来源，并推动更智慧的自动化和决策。随着工厂和企业的数位化，情境智慧和即时数据整合变得越来越必要。为了契合工业 4.0 的目标，知识图谱能够从复杂的非结构化资料中提供有组织的洞察。知识图谱支援预测分析，以优化流程并改进机器学习模型。市场对关联数据的依赖日益加深，推动了对可扩展知识图谱解决方案的需求。

资料隐私问题和监管合规性

由于需要严格遵守CCPA和GDPR等隐私法规，跨资料孤岛的资料整合对组织而言极具挑战性。这些限制资料共用和重复使用的法规使得建立端到端知识图谱更具挑战性。由于担心资料外洩和潜在的法律后果，企业不愿采用基于图谱的解决方案。此外，匿名化技术通常会降低资料质量，进而影响知识图谱的效能。因此，企业仍保持谨慎，导致市场采用速度缓慢。

COVID-19的影响

新冠疫情加速了数位转型，并增加了对高阶资料管理工具的需求，对知识图谱市场产生了重大影响。随着企业转向远端运营，对高效数据整合、情境化和即时洞察的需求激增。医疗保健、电子商务和金融等行业已利用知识图谱来简化决策流程并提升客户体验。最初对 IT 预算造成衝击的因素，从长远来看产生了积极影响，促使企业采用语义技术和人工智慧主导的资料框架。

预计解决方案部门将成为预测期内最大的部门

凭藉其先进的数据整合、语义搜寻和关係映射功能，解决方案领域预计将在预测期内占据最大的市场占有率。这些解决方案使企业能够从复杂的资料集中获得更深入的洞察，并做出明智的决策。越来越多的企业正在采用这些工具来改善客户体验、个人化服务并简化业务。对人工智慧解决方案的需求正在加速其在医疗保健、金融和电子商务等行业的部署。

预计医疗保健和生命科学领域在预测期内将实现最高的复合年增长率。

在预测期内，医疗保健和生命科学领域预计将实现最高成长率，这得益于其能够实现高级资料整合和对大量临床资料集进行语义搜寻的能力。知识图谱透过丰富的上下文资料建模，增强了药物发现、患者照护和临床试验优化。知识图谱透过连结不同的医疗记录、基因组数据和研究论文，支持即时洞察和个人化医疗。它们还透过提供复杂生物医学关係的统一视图来改善决策。对智慧资料结构化日益增长的需求，正推动其在该领域的强劲应用。

占比最大的地区：

在预测期内，由于电子商务、医疗保健和金融等领域的数位转型不断推进，亚太地区预计将占据最大的市场占有率。中国、日本和印度等国家正大力投资人工智慧和语意技术，推动这些技术的普及。精通技术的人口和政府主导的人工智慧倡议正在进一步增强市场。此外，人们对数据主导决策和自然语言处理的兴趣日益浓厚，这促使企业采用知识图谱来增强洞察力和自动化程度，使该地区成为创新和市场扩张的温床。

复合年增长率最高的地区：

在预测期内，北美预计将呈现最高的复合年增长率，这主要得益于Google、微软和 IBM 等科技巨头的推动。对企业人工智慧、高阶分析和个人化客户体验的旺盛需求正在推动市场成长。该地区受惠于成熟的云端基础设施和对语意网路技术的大量研发投入。知识图谱正越来越多地被用于改善资料整合、增强搜寻能力，并推动医疗保健、金融服务和保险（BFSI）和媒体等领域的商业智慧。监管合规性和资料隐私方面的考量也在影响该地区解决方案的开发和部署。

免费客製化服务

本报告的所有订阅者均可享有以下免费自订选项之一：

  • 公司简介
    • 对其他公司（最多 3 家）进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家市场进行估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第 2 章 简介

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 主要研究资料
    • 二手研究资讯来源
    • 先决条件

第三章市场走势分析

  • 介绍
  • 驱动程式
  • 限制因素
  • 市场机会
  • 威胁
  • 最终用户分析
  • 新兴市场
  • COVID-19的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 企业之间的竞争

5. 全球知识图谱市场（按组成部分）

  • 解决方案
  • 服务

第六章全球知识图谱市场（依部署方法）

  • 本地
  • 云端基础

第七章。按组织规模分類的全球知识图谱市场

  • 小型企业
  • 大公司

第八章全球知识图谱市场（按应用）

  • 资料管理
  • 资讯搜寻
  • 建议引擎
  • 风险与合规管理
  • 数据集成
  • 语意搜寻
  • 其他用途

第九章全球知识图谱市场（依最终用户划分）

  • 医学与生命科学
  • 零售与电子商务
  • 媒体与娱乐
  • 政府/公共部门
  • 资讯科技/通讯
  • 製造业
  • 教育
  • 其他最终用户

第十章全球知识图谱市场（按地区）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 合约、商业伙伴关係和合资企业
  • 企业合併与收购（M&A）
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章 公司概况

  • Neo4j
  • Franz Inc
  • Graphwise
  • IBM
  • Microsoft
  • Amazon Web Services（AWS）
  • Google（Alphabet）
  • Oracle
  • SAP
  • TigerGraph
  • Stardog
  • Ontotext
  • Cambridge Semantics
  • ArangoDB
  • Bitnine
  • DataStax
  • Diffbot Technologies
  • Datavid
Product Code: SMRC29941

According to Stratistics MRC, the Global Knowledge Graph Market is accounted for $1.54 billion in 2025 and is expected to reach $4.24 billion by 2032 growing at a CAGR of 15.5% during the forecast period. A knowledge graph is an ordered graph with nodes (entities) and edges (relationships) that represents real-world entities and their relationships. It allows machines to analyse data similarly to humans by combining data from several sources to create context and meaning. Knowledge graphs enhance information retrieval, semantic search, and decision-making in artificial intelligence, search engines, and data analytics. They facilitate inference, querying, and the discovery of obscure patterns in intricate datasets. Enterprise-level knowledge models for intelligent applications and systems, as well as Google Knowledge Graph, are notable examples.

Market Dynamics:

Driver:

Growing demand for AI and semantic search capabilities

AI is being used by businesses more and more to extract valuable insights from massive amounts of unstructured data. By comprehending purpose and context, semantic search improves user experience and increases the precision of search results. Knowledge graphs power intelligent applications like recommendation engines and chatbots by allowing robots to process data relationships. Businesses are combining knowledge graphs with AI solutions as they aim for automation and more intelligent decision-making. Market expansion is being accelerated by this trend in industries like e-commerce, healthcare, and finance.

Restraint:

High complexity and lack of skilled professionals

The complexity of ontology design and data modelling frequently overwhelms current IT teams. Furthermore, integrating with legacy systems delays adoption by increasing the technical burden. The lack of qualified experts with knowledge graph technologies like RDF, SPARQL, and OWL is a significant obstacle. The adoption and scalability of enterprise-level solutions are constrained by this talent shortage. Many companies are therefore hesitant to make a full investment in knowledge graph initiatives.

Opportunity:

Rising adoption of industry 4.0 and digital

Knowledge graphs are being used by organisations to link different data sources, facilitating more intelligent automation and decision-making. Contextual intelligence and real-time data integration are becoming more and more necessary as factories and businesses digitise. In line with the objectives of Industry 4.0, knowledge graphs offer organised insights from complicated, unstructured data. They support predictive analytics for process optimisation and improve machine learning models. The need for scalable knowledge graph solutions is being driven by the market's increasing reliance on linked data.

Threat:

Data privacy concerns and regulatory compliance

Integrating data across silos is difficult for organisations because of the stringent adherence to privacy regulations like the CCPA and GDPR. Building thorough knowledge graphs is made more difficult by these rules, which limit the sharing and reuse of data. Businesses are hesitant to engage in graph-based solutions due to concerns about data breaches and potential legal repercussions. Furthermore, anonymisation methods frequently result in lower-quality data, which affects knowledge graph performance. Businesses continue to be cautious as a result, which slows market adoption.

Covid-19 Impact

The COVID-19 pandemic significantly influenced the Knowledge Graph market by accelerating digital transformation and increasing demand for advanced data management tools. As organizations shifted to remote operations, the need for efficient data integration, contextualization, and real-time insights surged. Industries such as healthcare, e-commerce, and finance leveraged knowledge graphs to streamline decision-making and enhance customer experiences. Despite initial disruptions in IT budgets, the long-term impact was positive, driving adoption of semantic technologies and AI-driven data frameworks across enterprises.

The solutions segment is expected to be the largest during the forecast period

The solutions segment is expected to account for the largest market share during the forecast period, due to advanced data integration, semantic search, and relationship mapping capabilities. These solutions enable organizations to derive deeper insights from complex datasets, driving intelligent decision-making. Businesses increasingly adopt these tools to enhance customer experience, personalize services, and streamline operations. The demand for AI-powered solutions accelerates their deployment across industries like healthcare, finance, and e-commerce.

The healthcare and life sciences segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare and life sciences segment is predicted to witness the highest growth rate by enabling advanced data integration and semantic search across vast clinical datasets. It enhances drug discovery, patient care, and clinical trial optimization through context-rich data modeling. Knowledge graphs support real-time insights and personalized medicine by connecting disparate health records, genomic data, and research articles. They also improve decision-making by offering a unified view of complex biomedical relationships. This growing need for intelligent data structuring drives strong adoption in the sector.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share due to the increasing digital transformation across sectors like e-commerce, healthcare, and finance. Countries such as China, Japan, and India are heavily investing in AI and semantic technologies, driving adoption. The presence of tech-savvy populations and government-led AI initiatives further bolster the market. Additionally, growing interest in data-driven decision-making and natural language processing is encouraging enterprises to deploy knowledge graphs for enhanced insights and automation, making the region a hotbed for innovation and market expansion.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR by tech giants such as Google, Microsoft, and IBM. High demand for enterprise AI, advanced analytics, and personalized customer experiences is propelling market growth. The region benefits from well-established cloud infrastructure and significant R&D investment in semantic web technologies. Knowledge graphs are increasingly used in sectors like healthcare, BFSI, and media for improving data integration, enhancing search capabilities, and driving business intelligence. Regulatory compliance and data privacy considerations also shape the development and deployment of solutions in the region.

Key players in the market

Some of the key players profiled in the Knowledge Graph Market include Neo4j, Franz Inc, Graphwise, IBM, Microsoft, Amazon Web Services (AWS), Google (Alphabet), Oracle, SAP, TigerGraph, Stardog, Ontotext, Cambridge Semantics, ArangoDB, Bitnine, DataStax, Diffbot Technologies and Datavid.

Key Developments:

In March 2024, Neo4j partnered with Microsoft to offer unified GenAI and data solutions, enhancing the development of explainable AI systems using knowledge graphs. This collaboration integrates Neo4j's graph technology with Microsoft Azure's AI capabilities, enabling enterprises to build accurate, transparent, and context-aware AI applications that minimize hallucinations and ensure data-driven decision-making across various domains.

In January 2024, Franz Inc. launched AllegroGraph Cloud, a hosted Neuro-Symbolic AI and Knowledge Graph platform delivering enterprise-grade capabilities through a fully managed service, enabling organizations to build intelligent applications with scalable, secure, and flexible deployment.

Components Covered:

  • Solutions
  • Services

Deployment Modes Covered:

  • On-Premises
  • Cloud-Based

Organization Sizes Covered:

  • Small and Medium-sized Enterprises (SMEs)
  • Large Enterprises

Applications Covered:

  • Data Management
  • Information Retrieval
  • Recommendation Engines
  • Risk and Compliance Management
  • Data Integration
  • Semantic Search
  • Other Applications

End Users Covered:

  • Healthcare and Life Sciences
  • Retail and E-commerce
  • Media and Entertainment
  • Government and Public Sector
  • IT and Telecommunications
  • Manufacturing
  • Education
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Knowledge Graph Market, By Component

  • 5.1 Introduction
  • 5.2 Solutions
  • 5.3 Services

6 Global Knowledge Graph Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premises
  • 6.3 Cloud-Based

7 Global Knowledge Graph Market, By Organization Size

  • 7.1 Introduction
  • 7.2 Small and Medium-sized Enterprises (SMEs)
  • 7.3 Large Enterprises

8 Global Knowledge Graph Market, By Application

  • 8.1 Introduction
  • 8.2 Data Management
  • 8.3 Information Retrieval
  • 8.4 Recommendation Engines
  • 8.5 Risk and Compliance Management
  • 8.6 Data Integration
  • 8.7 Semantic Search
  • 8.8 Other Applications

9 Global Knowledge Graph Market, By End User

  • 9.1 Introduction
  • 9.2 Healthcare and Life Sciences
  • 9.3 Retail and E-commerce
  • 9.4 Media and Entertainment
  • 9.5 Government and Public Sector
  • 9.6 IT and Telecommunications
  • 9.7 Manufacturing
  • 9.8 Education
  • 9.9 Other End Users

10 Global Knowledge Graph Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Neo4j
  • 12.2 Franz Inc
  • 12.3 Graphwise
  • 12.4 IBM
  • 12.5 Microsoft
  • 12.6 Amazon Web Services (AWS)
  • 12.7 Google (Alphabet)
  • 12.8 Oracle
  • 12.9 SAP
  • 12.10 TigerGraph
  • 12.11 Stardog
  • 12.12 Ontotext
  • 12.12 Cambridge Semantics
  • 12.14 ArangoDB
  • 12.15 Bitnine
  • 12.16 DataStax
  • 12.17 Diffbot Technologies
  • 12.18 Datavid

List of Tables

  • Table 1 Global Knowledge Graph Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Knowledge Graph Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Knowledge Graph Market Outlook, By Solutions (2024-2032) ($MN)
  • Table 4 Global Knowledge Graph Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global Knowledge Graph Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 6 Global Knowledge Graph Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 7 Global Knowledge Graph Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 8 Global Knowledge Graph Market Outlook, By Organization Size (2024-2032) ($MN)
  • Table 9 Global Knowledge Graph Market Outlook, By Small and Medium-sized Enterprises (SMEs) (2024-2032) ($MN)
  • Table 10 Global Knowledge Graph Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 11 Global Knowledge Graph Market Outlook, By Application (2024-2032) ($MN)
  • Table 12 Global Knowledge Graph Market Outlook, By Data Management (2024-2032) ($MN)
  • Table 13 Global Knowledge Graph Market Outlook, By Information Retrieval (2024-2032) ($MN)
  • Table 14 Global Knowledge Graph Market Outlook, By Recommendation Engines (2024-2032) ($MN)
  • Table 15 Global Knowledge Graph Market Outlook, By Risk and Compliance Management (2024-2032) ($MN)
  • Table 16 Global Knowledge Graph Market Outlook, By Data Integration (2024-2032) ($MN)
  • Table 17 Global Knowledge Graph Market Outlook, By Semantic Search (2024-2032) ($MN)
  • Table 18 Global Knowledge Graph Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 19 Global Knowledge Graph Market Outlook, By End User (2024-2032) ($MN)
  • Table 20 Global Knowledge Graph Market Outlook, By Healthcare and Life Sciences (2024-2032) ($MN)
  • Table 21 Global Knowledge Graph Market Outlook, By Retail and E-commerce (2024-2032) ($MN)
  • Table 22 Global Knowledge Graph Market Outlook, By Media and Entertainment (2024-2032) ($MN)
  • Table 23 Global Knowledge Graph Market Outlook, By Government and Public Sector (2024-2032) ($MN)
  • Table 24 Global Knowledge Graph Market Outlook, By IT and Telecommunications (2024-2032) ($MN)
  • Table 25 Global Knowledge Graph Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 26 Global Knowledge Graph Market Outlook, By Education (2024-2032) ($MN)
  • Table 27 Global Knowledge Graph Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.