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知識圖譜市場：2032 年全球預測 - 按組件、部署方法、組織規模、應用、最終用戶和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球知識圖譜市場預計在 2025 年達到 15.4 億美元，到 2032 年將達到 42.4 億美元，預測期內的複合年成長率為 15.5%。

知識圖譜是一種有序的圖，由節點（實體）和邊（關係）組成，用於表示現實世界中的實體及其關係。它透過整合來自多個來源的資料來創建上下文和含義，使機器能夠像人類一樣分析資料。知識圖譜為人工智慧、搜尋引擎和資料分析中的資訊搜尋、語意搜尋和決策提供支援。知識圖譜有助於推理、查詢和發現複雜資料集中的隱藏模式。作為智慧應用和系統的企業級知識模型，Google知識圖譜就是其中的傑出代表。

對人工智慧和語義搜尋功能的需求不斷成長

企業擴大使用人工智慧從海量非結構化資料中提取有價值的洞察。透過理解意圖和上下文，語義搜尋可以提升使用者體驗並提高搜尋結果的準確性。知識圖譜透過讓機器人處理資料關係，為建議引擎和聊天機器人等智慧應用提供支援。企業正在將知識圖譜與人工智慧解決方案結合，以實現自動化和更智慧的決策。這一趨勢正在加速電子商務、醫療保健和金融等行業的市場擴張。

複雜性高，缺乏熟練的專業人員

本體設計和資料建模的複雜性常常令現有IT團隊不堪負荷。此外，與舊有系統的整合增加了技術負擔，並減緩了採用速度。缺乏RDF、SPARQL和OWL等知識圖譜技術的合格專家是一大障礙。人才短缺限制了企業級解決方案的採用和擴充性。因此，許多企業不願在知識圖譜領域投入大量資金。

工業 4.0 和日益成長的數位化應用

組織使用知識圖譜來連接不同的資料來源，並推動更智慧的自動化和決策。隨著工廠和企業的數位化，情境智慧和即時數據整合變得越來越必要。為了契合工業 4.0 的目標，知識圖譜能夠從複雜的非結構化資料中提供有組織的洞察。知識圖譜支援預測分析，以最佳化流程並改進機器學習模型。市場對關聯數據的依賴日益加深，推動了對可擴展知識圖譜解決方案的需求。

資料隱私問題和監管合規性

由於需要嚴格遵守CCPA和GDPR等隱私法規，跨資料孤島的資料整合對組織而言極具挑戰性。這些限制資料共用和重複使用的法規使得建立端到端知識圖譜更具挑戰性。由於擔心資料外洩和潛在的法律後果，企業不願採用基於圖譜的解決方案。此外，匿名化技術通常會降低資料質量，進而影響知識圖譜的效能。因此，企業仍保持謹慎，導致市場採用速度緩慢。

COVID-19的影響

新冠疫情加速了數位轉型，並增加了對高階資料管理工具的需求，對知識圖譜市場產生了重大影響。隨著企業轉向遠端營運，對高效數據整合、情境化和即時洞察的需求激增。醫療保健、電子商務和金融等行業已利用知識圖譜來簡化決策流程並提升客戶體驗。最初對 IT 預算造成衝擊的因素，從長遠來看產生了積極影響，促使企業採用語義技術和人工智慧主導的資料框架。

預計解決方案部門將成為預測期內最大的部門

憑藉其先進的數據整合、語義搜尋和關係映射功能，解決方案領域預計將在預測期內佔據最大的市場佔有率。這些解決方案使企業能夠從複雜的資料集中獲得更深入的洞察，並做出明智的決策。越來越多的企業正在採用這些工具來改善客戶體驗、個人化服務並簡化業務。對人工智慧解決方案的需求正在加速其在醫療保健、金融和電子商務等行業的部署。

預計醫療保健和生命科學領域在預測期內將實現最高的複合年成長率。

在預測期內，醫療保健和生命科學領域預計將實現最高成長率，這得益於其能夠實現高級資料整合和對大量臨床資料集進行語義搜尋的能力。知識圖譜透過豐富的上下文資料建模，增強了藥物發現、患者照護和臨床試驗最佳化。知識圖譜透過連結不同的醫療記錄、基因組數據和研究論文，支持即時洞察和個人化醫療。它們還透過提供複雜生物醫學關係的統一視圖來改善決策。對智慧資料結構化日益成長的需求，正推動其在該領域的強勁應用。

佔比最大的地區：

在預測期內，由於電子商務、醫療保健和金融等領域的數位轉型不斷推進，亞太地區預計將佔據最大的市場佔有率。中國、日本和印度等國家正大力投資人工智慧和語意技術，推動這些技術的普及。精通技術的人口和政府主導的人工智慧舉措正在進一步增強市場。此外，人們對數據主導決策和自然語言處理的興趣日益濃厚，這促使企業採用知識圖譜來增強洞察力和自動化程度，使該地區成為創新和市場擴張的溫床。

複合年成長率最高的地區：

在預測期內，北美預計將呈現最高的複合年成長率，這主要得益於Google、微軟和 IBM 等科技巨頭的推動。對企業人工智慧、高階分析和個人化客戶體驗的旺盛需求正在推動市場成長。該地區受惠於成熟的雲端基礎設施和對語意網路技術的大量研發投入。知識圖譜正擴大被用於改善資料整合、增強搜尋能力，並推動醫療保健、金融服務和保險（BFSI）和媒體等領域的商業智慧。監管合規性和資料隱私方面的考量也在影響該地區解決方案的開發和部署。

免費客製化服務

本報告的所有訂閱者均可享有以下免費自訂選項之一：

  • 公司簡介
    • 對其他公司（最多 3 家）進行全面分析
    • 主要企業的SWOT分析（最多3家公司）
  • 區域分類
    • 根據客戶興趣對主要國家市場進行估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第 2 章 簡介

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 主要研究資料
    • 二手研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 介紹
  • 驅動程式
  • 限制因素
  • 市場機會
  • 威脅
  • 最終用戶分析
  • 新興市場
  • COVID-19的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 企業之間的競爭

5. 全球知識圖譜市場（按組成部分）

  • 解決方案
  • 服務

第6章全球知識圖譜市場（依部署方法）

  • 本地
  • 雲端基礎

第7章。按組織規模分類的全球知識圖譜市場

  • 小型企業
  • 主要企業

第8章全球知識圖譜市場（按應用）

  • 資料管理
  • 資訊搜尋
  • 建議引擎
  • 風險與合規管理
  • 資料整合
  • 語意搜尋
  • 其他用途

第9章全球知識圖譜市場（依最終用戶分類）

  • 醫學與生命科學
  • 零售與電子商務
  • 媒體與娛樂
  • 政府/公共部門
  • 資訊科技/通訊
  • 製造業
  • 教育
  • 其他最終用戶

第10章全球知識圖譜市場（按地區）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 合約、商業夥伴關係和合資企業
  • 企業合併與收購（M&A）
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章 公司概況

  • 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.