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全球知识图谱市场 - 2023-2030

Global Knowledge Graph Market - 2023-2030

出版日期: 2023年12月15日 | 出版商:

DataM Intelligence | 英文 232 Pages | 商品交期: 最快1-2个工作天内

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简介目录

概述

全球知识图谱市场在 2022 年达到 7 亿美元，预计到 2030 年将达到 36 亿美元，2023-2030 年预测期间CAGR为 22.1%。

电子商务、内容交付和社交媒体平台使用知识图来支援推荐系统，从而增强用户体验并推动用户参与。许多组织需要有效的解决方案来整合和理解它们产生的大量结构化和非结构化资料。知识图用于透过连结相关资讯并提供上下文来丰富内容。

知识图谱提高了搜寻引擎和发现平台的效率和准确性，使用户能够更轻鬆地找到相关资讯。随着资料隐私法规变得更加严格，组织寻求资料治理解决方案。知识图透过提供资料沿袭和资料使用情况的可见性来协助资料治理。

由于主要参与者的产品发布增加，北美在知识图谱市场中占据了最大的市场份额。例如，2023 年 6 月 7 日，全球领先的图形资料库和分析公司 Neo4j 宣布与 Google Cloud Vertex AI 中的生成式 AI 功能整合新产品。 Vertex AI 的生成式 AI 功能用于为知识图提供自然语言介面。

动力学

全球物联网 (IoT) 的使用不断增长

物联网（IoT）设备产生各种各样的资料。知识图可以整合来自不同物联网来源的资料，提供物联网生态系统的整体视图。物联网资料有不同的格式和标准。知识图有助于建立语义互通性，确保可以连贯地理解和分析来自各种物联网设备的资料。知识图即时处理和分析这些资料，从而可以立即做出决策并回应物联网事件和异常。

物联网资料在放置在上下文中时会变得更有价值。知识图透过将物联网资料连结到相关实体和关係来提供上下文，从而实现更深入的见解。知识图与物联网资料结合，支援预测分析。这对于预测性维护等应用特别有价值，物联网感测器可以帮助预测设备故障。物流和供应链管理中的物联网设备受益于知识图。这些图表提供了整个供应链的即时可见性和优化机会。

物联网是智慧城市和基础设施的关键组成部分。知识图有助于管理和优化智慧城市的各个方面，从交通和公用事业到公共安全。医疗保健中的物联网依赖于患者监测设备和穿戴式技术。知识图使医疗保健提供者能够汇总和分析患者资料，以改善护理和医学研究。

全球越来越多地采用机器学习和人工智慧

机器学习和人工智慧用于丰富知识图谱的内容。它从文字、图像和影片等非结构化资料来源中提取有价值的见解，并用这些资讯填充知识图谱。机器学习和人工智慧有助于理解资料的语义，从而识别实体和概念之间的关係。这改善了知识图中连结的脉络和相关性。

由机器学习演算法支援的知识图支援电子商务、内容交付和个人化使用者体验中的推荐系统。人工智慧驱动的推荐可提高用户参与度和满意度。人工智慧和自然语言处理技术可以实现与知识图谱的对话互动。聊天机器人和虚拟助理存取和查询知识图谱，为使用者提供类似人类的互动和即时回应。

数据品质低落与知识图谱整合

知识图谱资料品质低，导致资讯不准确、过时。这破坏了知识库的可信度并导致错误的结论。当知识图提供资料的整体视图并实现有意义的连结时，它们才最有价值。糟糕的资料整合使得创建这些连接变得困难，从而限制了知识图的可用性和实用性。

不一致的资料结构和格式阻碍了知识图谱内的语意一致性。因此，连结和理解资料存在困难。资料整合不足导致资料孤岛，资讯孤立且无法进行分析。知识图谱旨在打破这些孤岛，但资料整合度低使得这个目标难以实现。

Overview

Global Knowledge Graph Market reached US$ 0.7 billion in 2022 and is expected to reach US$ 3.6 billion by 2030, growing with a CAGR of 22.1% during the forecast period 2023-2030.

E-commerce, content delivery and social media platforms use knowledge graphs to power recommendation systems that enhance user experiences and drive user engagement. Many organizations need effective solutions to integrate and make sense of the vast amounts of structured and unstructured data they generate. Knowledge graphs are employed to enrich content by linking related information and providing context.

Knowledge graphs improve the efficiency and accuracy of search engines and discovery platforms, enabling users to find relevant information more easily. As data privacy regulations become more stringent organizations seek data governance solutions. Knowledge graphs assist in data governance by providing data lineage and visibility into data usage.

North America accounted largest market share in the knowledge graph market due to the increase in product launches by major key players. For instance, on June 07, 2023, Neo4j, the world's leading graph database and analytics company announced new product integration with Generative AI Features in Google Cloud Vertex AI. Vertex AI's generative AI capabilities are used to provide a natural language interface to the knowledge graph.

Dynamics

Growing Use of the Internet of Things (IoT) Globally

Internet of Things(IoT) devices produce a wide variety of data. Knowledge Graphs enable the integration of data from diverse IoT sources, providing a holistic view of the IoT ecosystem. IoT data come in different formats and standards. Knowledge graphs help establish semantic interoperability, ensuring that data from various IoT devices can be understood and analyzed coherently. Knowledge graphs process and analyze this data in real time, allowing for immediate decision-making and response to IoT events and anomalies.

IoT data becomes more valuable when placed in context. Knowledge Graphs provide the context by linking IoT data to relevant entities and relationships, enabling deeper insights. Knowledge graphs, when combined with IoT data, support predictive analytics. The is particularly valuable for applications like predictive maintenance, where IoT sensors help anticipate equipment failures. IoT devices in logistics and supply chain management benefit from knowledge graphs. The graphs provide real-time visibility and optimization opportunities throughout the supply chain.

IoT is a key component of smart cities and infrastructure. Knowledge graphs help manage and optimize various aspects of smart cities, from traffic and utilities to public safety. IoT in healthcare relies on patient monitoring devices and wearable technology. Knowledge graphs enable healthcare providers to aggregate and analyze patient data for improved care and medical research.

Growing Adoption of Machine Learning and Artificial Intelligence Globally

Machine learning and artificial intelligence are used to enrich the content of a knowledge graph. It extract valuable insights from unstructured data sources such as text, images and videos and populate the knowledge graph with this information. Machine learning and artificial intelligence help in understanding the semantics of data, enabling the identification of relationships between entities and concepts. The improves the context and relevance of the connections within the knowledge graph.

Knowledge graphs, when powered by machine learning algorithms support recommendation systems in e-commerce, content delivery and personalized user experiences. AI-driven recommendations enhance user engagement and satisfaction. Artificial intelligence and natural language processing technologies enable conversational interactions with knowledge graphs. Chatbots and virtual assistants access and query the knowledge graph, providing users with human-like interactions and instant responses.

Low Data Quality and Integration of Knowledge Graph

Low data quality of knowledge graph results in inaccurate and outdated information. The undermines the trustworthiness of the knowledge base and leads to erroneous conclusions. Knowledge graphs are most valuable when they provide a holistic view of data and enable meaningful connections. Poor data integration makes it challenging to create these connections, limiting the usability and utility of the knowledge graph.

Inconsistent data structures and formats hinder semantic consistency within the knowledge graph. Due to this, there are difficulties in linking and making sense of the data. Inadequate data integration resulted in data silos, where information is isolated and not accessible for analysis. Knowledge graphs are designed to break down these silos, but low data integration makes it difficult to achieve this goal.

Segment Analysis

The global knowledge graph market is segmented based on type, task, data source organization size, application, end-user and region.

Growing Industrial Adoption of the Structured Knowledge Graph

Based on the data source, the knowledge graph market is divided into structured, unstructured and semi-structured. The structured segment accounted for 1/3rd of the market share in the global knowledge graph market. Structured data sources provide well-organized and standardized data and make it easier to integrate information from multiple sources. The integration is crucial for building comprehensive and interconnected knowledge graphs.

Structured data sources offer higher data quality compared to unstructured or semi-structured data. The is essential for ensuring that the information in the knowledge graph is accurate and trustworthy. Structured data sources are semantically consistent, with clear definitions and standardized formats. The consistency facilitates the creation of meaningful relationships and connections within the knowledge graph. In many domains and industries, structured data sources adhere to industry-specific standards and regulations, ensuring compliance and data consistency in the knowledge graph.

Growing product launches by major key players help to boost market growth over the forecast period. For instance, on February 01, 2022, Clausematch, a technology company launched a structured knowledge graph in the market to drive the digitization of regulation with the use of AI. The company has been involved in various projects in this domain. Regulators and financial services companies have access to test the graph and see how regulation in a structured digital format works.

Geographical Penetration

High Penetration of Digital Advertising in North America

North America accounted largest market share in the global knowledge graph market due to rapid growth in artificial intelligence and machine learning platforms. The U.S. and Canada accounted for the largest market share due to the availability of large enterprises. Knowledge graphs help organizations integrate data from different sources and make it easier to analyze and derive insights from structured and unstructured data.

Knowledge graphs have a growing role in healthcare and life sciences for patient data integration, drug discovery and clinical decision support systems. According to the data given by cross river therapy in 2022, U.S. healthcare industry is the world's third-largest economy. The U.S. has the greatest healthcare spending US$10,224 per capita. Also growing adoption of the knowledge graphs in the financial sector for risk assessment, fraud detection and portfolio management in North America helps to boost regional market growth of the knowledge graph market.

Competitive Landscape

The major global players in the market include: AWS, Cambridge Semantics, Franz Inc., Google, IBM Corporation, Microsoft, Stardog, Neo4j, Ontotext and Oracle.

COVID-19 Impact Analysis

The need for organizations to adapt to remote work and changing business environments has increased the focus on data integration. Knowledge graphs, with their ability to integrate diverse data sources, become more critical for organizations aiming to streamline their data workflows. The pandemic accelerated digital transformation initiatives across industries. Businesses and institutions that invested in digital technologies, including knowledge graphs, have found them valuable for organizing and leveraging data in the new normal.

The dynamic nature of the pandemic emphasized the importance of real-time analytics. Knowledge graphs when combined with technologies like graph databases and semantic technologies provide the foundation for real-time insights by connecting and analyzing data in near real-time. Some sectors, such as healthcare have seen increased interest in knowledge graphs for modeling and analyzing complex relationships in medical data. Other sectors, particularly those facing economic challenges, have slowed down certain technology investments.

Russia-Ukraine War Impact Analysis

Geopolitical events contribute to global economic uncertainty. Uncertain economic conditions influence organizations' budget allocations, potentially affecting investment decisions in technology, including knowledge graph initiatives. The impact on the knowledge graph market varies by region. Instability in certain regions leads to shifts in priorities, investments or project timelines.

Supply chain disruptions caused by geopolitical events affect the availability and cost of technology components. Organizations implementing knowledge graphs might need to assess and adapt to changes in the supply chain for relevant technologies. Government priorities and funding for technology initiatives shift during periods of geopolitical tension. The impact knowledge graph projects that receive government support or are aligned with specific national or regional strategies.

By Type

General Knowledge Graph
Industry Knowledge Graph

By Task

Link Prediction
Entity Resolution
Link-based Clustering
Internet
Others

By Data Source

Structured
Unstructured
Semi-structured

By Organization Size

SMEs
Large Enterprises

By Application

Semantic search
Recommendation systems
Data integration
Knowledge management
AI & machine learning

By End-User

Healthcare
E-commerce & retail
BFSI
Government
Media & entertainment
Others

By Region

North America
- U.S.
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Russia
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Key Developments

On March 21, 2023, Kobai, the codeless knowledge graph platform launched Kobai Saturn, a knowledge graph. The newly launched graph is the industry's first knowledge graph to harness the scale, performance and cost efficiency of the bakehouse architecture.
On November 05, 2023, Foursquare, an independent geospatial technology platform launched its geospatial knowledge graph in the market. The newly launched graph helps to lower the barrier to entry for location intelligence and limits the time it takes to uncover crucial insights within geospatial data queries.
On May 02, 2022, the Copyright Clearance Center (CCC) announced robust knowledge graph capabilities through the CCC expert view. It provides details about at Bio-IT World Session. Copyright clearance center expert view, a knowledge graph has capabilities to help life science companies identify qualified experts.

Why Purchase the Report?

To visualize the global knowledge graph market segmentation based on type, task, data source organization size, application, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of knowledge graph market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global knowledge graph market report would provide approximately 85 tables, 92 figures and 232 Pages.

Target Audience 2023

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Type
3.2. Snippet by Task
3.3. Snippet by Data Source
3.4. Snippet by Organization Size
3.5. Snippet by Application
3.6. Snippet by End-User
3.7. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Growing Use of the Internet of Things (IoT) Globally
  - 4.1.1.2. Growing Adoption of Machine Learning and Artificial Intelligence Globally
- 4.1.2. Restraints
  - 4.1.2.1. Low Data Quality and Integration of Knowledge Graph
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Type

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 7.1.2. Market Attractiveness Index, By Type
7.2. General Knowledge Graph*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Industry Knowledge Graph

8. By Task

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Task
- 8.1.2. Market Attractiveness Index, By Task
8.2. Link Prediction*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Entity Resolution
8.4. Link-based Clustering
8.5. Internet
8.6. Others

9. By Data Source

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Data Source
- 9.1.2. Market Attractiveness Index, By Data Source
9.2. Structured*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Unstructured
9.4. Semi-structured

10. By Organization Size

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 10.1.2. Market Attractiveness Index, By Organization Size
10.2. SMEs*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Large Enterprises

11. By Application

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.1.2. Market Attractiveness Index, By Application
11.2. Semantic Search*
- 11.2.1. Introduction
- 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
11.3. Recommendation systems
11.4. Data integration
11.5. Knowledge management
11.6. AI & machine learning

12. By End-User

12.1. Introduction
- 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.1.2. Market Attractiveness Index, By End-User
12.2. Healthcare*
- 12.2.1. Introduction
- 12.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
12.3. E-commerce & retail
12.4. BFSI
12.5. Government
12.6. Media & entertainment
12.7. Others

13. By Region

13.1. Introduction
- 13.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 13.1.2. Market Attractiveness Index, By Region
13.2. North America
- 13.2.1. Introduction
- 13.2.2. Key Region-Specific Dynamics
- 13.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 13.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Task
- 13.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Data Source
- 13.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 13.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.2.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.2.9.1. U.S.
  - 13.2.9.2. Canada
  - 13.2.9.3. Mexico
13.3. Europe
- 13.3.1. Introduction
- 13.3.2. Key Region-Specific Dynamics
- 13.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 13.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Task
- 13.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Data Source
- 13.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 13.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.3.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.3.9.1. Germany
  - 13.3.9.2. UK
  - 13.3.9.3. France
  - 13.3.9.4. Italy
  - 13.3.9.5. Russia
  - 13.3.9.6. Rest of Europe
13.4. South America
- 13.4.1. Introduction
- 13.4.2. Key Region-Specific Dynamics
- 13.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 13.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Task
- 13.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Data Source
- 13.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 13.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.4.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.4.9.1. Brazil
  - 13.4.9.2. Argentina
  - 13.4.9.3. Rest of South America
13.5. Asia-Pacific
- 13.5.1. Introduction
- 13.5.2. Key Region-Specific Dynamics
- 13.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 13.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Task
- 13.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Data Source
- 13.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 13.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.5.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.5.9.1. China
  - 13.5.9.2. India
  - 13.5.9.3. Japan
  - 13.5.9.4. Australia
  - 13.5.9.5. Rest of Asia-Pacific
13.6. Middle East and Africa
- 13.6.1. Introduction
- 13.6.2. Key Region-Specific Dynamics
- 13.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 13.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Task
- 13.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Data Source
- 13.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 13.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.6.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

14. Competitive Landscape

14.1. Competitive Scenario
14.2. Market Positioning/Share Analysis
14.3. Mergers and Acquisitions Analysis

15. Company Profiles

15.1. AWS*
- 15.1.1. Company Overview
- 15.1.2. Product Portfolio and Description
- 15.1.3. Financial Overview
- 15.1.4. Key Developments
15.2. Cambridge Semantics
15.3. Franz Inc.
15.4. Google
15.5. IBM Corporation
15.6. Microsoft
15.7. Stardog
15.8. Neo4j
15.9. Ontotext
15.10. Oracle

LIST NOT EXHAUSTIVE

16. Appendix

16.1. About Us and Services
16.2. Contact Us

02-2729-4219

+886-2-2729-4219

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