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1396666

全球知識圖譜市場 - 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個工作天內

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簡介目錄

概述

全球知識圖譜市場在 2022 年達到 7 億美元，預計到 2030 年將達到 36 億美元，2023-2030 年預測期間CAGR為 22.1%。

電子商務、內容交付和社交媒體平台使用知識圖來支援推薦系統，從而增強用戶體驗並推動用戶參與。許多組織需要有效的解決方案來整合和理解它們產生的大量結構化和非結構化資料。知識圖用於透過連結相關資訊並提供上下文來豐富內容。

知識圖譜提高了搜尋引擎和發現平台的效率和準確性，使用戶能夠更輕鬆地找到相關資訊。隨著資料隱私法規變得更加嚴格，組織尋求資料治理解決方案。知識圖透過提供資料沿襲和資料使用情況的可見性來協助資料治理。

由於主要參與者的產品發布增加，北美在知識圖譜市場中佔據了最大的市場佔有率。例如，2023 年 6 月 7 日，全球領先的圖形資料庫和分析公司 Neo4j 宣布與 Google Cloud Vertex AI 中的生成式 AI 功能整合新產品。 Vertex AI 的生成式 AI 功能用於為知識圖提供自然語言介面。

動力學

全球物聯網 (IoT) 的使用不斷成長

物聯網（IoT）設備產生各種各樣的資料。知識圖可以整合來自不同物聯網來源的資料，提供物聯網生態系統的整體視圖。物聯網資料有不同的格式和標準。知識圖有助於建立語義互通性，確保可以連貫地理解和分析來自各種物聯網設備的資料。知識圖即時處理和分析這些資料，從而可以立即做出決策並回應物聯網事件和異常。

物聯網資料在放置在上下文中時會變得更有價值。知識圖透過將物聯網資料連結到相關實體和關係來提供上下文，從而實現更深入的見解。知識圖與物聯網資料結合，支援預測分析。這對於預測性維護等應用特別有價值，物聯網感測器可以幫助預測設備故障。物流和供應鏈管理中的物聯網設備受益於知識圖。這些圖表提供了整個供應鏈的即時可見性和最佳化機會。

物聯網是智慧城市和基礎設施的關鍵組成部分。知識圖有助於管理和最佳化智慧城市的各個方面，從交通和公用事業到公共安全。醫療保健中的物聯網依賴於患者監測設備和穿戴式技術。知識圖使醫療保健提供者能夠匯總和分析患者資料，以改善護理和醫學研究。

全球擴大採用機器學習和人工智慧

機器學習和人工智慧用於豐富知識圖譜的內容。它從文字、圖像和影片等非結構化資料來源中提取有價值的見解，並用這些資訊填充知識圖譜。機器學習和人工智慧有助於理解資料的語義，從而識別實體和概念之間的關係。這改善了知識圖中連結的脈絡和相關性。

由機器學習演算法支援的知識圖支援電子商務、內容交付和個人化使用者體驗中的推薦系統。人工智慧驅動的推薦可提高用戶參與度和滿意度。人工智慧和自然語言處理技術可以實現與知識圖譜的對話互動。聊天機器人和虛擬助理存取和查詢知識圖譜，為使用者提供類似人類的互動和即時回應。

數據品質低落與知識圖譜整合

知識圖譜資料品質低，導致資訊不準確、過時。這破壞了知識庫的可信度並導致錯誤的結論。當知識圖提供資料的整體視圖並實現有意義的連結時，它們才最有價值。糟糕的資料整合使得創建這些連接變得困難，從而限制了知識圖的可用性和實用性。

不一致的資料結構和格式阻礙了知識圖譜內的語意一致性。因此，連結和理解資料存在困難。資料整合不足導致資料孤島，資訊孤立且無法進行分析。知識圖譜旨在打破這些孤島，但資料整合度低使得這個目標難以實現。

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