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ナレッジグラフの世界市場-2023年～2030年

Global Knowledge Graph Market - 2023-2030

出版日
ページ情報
英文 232 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=144.64円
ナレッジグラフの世界市場-2023年～2030年
出版日: 2023年12月15日
発行: DataM Intelligence
ページ情報: 英文 232 Pages
納期: 即日から翌営業日
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  • 概要
  • 目次
概要

概要

世界のナレッジグラフ市場は、2022年に7億米ドルに達し、2023-2030年の予測期間中にCAGR 22.1％で成長し、2030年には36億米ドルに達すると予測されています。

eコマース、コンテンツ配信、ソーシャルメディア・プラットフォームは、ナレッジグラフを利用して、ユーザー体験を向上させ、ユーザー・エンゲージメントを促進するレコメンデーション・システムを構築しています。多くの企業では、膨大な量の構造化データおよび非構造化データを統合し、理解するための効果的なソリューションを必要としています。ナレッジグラフは、関連する情報をリンクし、コンテキストを提供することで、コンテンツを充実させるために採用されています。

ナレッジグラフは、検索エンジンやディスカバリ・プラットフォームの効率と精度を向上させ、ユーザーが関連情報をより簡単に見つけることを可能にします。データ・プライバシー規制が厳しくなるにつれ、企業はデータ・ガバナンス・ソリューションを求めています。ナレッジグラフは、データのリネージやデータ利用の可視性を提供することで、データガバナンスを支援します。

ナレッジグラフ市場では、主要プレーヤーによる製品投入の増加により、北米が最大の市場シェアを占めています。例えば、2023年06月07日、グラフデータベースとアナリティクスの世界的大手企業であるNeo4jは、Google Cloud Vertex AIにおけるジェネレーティブAI機能との新製品統合を発表しました。Vertex AIのジェネレーティブAI機能は、ナレッジグラフへの自然言語インタフェースを提供するために使用されます。

力学

世界のモノのインターネット（IoT）の利用拡大

モノのインターネット（IoT）デバイスは多種多様なデータを生成します。ナレッジグラフは、多様なIoTソースからのデータの統合を可能にし、IoTエコシステムの全体的なビューを提供します。IoTデータはさまざまなフォーマットや規格で提供されています。ナレッジグラフはセマンティックな相互運用性を確立し、さまざまなIoTデバイスからのデータを首尾一貫して理解・分析できるようにします。ナレッジグラフは、このデータをリアルタイムで処理・分析し、IoTイベントや異常に対する迅速な意思決定と対応を可能にします。

IoTデータは、コンテキストの中に置かれることで、より価値が高まります。ナレッジグラフは、IoTデータを関連するエンティティや関係にリンクすることでコンテキストを提供し、より深い洞察を可能にします。ナレッジグラフは、IoTデータと組み合わせることで、予測分析をサポートします。ナレッジグラフは、IoTセンサーが機器の故障を予測する、予知保全のようなアプリケーションで特に価値があります。ロジスティクスやサプライチェーン管理におけるIoTデバイスは、ナレッジグラフの恩恵を受ける。このグラフは、サプライチェーン全体にわたってリアルタイムの可視性と最適化の機会を提供します。

IoTはスマートシティやインフラの重要な構成要素です。ナレッジグラフは、交通やユーティリティから公共安全まで、スマートシティのさまざまな側面の管理と最適化に役立ちます。ヘルスケアにおけるIoTは、患者モニタリング・デバイスやウェアラブル・テクノロジーに依存しています。ナレッジグラフにより、ヘルスケアプロバイダーは患者データを集約・分析し、ケアや医療研究の改善に役立てることができます。

世界的に拡大する機械学習と人工知能の採用

機械学習と人工知能は、ナレッジグラフのコンテンツを充実させるために使用されます。機械学習と人工知能は、テキスト、画像、動画などの非構造化データ・ソースから貴重な洞察を抽出し、ナレッジ・グラフにその情報を入力します。機械学習と人工知能はデータのセマンティクスを理解するのに役立ち、エンティティや概念間の関係を特定することを可能にします。これにより、ナレッジグラフ内の接続のコンテキストと関連性が向上します。

ナレッジグラフは、機械学習アルゴリズムによって、eコマース、コンテンツ配信、パーソナライズされたユーザー体験におけるレコメンデーションシステムをサポートします。AI主導のレコメンデーションは、ユーザーのエンゲージメントと満足度を高める。人工知能と自然言語処理技術は、ナレッジグラフとの会話型インタラクションを可能にします。チャットボットやバーチャルアシスタントがナレッジグラフにアクセスし、クエリを実行することで、人間に近いインタラクションや即座の応答をユーザーに提供します。

ナレッジグラフの低いデータ品質と統合性

ナレッジグラフのデータ品質が低いと、不正確で古い情報になります。これは知識ベースの信頼性を損ない、誤った結論につながります。ナレッジグラフは、データの全体的なビューを提供し、意味のある接続を可能にするときに最も価値があります。データ統合が不十分だと、このような接続を作成することが困難になり、ナレッジグラフの使いやすさと有用性が制限されます。

一貫性のないデータ構造やフォーマットは、ナレッジグラフ内の意味的一貫性を阻害します。このため、データのリンクや意味づけに困難が生じる。データ統合が不十分なため、データがサイロ化し、情報が孤立して分析にアクセスできないです。ナレッジグラフは、このようなサイロ化を解消するために設計されているが、データ統合が不十分なため、この目標を達成することが難しいです。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • 世界のモノのインターネット（IoT）の利用拡大
      • 世界の機械学習と人工知能の採用拡大
    • 抑制要因
      • 低いデータ品質とナレッジグラフの統合
    • 機会
    • 影響分析

第5章 業界分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析

第6章 COVID-19分析

第7章 タイプ別

  • 一般知識グラフ
  • 業界知識グラフ

第8章 タスク別

  • リンク予測
  • エンティティ解決
  • リンクベースのクラスタリング
  • インターネット
  • その他

第9章 データソース別

  • 構造化
  • 非構造化
  • 半構造化

第10章 組織規模別

  • 中小企業
  • 大企業

第11章 用途別

  • セマンティック検索
  • 推薦システム
  • データ統合
  • ナレッジマネジメント
  • AI・機械学習

第12章 エンドユーザー別

  • ヘルスケア
  • eコマース＆リテール
  • BFSI
  • 政府機関
  • メディア＆エンターテイメント
  • その他

第13章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • ロシア
    • その他欧州
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋地域
  • 中東・アフリカ

第14章 競合情勢

  • 競合シナリオ
  • 市況/シェア分析
  • M&A分析

第15章 企業プロファイル

  • AWS
    • 会社概要
    • 製品ポートフォリオと説明
    • 財務概要
    • 主な発展
  • Cambridge Semantics
  • Franz Inc.
  • Google
  • IBM Corporation
  • Microsoft
  • Stardog
  • Neo4j
  • Ontotext
  • Oracle

第16章 付録

目次
Product Code: ICT7544

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

Table of Contents

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