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It is anticipated that the knowledge graph market would increase from USD 0.19 billion in 2023 to USD 2.24 billion by 2030, representing a CAGR of 22.8% between 2023 and 2030.
Due to the ever-increasing volume, pace, and variety of big data, effective data processing and analysis have become increasingly important. Knowledge graphs offer real-time data processing, letting organizations make swift, data-driven decisions based on the most up-to-date information available. There's also an increasing demand for more complex data models that can comprehend and analyze human language in light of recent developments in natural language processing. By giving machines the ability to understand the context and links between words and sentences, knowledge graphs play a significant role in improving NLP capabilities.
The proliferation of commercial and consumer-generated data, along with cutting-edge AI (Artificial Intelligence) and ML (Machine Learning) algorithms, has made knowledge graph applications indispensable. Furthermore, the availability of high-performance computing resources and the proliferation of cloud computing platforms have made it easier to process enormous volumes of data and install complicated AI models. The Internet of Things (IoT) has added to this momentum, as AI/ML enables the extraction of useful insights from IoT data to expand knowledge graphs. Knowledge graph development has also benefited from advancements in Natural Language Processing (NLP) technology, which has made it easier to comprehend and extract relevant information from textual material. Artificial intelligence (AI) and machine learning (ML) are being used in more and more contexts because of their ability to automate processes, guarantee compliance with rules, and provide unique user experiences, all of which boosts demand. Moreover, the recent pandemic boosted digital transformation efforts, underlining the necessity for AI and ML solutions in knowledge graph applications to fulfill growing user expectations.
Several factors, including the complexity of the domain, the size of the knowledge graph, the technological stack employed, and the continuing maintenance requirements, can greatly affect the total cost of producing and maintaining a knowledge graph for the market. The size and intricacy of the knowledge graph is crucial, with larger and more complicated graphs typically resulting in higher development costs. Obtaining high-quality data sources may necessitate spending money on data acquisition or creating data collection methods. Costs can be affected by factors such as the selected technology stack, which may involve license fees and ongoing operational costs for cloud-based solutions. The price tag increases with the addition of elements like expert development teams, custom ontology and schema design, and regular upkeep. The price tag increases with the inclusion of scalability, security, and regulatory compliance needs.
There is a great deal of room for growth and innovation in the knowledge graph industry thanks to the introduction of NLP methods. Natural language processing allows for the incorporation of unstructured text data's entities, relations, and facts into the knowledge graph. It provides for sentiment analysis, contextual comprehension, and the ability to handle natural language queries, making knowledge graphs more accessible and user-friendly. Additionally, NLP can help with personalization, data quality, and trend analysis. Together, natural language processing (NLP) and knowledge graphs help businesses get new levels of insight from their data, streamline their data integration processes, and give customers a more personalized experience with their knowledge graph-based systems.
The knowledge graph industry is undergoing a revolutionary shift due to the incorporation of structured data sources. Companies may now build a thorough understanding of complicated links and interconnections by easily integrating multiple data repositories, such as databases and organized datasets, into the knowledge graph architecture. The knowledge graph can then be semantically enriched with additional meaning and context thanks to this incorporation. In addition, entity resolution from structured data sources is more effective, which improves data quality and consistency by removing duplicates. As a result, organizations are better able to understand complex knowledge domains and make decisions based on trustworthy insights thanks to the use of structured data as the foundation for generating a structured knowledge representation within the knowledge graph.
Report Coverage
Global Knowledge Graph research report categorizes the market for global based on various segments and regions, forecasts revenue growth, and analyzes trends in each submarket. Global Knowledge Graph report analyses the key growth drivers, opportunities, and challenges influencing the global market. Recent market developments and Knowledge Graph competitive strategies such as expansion, product launch and development, partnership, merger, and acquisition have been included to draw the competitive landscape in the market. The report strategically identifies and profiles the key Knowledge Graph market players and analyses their core competencies in each global market sub-segments.
Key Points Covered in the Report
Competitive Environment:
The research provides an accurate study of the major organisations and companies operating in the global Knowledge Graph market, along with a comparative evaluation based on their product portfolios, corporate summaries, geographic reach, business plans, Knowledge Graph market shares in specific segments, and SWOT analyses. A detailed analysis of the firms' recent news and developments, such as product development, inventions, joint ventures, partnerships, mergers and acquisitions, strategic alliances, and other activities, is also included in the study. This makes it possible to assess the level of market competition as a whole.
List of Major Market Participants
IBM (US), Microsoft (US), AWS (US), Neo4j (US), TigerGraph (US), SAP (Germany), Oracle (US), Stardog (US), Franz Inc (US), Ontotext (Bulgaria), Semantic Web Company (Austria), OpenLink Software (US), MarkLogic (US), Datavid (UK), GraphBase (Australia), Cambridge Semantics (US), CoverSight (US), Eccena Gmbh (Germany), ArangoDB (US), Fluree (US), DiffBot (US), Bitnine (US), Memgraph (England), GraphAware (UK), Onlim (Austria)
Primary Target Market
Market Segment:
This study forecasts global, regional, and country revenue from 2019 to 2030. INFINITIVE DATA EXPERT has segmented the global Knowledge Graph market based on the below-mentioned segments:
Global Knowledge Graph Market, By Model Type
RDF Graph
Conceptual Graph
Semantic Graph
Global Knowledge Graph market, By Application
Semantic Search
Question Answering
Recommendation Systems
Enterprise Knowledge Management
Other Applications
Global Knowledge Graph Market, By Data Source
Structured Data
Unstructured Data
Semi-structured Data
Global Knowledge Graph market, Regional Analysis
Due to the ever-increasing volume, pace, and variety of big data, effective data processing and analysis have become increasingly important. Knowledge graphs offer real-time data processing, letting organizations make swift, data-driven decisions based on the most up-to-date information available. There's also an increasing demand for more complex data models that can comprehend and analyze human language in light of recent developments in natural language processing. By giving machines the ability to understand the context and links between words and sentences, knowledge graphs play a significant role in improving NLP capabilities.
The proliferation of commercial and consumer-generated data, along with cutting-edge AI (Artificial Intelligence) and ML (Machine Learning) algorithms, has made knowledge graph applications indispensable. Furthermore, the availability of high-performance computing resources and the proliferation of cloud computing platforms have made it easier to process enormous volumes of data and install complicated AI models. The Internet of Things (IoT) has added to this momentum, as AI/ML enables the extraction of useful insights from IoT data to expand knowledge graphs. Knowledge graph development has also benefited from advancements in Natural Language Processing (NLP) technology, which has made it easier to comprehend and extract relevant information from textual material. Artificial intelligence (AI) and machine learning (ML) are being used in more and more contexts because of their ability to automate processes, guarantee compliance with rules, and provide unique user experiences, all of which boosts demand. Moreover, the recent pandemic boosted digital transformation efforts, underlining the necessity for AI and ML solutions in knowledge graph applications to fulfill growing user expectations.
Several factors, including the complexity of the domain, the size of the knowledge graph, the technological stack employed, and the continuing maintenance requirements, can greatly affect the total cost of producing and maintaining a knowledge graph for the market. The size and intricacy of the knowledge graph is crucial, with larger and more complicated graphs typically resulting in higher development costs. Obtaining high-quality data sources may necessitate spending money on data acquisition or creating data collection methods. Costs can be affected by factors such as the selected technology stack, which may involve license fees and ongoing operational costs for cloud-based solutions. The price tag increases with the addition of elements like expert development teams, custom ontology and schema design, and regular upkeep. The price tag increases with the inclusion of scalability, security, and regulatory compliance needs.
There is a great deal of room for growth and innovation in the knowledge graph industry thanks to the introduction of NLP methods. Natural language processing allows for the incorporation of unstructured text data's entities, relations, and facts into the knowledge graph. It provides for sentiment analysis, contextual comprehension, and the ability to handle natural language queries, making knowledge graphs more accessible and user-friendly. Additionally, NLP can help with personalization, data quality, and trend analysis. Together, natural language processing (NLP) and knowledge graphs help businesses get new levels of insight from their data, streamline their data integration processes, and give customers a more personalized experience with their knowledge graph-based systems.
The knowledge graph industry is undergoing a revolutionary shift due to the incorporation of structured data sources. Companies may now build a thorough understanding of complicated links and interconnections by easily integrating multiple data repositories, such as databases and organized datasets, into the knowledge graph architecture. The knowledge graph can then be semantically enriched with additional meaning and context thanks to this incorporation. In addition, entity resolution from structured data sources is more effective, which improves data quality and consistency by removing duplicates. As a result, organizations are better able to understand complex knowledge domains and make decisions based on trustworthy insights thanks to the use of structured data as the foundation for generating a structured knowledge representation within the knowledge graph.
Report Coverage
Global Knowledge Graph research report categorizes the market for global based on various segments and regions, forecasts revenue growth, and analyzes trends in each submarket. Global Knowledge Graph report analyses the key growth drivers, opportunities, and challenges influencing the global market. Recent market developments and Knowledge Graph competitive strategies such as expansion, product launch and development, partnership, merger, and acquisition have been included to draw the competitive landscape in the market. The report strategically identifies and profiles the key Knowledge Graph market players and analyses their core competencies in each global market sub-segments.
| REPORT ATTRIBUTES | DETAILS |
|---|---|
| Study Period | 2017-2030 |
| Base Year | 2022 |
| Forecast Period | 2022-2030 |
| Historical Period | 2017-2021 |
| Unit | Value (USD Billion) |
| Key Companies Profiled | IBM (US), Microsoft (US), AWS (US), Neo4j (US), TigerGraph (US), SAP (Germany), Oracle (US), Stardog (US), Franz Inc (US), Ontotext (Bulgaria), Semantic Web Company (Austria), OpenLink Software (US), MarkLogic (US), Datavid (UK), GraphBase (Australia), Cambridge Semantics (US), CoverSight (US), Eccena Gmbh (Germany), ArangoDB (US), Fluree (US), DiffBot (US), Bitnine (US), Memgraph (England), GraphAware (UK), Onlim (Austria) |
| Segments Covered | • By Product |
| Customization Scope | Free report customization (equivalent to up to 3 analyst working days) with purchase. Addition or alteration to country, regional & segment scope |
Key Points Covered in the Report
- Market Revenue of Knowledge Graph Market from 2021 to 2030.
- Market Forecast for Knowledge Graph Market from 2021 to 2030.
- Regional Market Share and Revenue from 2021 to 2030.
- Country Market share within region from 2021 to 2030.
- Key Type and Application Revenue and forecast.
- Company Market Share Analysis, Knowledge Graph competitive scenario, ranking, and detailed company
profiles. - Market driver, restraints, and detailed COVID-19 impact on Knowledge Graph
Market
Competitive Environment:
The research provides an accurate study of the major organisations and companies operating in the global Knowledge Graph market, along with a comparative evaluation based on their product portfolios, corporate summaries, geographic reach, business plans, Knowledge Graph market shares in specific segments, and SWOT analyses. A detailed analysis of the firms' recent news and developments, such as product development, inventions, joint ventures, partnerships, mergers and acquisitions, strategic alliances, and other activities, is also included in the study. This makes it possible to assess the level of market competition as a whole.
List of Major Market Participants
IBM (US), Microsoft (US), AWS (US), Neo4j (US), TigerGraph (US), SAP (Germany), Oracle (US), Stardog (US), Franz Inc (US), Ontotext (Bulgaria), Semantic Web Company (Austria), OpenLink Software (US), MarkLogic (US), Datavid (UK), GraphBase (Australia), Cambridge Semantics (US), CoverSight (US), Eccena Gmbh (Germany), ArangoDB (US), Fluree (US), DiffBot (US), Bitnine (US), Memgraph (England), GraphAware (UK), Onlim (Austria)
Primary Target Market
- Market Players of Knowledge Graph
- Investors
- End-users
- Government Authorities
- Consulting And Research Firm
- Venture capitalists
- Third-party knowledge providers
- Value-Added Resellers (VARs)
Market Segment:
This study forecasts global, regional, and country revenue from 2019 to 2030. INFINITIVE DATA EXPERT has segmented the global Knowledge Graph market based on the below-mentioned segments:
Global Knowledge Graph Market, By Model Type
RDF Graph
Conceptual Graph
Semantic Graph
Global Knowledge Graph market, By Application
Semantic Search
Question Answering
Recommendation Systems
Enterprise Knowledge Management
Other Applications
Global Knowledge Graph Market, By Data Source
Structured Data
Unstructured Data
Semi-structured Data
Global Knowledge Graph market, Regional Analysis
- Europe: Germany, Uk, France, Italy, Spain, Russia, Rest of Europe
- The Asia Pacific: China,Japan,India,South Korea,Australia,Rest of Asia Pacific
- South America: Brazil, Argentina, Rest of South America
- Middle East & Africa: UAE, Saudi Arabia, Qatar, South Africa, Rest of Middle East & Africa
About This Research
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