Attention all professionals looking to modernize your knowledge graphs and architecture!
Do you struggle with asking the right questions to drive results? Are you tired of spending endless hours sifting through irrelevant information, only to end up with subpar outcomes?Introducing our Knowledge Graphs and Architecture Modernization Knowledge Base – the ultimate tool to streamline your processes and achieve optimal results by urgency and scope.
With 1,541 carefully curated prioritized requirements, solutions, benefits, and case studies, our dataset is the most comprehensive and efficient resource on the market.
Don′t settle for mediocre results and wasting precious time and resources.
Our Knowledge Graphs and Architecture Modernization Knowledge Base stands out among our competitors and alternatives, specifically designed for professionals like you.
Featuring a user-friendly interface, this do-it-yourself and affordable product is suitable for any business seeking to modernize their knowledge architecture.
Our dataset offers a detailed overview of the product specifications and types, making it easy for you to find the perfect solution for your needs.
Don′t waste time on semi-related products – our Knowledge Graphs and Architecture Modernization Knowledge Base is tailored specifically for your industry.
But what truly sets us apart from the rest is the numerous benefits of our product.
Say goodbye to trial and error, and hello to streamlined processes and optimal results.
Our Knowledge Graphs and Architecture Modernization Knowledge Base has been extensively researched to ensure it meets the highest standards, providing you with accurate and effective solutions every time.
Concerned about the cost? Don′t be.
Our product offers an affordable alternative without compromising on quality.
We understand the importance of modernizing your knowledge graphs and architecture, which is why we have made it accessible to businesses of all sizes.
Still unsure? Let us break it down for you.
Our Knowledge Graphs and Architecture Modernization Knowledge Base will save you time, resources, and money, all while driving optimal results.
With our dataset, you′ll be able to ask the right questions and get the right solutions, enhancing your overall business performance.
Don′t hesitate any longer – join the numerous businesses already benefiting from our Knowledge Graphs and Architecture Modernization Knowledge Base.
Upgrade your knowledge infrastructure and stay ahead of your competitors with our comprehensive and efficient solution.
Experience the ease and effectiveness of modernizing your processes with us today!
What would prompt engineering for data integration look like? How can certain steps in knowledge engineering be automated or crowdsourced, and to what extent? Why do you need ontology for effective business decision making?
Knowledge Graphs
Knowledge Graphs for data integration involve engineering efforts to model, map, and merge diverse data sources into a unified graph structure, ensuring interoperability and enabling sophisticated querying and analytics.
1. Improved data accuracy: Knowledge graphs ensure consistent data representation, reducing errors.
2. Better decision-making: Connected data leads to well-informed decisions and strategic planning.
3. Enhanced data discovery: Graphs reveal hidden relationships, enabling insights.
4. Faster data integration: Graphs enable easier connections between disparate data sources.
5. Scalability: Graph databases handle growing data volumes seamlessly.
6. Improved collaboration: Shared knowledge graphs foster teamwork and knowledge transfer.
7. Data governance: Graphs aid in implementing consistent data management policies.
CONTROL QUESTION: What would prompt engineering for data integration look like?
Big Hairy Audacious Goal (BHAG) for 10 years from now: A big, hairy, audacious goal (BHAG) for knowledge graphs in 10 years, regarding engineering for data integration, could be:
By 2032, enable seamless and universal data integration through a decentralized, interoperable, and autonomic knowledge graph fabric, powering real-time, context-aware, and privacy-preserving data access, analysis, and decision-making for individuals, organizations, and machines.
To achieve this BHAG, the following engineering challenges and opportunities should be explored:
1. Decentralized architecture: Design a decentralized, peer-to-peer knowledge graph fabric, utilizing distributed ledger technology or similar approaches for data integrity and consensus. Create self-organizing and self-healing networks for scalability, fault-tolerance, and resiliency.
2. Interoperability and standardization: Develop, adopt and maintain a comprehensive set of open standards for metadata representation, data exchange, and querying, focusing on extensibility, adaptability, and openness to accommodate diverse data sources and domains.
3. Autonomic data integration: Build data integration capabilities into the core architecture, allowing for automatic schema mapping, entity resolution, and data transformation, minimizing human intervention, and ensuring seamless and adaptive data integration.
4. Real-time data processing: Design low-latency and high-performance data processing and querying systems, incorporating advanced caching, indexing, and parallel processing techniques, optimized for diverse query patterns and use cases.
5. Context-awareness: Build context-aware data access and retrieval capabilities, incorporating machine learning models, reasoning engines, and natural language processing techniques, enabling accurate and relevant data delivery.
6. Privacy and security: Implement privacy-preserving data structures and access patterns, incorporating encryption, differential privacy, secure multi-party computation, and other techniques for maintaining data confidentiality. Ensure secure data access, distribution, and sharing through robust authentication, authorization, and auditing mechanisms.
7. Usability and education: Invest in user interface design, training, and education, focusing on simplicity, automation, and accessibility, empowering data professionals and non-experts to effectively harness the power of knowledge graphs for data-driven decision-making.
8. Governance and sustainability: Establish a robust governance framework for community collaboration, decision-making, and maintenance, balancing the needs of diverse stakeholders and ensuring the long-term vision, sustainability, and ethical alignment of the knowledge graph fabric.
This BHAG would transform data integration, enabling a universal data infrastructure, underpinning a myriad of data-driven applications and services, fostering innovation, efficiency, and collaboration for individuals, organizations, and machines.
"I`ve used several datasets in the past, but this one stands out for its completeness. It`s a valuable asset for anyone working with data analytics or machine learning."
Synopsis:
The client is a multinational retail corporation looking to integrate and analyze data from various internal and external sources such as customer data, sales data, and social media data. These data sources are typically stored in different formats, silos, and disparate systems, making it challenging for the client to gain a holistic and actionable understanding of their data. The client approached our consulting firm to develop a data integration and analysis solution that could address their challenges and meet their business objectives.
Consulting Methodology:
To address the client′s needs, we proposed a data integration solution based on knowledge graphs. Knowledge graphs are a type of graph database that can model, store, and query complex and interconnected data, enabling users to discover insights and relationships that are not easily discernible through traditional tabular databases.
Our consulting methodology involved the following steps:
1. Data Discovery: We conducted a thorough data discovery phase to identify the data sources, formats, and characteristics that needed to be integrated. This phase involved working closely with the client′s data owners and stakeholders to understand their data needs, requirements, and constraints.
2. Data Modeling: Based on the data discovery phase, we developed a data model that represented the entities, relationships, and attributes of the data. This phase involved designing a knowledge graph schema that could capture the semantics and structure of the data, enabling us to create a unified and consistent view of the data.
3. Data Integration: We implemented a data integration pipeline that extracted, transformed, and loaded the data from the disparate sources into the knowledge graph. This phase involved developing data connectors, data cleansing, and data mapping rules, and data quality checks.
4. Data Analysis: We developed a set of data analysis tools and visualizations that enabled the client to explore and query the knowledge graph. This phase involved developing SPARQL queries, graph algorithms, and data analytics dashboards.
5. Data Governance: We established a data governance framework that ensured the quality, security, and privacy of the data. This phase involved developing data access policies, data security plans, and data backup and recovery procedures.
Deliverables:
The following are the key deliverables of the project:
1. Data Discovery Report: A comprehensive report that describes the data sources, formats, and characteristics, along with the data quality and data completeness metrics.
2. Data Model Documentation: A detailed documentation that describes the knowledge graph schema, entities, relationships, and attributes.
3. Data Integration Pipeline: A set of data integration scripts, tools, and workflows that enable the automated extraction, transformation, and loading of the data.
4. Data Analysis Tools: A set of data analysis tools and visualizations that enable the exploration and querying of the knowledge graph.
5. Data Governance Framework: A set of data governance policies, procedures, and guidelines that ensure the quality, security, and privacy of the data.
Implementation Challenges:
1. Data Quality: Ensuring the quality, accuracy, and completeness of the data was a significant challenge. We had to develop and implement robust data cleansing, data matching, and data validation rules to ensure the data quality.
2. Data Security: Ensuring the security and privacy of the data was a critical challenge. We had to implement strict data access policies, data encryption, and data anonymization techniques to protect the data.
3. Data Complexity: Handling the complexity and diversity of the data was a significant challenge. We had to design a flexible and scalable data model that could accommodate the various data sources, formats, and relationships.
4. Data Scalability: Ensuring the scalability and performance of the knowledge graph was a critical challenge. We had to design a distributed and horizontally scalable architecture that could handle large volumes of data and queries.
KPIs:
1. Data Integration Time: The time taken to integrate and load the data into the knowledge graph.
2. Data Quality Score: The percentage of data that meets the data quality and data completeness criteria.
3. Data Security Score: The percentage of data that meets the data access, data encryption, and data anonymization criteria.
4. Data Analysis Time: The time taken to answer a data analysis query or request.
5. User Satisfaction Score: The user satisfaction score measured through regular feedback surveys and user interviews.
Management Considerations:
1. Data Ownership: Clearly defining the data ownership and data stewardship roles and responsibilities were critical to ensure the success of the project.
2. Data Stewardship: Establishing a data stewardship function that could oversee the data quality, data security, and data governance was essential.
3. Data Communication: Developing a data communication plan that could articulate the benefits, value, and impact of the knowledge graph to the stakeholders was crucial.
4. Data Training: Providing training and support to the users to enable them to use the knowledge graph effectively was important.
5. Data Roadmap: Developing a data roadmap that outlines the future data needs, requirements, and opportunities was necessary to ensure the sustainability and scalability of the knowledge graph.
Conclusion:
Knowledge graphs provide a powerful and flexible solution for data integration and analysis. By modeling, storing, and querying complex and interconnected data, knowledge graphs enable users to discover insights and relationships that are not easily discernible through traditional tabular databases. This case study highlights the consulting methodology, deliverables, implementation challenges, KPIs, and management considerations of engineering for data integration using knowledge graphs.
Citations:
1. Chen, H., Liu, M., u0026 Li, X. (2020). Knowledge graph-based data integration for e-commerce: A survey. ACM Transactions on Intelligent Systems and Technology, 11(3), 1-25.
2. Ding, L., u0026 Wang, J. (2020). Ontology-based data integration for knowledge graphs. In Proceedings of the 29th IEEE International Conference on Tools with Artificial Intelligence (pp. 1127-1134). IEEE.
3. Erezyilmaz, D., u0026 Tekinerdogan, B. (2020). Knowledge graph-based data integration for Industry 4.0: A systematic literature review. IEEE Access, 8, 82305-82331.
4. Huang, J., u0026 Liu, M. (2019). Data integration for knowledge graphs: A survey. IEEE Transactions on Knowledge and Data Engineering, 31(11), 2079-2093.
5. Noy, N. F., u0026 McGuinness, D. L. (2004). Defining N3 using RDF syntax and semantics (W3C Recommendation). World Wide Web Consortium.
Do you struggle with asking the right questions to drive results? Are you tired of spending endless hours sifting through irrelevant information, only to end up with subpar outcomes?Introducing our Knowledge Graphs and Architecture Modernization Knowledge Base – the ultimate tool to streamline your processes and achieve optimal results by urgency and scope.
With 1,541 carefully curated prioritized requirements, solutions, benefits, and case studies, our dataset is the most comprehensive and efficient resource on the market.
Don′t settle for mediocre results and wasting precious time and resources.
Our Knowledge Graphs and Architecture Modernization Knowledge Base stands out among our competitors and alternatives, specifically designed for professionals like you.
Featuring a user-friendly interface, this do-it-yourself and affordable product is suitable for any business seeking to modernize their knowledge architecture.
Our dataset offers a detailed overview of the product specifications and types, making it easy for you to find the perfect solution for your needs.
Don′t waste time on semi-related products – our Knowledge Graphs and Architecture Modernization Knowledge Base is tailored specifically for your industry.
But what truly sets us apart from the rest is the numerous benefits of our product.
Say goodbye to trial and error, and hello to streamlined processes and optimal results.
Our Knowledge Graphs and Architecture Modernization Knowledge Base has been extensively researched to ensure it meets the highest standards, providing you with accurate and effective solutions every time.
Concerned about the cost? Don′t be.
Our product offers an affordable alternative without compromising on quality.
We understand the importance of modernizing your knowledge graphs and architecture, which is why we have made it accessible to businesses of all sizes.
Still unsure? Let us break it down for you.
Our Knowledge Graphs and Architecture Modernization Knowledge Base will save you time, resources, and money, all while driving optimal results.
With our dataset, you′ll be able to ask the right questions and get the right solutions, enhancing your overall business performance.
Don′t hesitate any longer – join the numerous businesses already benefiting from our Knowledge Graphs and Architecture Modernization Knowledge Base.
Upgrade your knowledge infrastructure and stay ahead of your competitors with our comprehensive and efficient solution.
Experience the ease and effectiveness of modernizing your processes with us today!
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1541 prioritized Knowledge Graphs requirements. - Extensive coverage of 136 Knowledge Graphs topic scopes.
- In-depth analysis of 136 Knowledge Graphs step-by-step solutions, benefits, BHAGs.
- Detailed examination of 136 Knowledge Graphs case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Service Oriented Architecture, Modern Tech Systems, Business Process Redesign, Application Scaling, Data Modernization, Network Science, Data Virtualization Limitations, Data Security, Continuous Deployment, Predictive Maintenance, Smart Cities, Mobile Integration, Cloud Native Applications, Green Architecture, Infrastructure Transformation, Secure Software Development, Knowledge Graphs, Technology Modernization, Cloud Native Development, Internet Of Things, Microservices Architecture, Transition Roadmap, Game Theory, Accessibility Compliance, Cloud Computing, Expert Systems, Legacy System Risks, Linked Data, Application Development, Fractal Geometry, Digital Twins, Agile Contracts, Software Architect, Evolutionary Computation, API Integration, Mainframe To Cloud, Urban Planning, Agile Methodologies, Augmented Reality, Data Storytelling, User Experience Design, Enterprise Modernization, Software Architecture, 3D Modeling, Rule Based Systems, Hybrid IT, Test Driven Development, Data Engineering, Data Quality, Integration And Interoperability, Data Lake, Blockchain Technology, Data Virtualization Benefits, Data Visualization, Data Marketplace, Multi Tenant Architecture, Data Ethics, Data Science Culture, Data Pipeline, Data Science, Application Refactoring, Enterprise Architecture, Event Sourcing, Robotic Process Automation, Mainframe Modernization, Adaptive Computing, Neural Networks, Chaos Engineering, Continuous Integration, Data Catalog, Artificial Intelligence, Data Integration, Data Maturity, Network Redundancy, Behavior Driven Development, Virtual Reality, Renewable Energy, Sustainable Design, Event Driven Architecture, Swarm Intelligence, Smart Grids, Fuzzy Logic, Enterprise Architecture Stakeholders, Data Virtualization Use Cases, Network Modernization, Passive Design, Data Observability, Cloud Scalability, Data Fabric, BIM Integration, Finite Element Analysis, Data Journalism, Architecture Modernization, Cloud Migration, Data Analytics, Ontology Engineering, Serverless Architecture, DevOps Culture, Mainframe Cloud Computing, Data Streaming, Data Mesh, Data Architecture, Remote Monitoring, Performance Monitoring, Building Automation, Design Patterns, Deep Learning, Visual Design, Security Architecture, Enterprise Architecture Business Value, Infrastructure Design, Refactoring Code, Complex Systems, Infrastructure As Code, Domain Driven Design, Database Modernization, Building Information Modeling, Real Time Reporting, Historic Preservation, Hybrid Cloud, Reactive Systems, Service Modernization, Genetic Algorithms, Data Literacy, Resiliency Engineering, Semantic Web, Application Portability, Computational Design, Legacy System Migration, Natural Language Processing, Data Governance, Data Management, API Lifecycle Management, Legacy System Replacement, Future Applications, Data Warehousing
Knowledge Graphs Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Knowledge Graphs
Knowledge Graphs for data integration involve engineering efforts to model, map, and merge diverse data sources into a unified graph structure, ensuring interoperability and enabling sophisticated querying and analytics.
1. Improved data accuracy: Knowledge graphs ensure consistent data representation, reducing errors.
2. Better decision-making: Connected data leads to well-informed decisions and strategic planning.
3. Enhanced data discovery: Graphs reveal hidden relationships, enabling insights.
4. Faster data integration: Graphs enable easier connections between disparate data sources.
5. Scalability: Graph databases handle growing data volumes seamlessly.
6. Improved collaboration: Shared knowledge graphs foster teamwork and knowledge transfer.
7. Data governance: Graphs aid in implementing consistent data management policies.
CONTROL QUESTION: What would prompt engineering for data integration look like?
Big Hairy Audacious Goal (BHAG) for 10 years from now: A big, hairy, audacious goal (BHAG) for knowledge graphs in 10 years, regarding engineering for data integration, could be:
By 2032, enable seamless and universal data integration through a decentralized, interoperable, and autonomic knowledge graph fabric, powering real-time, context-aware, and privacy-preserving data access, analysis, and decision-making for individuals, organizations, and machines.
To achieve this BHAG, the following engineering challenges and opportunities should be explored:
1. Decentralized architecture: Design a decentralized, peer-to-peer knowledge graph fabric, utilizing distributed ledger technology or similar approaches for data integrity and consensus. Create self-organizing and self-healing networks for scalability, fault-tolerance, and resiliency.
2. Interoperability and standardization: Develop, adopt and maintain a comprehensive set of open standards for metadata representation, data exchange, and querying, focusing on extensibility, adaptability, and openness to accommodate diverse data sources and domains.
3. Autonomic data integration: Build data integration capabilities into the core architecture, allowing for automatic schema mapping, entity resolution, and data transformation, minimizing human intervention, and ensuring seamless and adaptive data integration.
4. Real-time data processing: Design low-latency and high-performance data processing and querying systems, incorporating advanced caching, indexing, and parallel processing techniques, optimized for diverse query patterns and use cases.
5. Context-awareness: Build context-aware data access and retrieval capabilities, incorporating machine learning models, reasoning engines, and natural language processing techniques, enabling accurate and relevant data delivery.
6. Privacy and security: Implement privacy-preserving data structures and access patterns, incorporating encryption, differential privacy, secure multi-party computation, and other techniques for maintaining data confidentiality. Ensure secure data access, distribution, and sharing through robust authentication, authorization, and auditing mechanisms.
7. Usability and education: Invest in user interface design, training, and education, focusing on simplicity, automation, and accessibility, empowering data professionals and non-experts to effectively harness the power of knowledge graphs for data-driven decision-making.
8. Governance and sustainability: Establish a robust governance framework for community collaboration, decision-making, and maintenance, balancing the needs of diverse stakeholders and ensuring the long-term vision, sustainability, and ethical alignment of the knowledge graph fabric.
This BHAG would transform data integration, enabling a universal data infrastructure, underpinning a myriad of data-driven applications and services, fostering innovation, efficiency, and collaboration for individuals, organizations, and machines.
Customer Testimonials:
"I`ve used several datasets in the past, but this one stands out for its completeness. It`s a valuable asset for anyone working with data analytics or machine learning."
"The creators of this dataset deserve a round of applause. The prioritized recommendations are a game-changer for anyone seeking actionable insights. It has quickly become an essential tool in my toolkit."
"I`ve recommended this dataset to all my colleagues. The prioritized recommendations are top-notch, and the attention to detail is commendable. It has become a trusted resource in our decision-making process."
Knowledge Graphs Case Study/Use Case example - How to use:
Case Study: Engineering for Data Integration using Knowledge GraphsSynopsis:
The client is a multinational retail corporation looking to integrate and analyze data from various internal and external sources such as customer data, sales data, and social media data. These data sources are typically stored in different formats, silos, and disparate systems, making it challenging for the client to gain a holistic and actionable understanding of their data. The client approached our consulting firm to develop a data integration and analysis solution that could address their challenges and meet their business objectives.
Consulting Methodology:
To address the client′s needs, we proposed a data integration solution based on knowledge graphs. Knowledge graphs are a type of graph database that can model, store, and query complex and interconnected data, enabling users to discover insights and relationships that are not easily discernible through traditional tabular databases.
Our consulting methodology involved the following steps:
1. Data Discovery: We conducted a thorough data discovery phase to identify the data sources, formats, and characteristics that needed to be integrated. This phase involved working closely with the client′s data owners and stakeholders to understand their data needs, requirements, and constraints.
2. Data Modeling: Based on the data discovery phase, we developed a data model that represented the entities, relationships, and attributes of the data. This phase involved designing a knowledge graph schema that could capture the semantics and structure of the data, enabling us to create a unified and consistent view of the data.
3. Data Integration: We implemented a data integration pipeline that extracted, transformed, and loaded the data from the disparate sources into the knowledge graph. This phase involved developing data connectors, data cleansing, and data mapping rules, and data quality checks.
4. Data Analysis: We developed a set of data analysis tools and visualizations that enabled the client to explore and query the knowledge graph. This phase involved developing SPARQL queries, graph algorithms, and data analytics dashboards.
5. Data Governance: We established a data governance framework that ensured the quality, security, and privacy of the data. This phase involved developing data access policies, data security plans, and data backup and recovery procedures.
Deliverables:
The following are the key deliverables of the project:
1. Data Discovery Report: A comprehensive report that describes the data sources, formats, and characteristics, along with the data quality and data completeness metrics.
2. Data Model Documentation: A detailed documentation that describes the knowledge graph schema, entities, relationships, and attributes.
3. Data Integration Pipeline: A set of data integration scripts, tools, and workflows that enable the automated extraction, transformation, and loading of the data.
4. Data Analysis Tools: A set of data analysis tools and visualizations that enable the exploration and querying of the knowledge graph.
5. Data Governance Framework: A set of data governance policies, procedures, and guidelines that ensure the quality, security, and privacy of the data.
Implementation Challenges:
1. Data Quality: Ensuring the quality, accuracy, and completeness of the data was a significant challenge. We had to develop and implement robust data cleansing, data matching, and data validation rules to ensure the data quality.
2. Data Security: Ensuring the security and privacy of the data was a critical challenge. We had to implement strict data access policies, data encryption, and data anonymization techniques to protect the data.
3. Data Complexity: Handling the complexity and diversity of the data was a significant challenge. We had to design a flexible and scalable data model that could accommodate the various data sources, formats, and relationships.
4. Data Scalability: Ensuring the scalability and performance of the knowledge graph was a critical challenge. We had to design a distributed and horizontally scalable architecture that could handle large volumes of data and queries.
KPIs:
1. Data Integration Time: The time taken to integrate and load the data into the knowledge graph.
2. Data Quality Score: The percentage of data that meets the data quality and data completeness criteria.
3. Data Security Score: The percentage of data that meets the data access, data encryption, and data anonymization criteria.
4. Data Analysis Time: The time taken to answer a data analysis query or request.
5. User Satisfaction Score: The user satisfaction score measured through regular feedback surveys and user interviews.
Management Considerations:
1. Data Ownership: Clearly defining the data ownership and data stewardship roles and responsibilities were critical to ensure the success of the project.
2. Data Stewardship: Establishing a data stewardship function that could oversee the data quality, data security, and data governance was essential.
3. Data Communication: Developing a data communication plan that could articulate the benefits, value, and impact of the knowledge graph to the stakeholders was crucial.
4. Data Training: Providing training and support to the users to enable them to use the knowledge graph effectively was important.
5. Data Roadmap: Developing a data roadmap that outlines the future data needs, requirements, and opportunities was necessary to ensure the sustainability and scalability of the knowledge graph.
Conclusion:
Knowledge graphs provide a powerful and flexible solution for data integration and analysis. By modeling, storing, and querying complex and interconnected data, knowledge graphs enable users to discover insights and relationships that are not easily discernible through traditional tabular databases. This case study highlights the consulting methodology, deliverables, implementation challenges, KPIs, and management considerations of engineering for data integration using knowledge graphs.
Citations:
1. Chen, H., Liu, M., u0026 Li, X. (2020). Knowledge graph-based data integration for e-commerce: A survey. ACM Transactions on Intelligent Systems and Technology, 11(3), 1-25.
2. Ding, L., u0026 Wang, J. (2020). Ontology-based data integration for knowledge graphs. In Proceedings of the 29th IEEE International Conference on Tools with Artificial Intelligence (pp. 1127-1134). IEEE.
3. Erezyilmaz, D., u0026 Tekinerdogan, B. (2020). Knowledge graph-based data integration for Industry 4.0: A systematic literature review. IEEE Access, 8, 82305-82331.
4. Huang, J., u0026 Liu, M. (2019). Data integration for knowledge graphs: A survey. IEEE Transactions on Knowledge and Data Engineering, 31(11), 2079-2093.
5. Noy, N. F., u0026 McGuinness, D. L. (2004). Defining N3 using RDF syntax and semantics (W3C Recommendation). World Wide Web Consortium.
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