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D.2.11. Knowledge Graph Storage & Retrieval

Semantic data storage managed by the EKG/Platform.

The capability Knowledge Graph Storage & Retrieval (D.2.11) is part of the capability area Technology Execution in the Technology Pillar.

Semantic data storage managed by the EKG/Platform.

The Knowledge Graph Storage & Retrieval capability encompasses the effective management of data storage and efficient retrieval within the distributed architecture of the Enterprise Knowledge Graph (EKG). It involves implementing technologies, processes, and infrastructure to manage storage and retrieval at a local level within different components or nodes of the distributed EKG platform, potentially across different lines of business, different legal entities, teams or even across an ecosystem including third parties.

Key aspects of this capability, on the storage side, include:

  1. Local Storage Management: Managing data storage at a local level within individual components or nodes of the distributed EKG. This involves implementing scalable storage solutions that can handle the volume, velocity, and variety of data specific to each component.

  2. Data Modeling and Storage Structure: Designing and structuring the knowledge graph data model at a local level to accurately represent entities, relationships, and their properties. While storage structure is of lesser importance in a semantic technology context, it is still essential to ensure efficient organization and representation of data within the local storage of each component.

  3. Local Data Ingestion and Integration: Establishing mechanisms to ingest and integrate data at a local level within each component of the EKG. This includes local data extraction, transformation, and loading (ETL) processes, as well as integration interfaces specific to each component to seamlessly incorporate diverse data into the local storage.

  4. Indexing and Local Storage Optimization: Implementing indexing techniques and optimizing the storage structure at a local level to enable efficient data retrieval within each component. This includes creating indexes on relevant attributes and properties specific to each local storage to enhance query performance and optimize storage efficiency.

And on the retrieval side:

  1. Querying and Retrieval Optimization: Enabling fast and accurate retrieval of information within each component of the distributed EKG. This involves implementing optimized query mechanisms, query languages, or APIs specific to each component's storage technology to provide efficient retrieval of relevant information.

  2. Inter-Component Data Access and Retrieval: Establishing mechanisms for inter-component data access and retrieval within the distributed EKG. This includes ensuring seamless communication and data exchange between different components to support cross-component querying and retrieval of information.

  3. SPARQL Query Support: Ensuring the capability to query each node of the EKG architecture using SPARQL. SPARQL is a query language specifically designed for semantic knowledge graphs, allowing expressive querying with consideration for ontologies. Supporting SPARQL queries within the distributed EKG enables complex use cases that leverage the full expressivity of ontologies and semantic reasoning.

By managing data storage and retrieval at a local level within the distributed architecture of the EKG, organizations can ensure efficient utilization of storage resources and optimize retrieval mechanisms for each component. This distributed approach allows for scalability, fault tolerance, and performance optimization in large-scale EKG deployments, while SPARQL support enables powerful and ontology-aware querying within the EKG.

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Work in progress

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Work in progress. Describe the five levels of maturity for this Capability.

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Work in progress, describe how this capability is possibly being delivered today in a non-EKG context and optionally what the issues are that EKG could or should improve

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Work in progress, describe how this capability would be delivered or supported using an EKG approach, making the link to the "how" i.e. the EKG/Method.

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Work in progress, list examples of use cases that contribute to this capability, making the link to use cases in the catalog at https://catalog.ekgf.org/use-case/..

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