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7 changes: 6 additions & 1 deletion src/content/docs/guide/index.mdx
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Expand Up @@ -12,7 +12,12 @@ Knowledge graphs are structured representations of knowledge, enabling the linki
## Importance and Applications of Knowledge Graphs
Knowledge graphs enhance data interoperability, facilitate advanced analytics, and support intelligent applications like virtual assistants and predictive modeling. They are used in industries such as healthcare, finance, and e-commerce.

## Overview of the Software Solutions
## Interoperability of Knowledge Graphs
Interoperability is crucial in the context of Knowledge graphs because it enables seamless data integration, exchange, and reuse across different systems, domains, and organizations. By adhering to established standards, such as those for representation (RDF, Labelled Property Graphs, etc.), or querying (SPARQL, Graph Query Language, etc.), interoperable KGs ensure that diverse datasets can be linked and understood in a unified way, reducing data silos, and enhancing discoverability. In the following report, we provide an overview and best practices on such standards and practices by focusing on metadata mapping, linking, and integration:

[https://zenodo.org/records/15780729](https://zenodo.org/records/15780729)

## Overview of the Software Solutions
**Virtuoso**: A cross-platform universal server that integrates web, file, and database server functionalities with native XML storage and universal data access middleware.

**Apache Jena**: A Java framework for building Semantic Web and Linked Data applications, offering tools for data handling, ontology management, and SPARQL querying.
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