🚀 Virtuoso Open Source 7.2.15 is out!

A high-performance, multi-model DBMS supporting SQL, SPARQL & GraphQL. Also a Web App Server (HTTP, WebDAV, OpenAPI) & MCP-accessible via ODBC, JDBC, pyODBC & DotNet.

🔗 https://community.openlinksw.com/t/virtuoso-open-source-7-2-15-release-notes/5006

#VirtuosoRDBMS #OpenSource

Here's a link to the Claude Desktop artifact.

https://claude.ai/public/artifacts/19f792bc-867a-4ef7-910a-4696bdeec7d4

Here's a link to the MCP server for JDBC repo.

https://github.com/OpenLinkSoftware/mcp-jdbc-server/

An example of what’s now possible—thanks to our (@openlink) sophisticated MCP Server for JDBC, Oracle’s native JDBC Driver, and Anthropic's Claude Desktop as the MCP client:

A dashboard generated from Oracle’s HR database using the following natural language prompt:

Using JDBC with the following connection details:
URL: jdbc:oracle:thin:@oracle-host:1521:XE
Credentials: UID=hr, PWD=openlink
Write a query to produce a dashboard summarizing job history by employee.

#MCP #HowTo #JDBC #Oracle

Introducing the OpenLink AI Layer (OPAL) MCP Server

1/ This MCP Server is a game-changer for loosely coupling AI Agents & Data Spaces (databases, knowledge graphs, filesystems) coupling.

#MCP #VirtuosoRDBMS #OPAL #AI

We've just published a community post showcasing the power of the OpenLink AI Layer (OPAL) in action. It includes Cello, an AI Agent prototype built using the EU's Virtuoso-based SPARQL endpoint.

https://community.openlinksw.com/t/practical-genai-in-action-demonstrating-the-openlink-ai-layer-opal/4912

#UseCase #OPAL #GenAI #VirtuosoRDBMS #HowTo

Here’s another dashboard variant built using Claude Desktop as the MCP client and the virtual Northwind Knowledge Graph, derived from underlying Northwind tables. This edition adds both a Company and Employee dimension:

https://claude.site/artifacts/7c4f5e75-d33a-4902-8a83-ed095de146a2

#MCP #HowTo #KnowledgeGraph #Database #SQL #SPARQL

This week’s edition of my @linkedin newsletter shines a light on the impact of the Model Context Protocol (MCP) across multiple computing eras—leading right up to today.

We’ve arrived at a moment where loose coupling principles are cool (and super useful) again.

Naturally, there are some live demo nuggets in the post to help bring it all to life:

🔗 https://www.linkedin.com/pulse/model-context-protocol-bridging-generational-computing-idehen-qdtce/

#AI #GenAI #MCP #KnowledgeGraph #LinkedData #SemanticWeb #SQL #SPARQL #DBMS #DataConnectivity

The Model Context Protocol acts as a universal translator between LLMs and data sources, eliminating complex platform-specific requirements. Our new open source MCP server for ODBC (mcp-odbc-server) enables seamless integration of any ODBC-accessible data into RAG pipelines.

Read more in my latest newsletter!

https://www.linkedin.com/pulse/whats-model-context-protocol-why-important-kingsley-uyi-idehen-zy8ue

#MCP #ODBC #SPARQL #SQL #LinkedData #DBMS #DataConnectivity #AI #GenAI #Agentic

@medium,

I still don't have an email message or any sense of the location of my export archive. We are now going on 48 hrs since my initial request.

/cc @evhead @biz

@medium I've requested a data export over 24 hrs ago. I am still awaiting email notification for the download link.

How can Mastodon be more visible to Bluesky? Bridgy Fed makes it possible but is tricky to use, so I created an interactive cheatsheet: https://jonudell.info/fedi-bsky-interactive-cheatsheet/

https://thenewstack.io/making-the-fediverse-more-accessible-with-claude-3-7-sonnet/

#fediverse #bluesky #bridgy

Test 2:
What is https://x.ai/blog/grok-3#this about? Provide your response in JSON-LD comprising the following:

1. Blog/Article Description
2. FAQ
3. Glossary
4. HowTo
5. Relevant Product/Service, Offers, Features descriptions

You should set @base to the article url, and incorporate relevant cross-references from sources like DBpedia and Wikipedia. You can also use Wikidata if you are absolutely certain of the identifiers you choose.

Result: https://x.com/i/grok/share/dULAK5xSxlA60lKv7opfqP1oX

Attached is a Grok-based screencast showcasing how note-taking is evolving, now that LLMs eliminate the tedium of crafting machine-computable notes in RDF using any of its associated notations (e.g., JSON-LD).

LLMs are the “Easy Button” for understanding and harnessing the powerful benefits envisioned by The Semantic Web Project.

If you closely examine the GenAI disruption in software, you’ll notice that all roads lead to better note-taking—progressively enriching knowledge repositories that manifest as knowledge graphs constructed using Linked Data Principles.

#NoteTaking #SemanticWeb #AI #GenAI #LinkedData #KnowledgeGraph #LLMs

Here’s a DataTwingler AI Agent configuration in Markdown, designed for interacting with UniProt’s massive Knowledge Graph, which consists of 210 billion machine-computable statements of fact, meticulously curated by domain experts. github.com/OpenLinkSoft...

Here’s a DataTwingler AI Agent configuration in Markdown, designed for interacting with UniProt’s massive Knowledge Graph, which consists of 210 billion machine-computable statements of fact, meticulously curated by domain experts.

https://github.com/OpenLinkSoftware/Assistants/blob/main/data-twingler-uniprot.md

This AI Agent enables scalable, explainable, and verifiable AI interactions with UniProt’s Knowledge Graph, seamlessly integrating LLMs with high-quality biomedical data via its #VirtuosoRDBMS-based SPARQL query services endpoint.

Links:

* https://sparql.uniprot.org/.well-known/void -- Knowledge Graph Stats

* https://sparql.uniprot.org/sparql -- Query Service Endpoint

* https://github.com/sib-swiss/sparql-training/tree/master/uniprot -- SPARQL Querying Tutorial

* https://sparql.uniprot.org/.well-known/sparql-examples/ -- Sample Queries Collection

#VirtuosoRDBMS #RDF #KnowledgeGraph #LinkedData #SPARQL

UniProt’s latest publicly accessible Knowledge Graph now contains 210,122,358,019 RDF triples—powered by our (@openlink) #VirtuosoRDBMS open-source edition, live on the Web and available to AI Agents, Applications and Services across life sciences, genomics, bioinformatics, molecular biology, and related fields.

Utility?

210,122,358,019 factual statements, curated by domain experts, in subject, predicate, and object sentence form—all accessible via a live SPARQL Query Service endpoint.

Here’s a DataTwingler AI Agent configuration in Markdown, designed for interacting with UniProt’s massive Knowledge Graph, which consists of 210 billion machine-computable statements of fact, meticulously curated by domain experts.

https://github.com/OpenLinkSoftware/Assistants/blob/main/data-twingler-uniprot.md

This AI Agent enables scalable, explainable, and verifiable AI interactions with UniProt’s Knowledge Graph, seamlessly integrating LLMs with high-quality biomedical data via its #VirtuosoRDBMS-based SPARQL query services endpoint.

Links:

* https://sparql.uniprot.org/.well-known/void -- Knowledge Graph Stats

* https://sparql.uniprot.org/sparql -- Query Service Endpoint

* https://github.com/sib-swiss/sparql-training/tree/master/uniprot -- SPARQL Querying Tutorial

* https://sparql.uniprot.org/.well-known/sparql-examples/ -- Sample Queries Collection

#VirtuosoRDBMS #RDF #KnowledgeGraph #LinkedData #SPARQL