Since I'm not at #ISWC2025, it's more easy for me to speak up. There are ginormous issues with QLever and the associated Sparqloscope benchmark by Hannah Bast and colleagues. The main results table already shows something that's too good to be true. And while I'm sure that table is technically true, the tacit implication that this table has any bearing on real-world performance, is false. QLever is faster than the state of the art… at COUNTing. That's it. QLever can count faster. The implication is that this would mean QLever can also produce results faster. Yet we have zero reason to assume it can—until there's proof. In the real world, query engines rarely compute all results at once. They stream those results. The Sparqloscope benchmark is designed to trick established query engines into actually producing the result set and counting items. And you know what? Sometimes, the established engines are even faster at that than QLever, which seems to be purposefully designed to count fast. Yes—I'm sure QLever is a fast counter. But what on earth does that have to do with real-world streaming query performance? And did I mention that Virtuoso supports SPARQL UPDATE? How can you tell, just from the table? Well, Virtuoso is faster than QLever for just about anything that doesn't rely on pure counting. QLever does “Regex: prefix” or “Filter: English literals” in the ridiculously fast 0.01s? The only rational explanation is that it has a great structure for specifically this kind of counting (again, not results, just count). But Virtuoso is faster for “strbefore”? Well, there you see the real QLever performance when it cannot just count. And only one of those strategies has impact on the real world. So what if a query engine can count faster than any other to 65,099,859,287 (real result BTW). Call me when you can produce 65,099,859,287 results faster, then we'll have something to talk about. In the first place, it's a major failure of peer review that a benchmark based on COUNT was accepted. And I'd be very happy to be proven wrong: let's release the benchmark results for all engines, but without COUNT this time. Then we'll continue the conversation. https://lnkd.in/eT5XrR2k | 19 comments on LinkedIn

DuckDB community extension that adds support for SQL/PGQ and graph algorithms

Spacetime and information both have the basics of a geometry

pretty stoked that our paper on the vectorised #SPARQL execution engine in Stardog got accepted to the GRADES-NDA workshop at #SIGMOD2025. It's a cool piece of work describing how modern vectorised join algorithms, more widely known in the SQL world, make graph query processing much more efficient. Talking of up to an order of magnitude difference when it comes to analytical queries and large-scale joins. Hugely proud of my brilliant co-authors Simon Grätzer (the lead engineer on the BARQ project) and Lars Heling. It was their idea to do this work, and I couldn't be more proud that it worked out in the end. The preprint is now on arXiv: https://lnkd.in/eqXtVMqe

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, despite documented demand, ...

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The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI

PDF | BIFROST is a novel query engine for graph databases that supports high-fidelity data modeling on arbitrary and evolving graph topologies. It... | Find, read and cite all the research you need on ResearchGate

This week, I thoroughly enjoyed attending the 21st Extended #SemanticWeb Conference! Here’s a summary of the contributions presented at the conference about…

PDF | The Digital Thread is a system that connects different phases of the product lifecycle and the related data across one or more companies in the... | Find, read and cite all the research you need on ResearchGate

Natural language processing made easy

Seamlessy implement information extraction pipeline with LangChain and Neo4j

Working on a LangChain template that adds a custom graph conversational memory to the Neo4j Cypher chain, which uses LLMs to generate Cypher statements. This…