virtuoso-opensource

slation of these graph words sequences, achieved through neural machine translation. Our evaluation on LUBM1 synthetic dataset shows 97% validation accuracy and 87.76% on a subset of DBpedia while demonstrating a noise-tolerance unavailable with rule-based reasoners. NMT4RDFS: https://github.com/Bassem-Makni/NMT4RDFS ... A human-generated review article with an emphasis on standards; with citations to summarize: "Why do we need SWRL and RIF in an OWL2 world?" [with SPARQL CONSTRUCT, SPIN, and now SHACL] https://answers.knowledgegraph.tech/t/why-do-we-need-swrl-an... https://spinrdf.org/spin-shacl.html : > From SPIN to SHACL In July 2017, the W3C has ratified the Shapes Constraint Language (SHACL) as an official W3C Recommendation. SHACL was strongly influenced by SPIN and can be regarded as its legitimate successor. This document explains how the two languages relate and shows how basically every SPIN feature has a direct equivalent in SHACL, while SHACL improves over the features explored by SPIN /? Shacl datalog https://www.google.com/search?q=%22shacl%22+%22datalog%22 "Reconciling SHACL and Ontologies: Semantics and Validation via Rewriting" (2023) https://scholar.google.com/scholar?q=Reconciling+SHACL+and+O... : > SHACL is used for expressing integrity constraints on complete data, while OWL allows inferring implicit facts from incomplete data; SHACL reasoners perform validation, while OWL reasoners do logical inference. Integrating these two tasks into one uniform approach is a relevant but challenging problem. "Well-founded Semantics for Recursive SHACL" (2022) [and datalog] https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A169... "SHACL Constraints with Inference Rules" (2019) https://arxiv.org/abs/1911.00598 https://scholar.google.com/scholar?cites=1685576975485159766... Datalog > Evaluation: https://en.wikipedia.org/wiki/Datalog#Evaluation ... VMware/ddlog: Differential datalog > Bottom-up: DDlog starts from a set of input facts and computes all possible derived facts by following user-defined rules, in a bottom-up fashion. In contrast, top-down engines are optimized to answer individual user queries without computing all possible facts ahead of time. For example, given a Datalog program that computes pairs of connected vertices in a graph, a bottom-up engine maintains the set of all such pairs. A top-down engine, on the other hand, is triggered by a user query to determine whether a pair of vertices is connected and handles the query by searching for a derivation chain back to ground facts. The bottom-up approach is preferable in applications where all derived facts must be computed ahead of time and in applications where the cost of initial computation is amortized across a large number of queries. From https://community.openlinksw.com/t/virtuoso-openlink-reasoni... https://github.com/openlink/virtuoso-opensource/issues/660 : > The Virtuoso built-in (rule sets) and custom inferencing and reasoning is backward chaining, where the inferred results are materialised at query runtime. This results in fewer physical triples having to exist in the database, saving space and ultimately cost of ownership, i.e., less physical resources are required, compared to forward chaining where the inferred data is pre-generated as physical triples, requiring more physical resources for hosting the data. FWIU it's called ShaclSail, and there's a NotifyingSail: org.eclipse.rdf4j.sail.shacl.ShaclSail: https://rdf4j.org/javadoc/3.2.0/org/eclipse/rdf4j/sail/shacl...