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Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
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Sounds awesome--feel free to get in touch with us (the authors of this paper) and share your progress. We have a similar single-node Datalog engine in Rust, it would be cool to benchmark your results against parallel Ascent (https://github.com/s-arash/ascent).
https://github.com/topics/datalog?l=rust ... Cozo, Crepe
Crepe: https://github.com/ekzhang/crepe :
> Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs.
Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb :
> Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
Looks like roxi supports n3, which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi
FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html :
> simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed).
ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15
Fast CUDA hashmaps
Gdlog is built on CuCollections.
GPU HashMap libs to benchmark: Warpcore, CuCollections,
https://github.com/NVIDIA/cuCollections
https://github.com/NVIDIA/cccl
https://github.com/sleeepyjack/warpcore
/? Rocm HashMap
DeMoriarty/DOKsparse:
https://github.com/topics/datalog?l=rust ... Cozo, Crepe
Crepe: https://github.com/ekzhang/crepe :
> Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs.
Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb :
> Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
Looks like roxi supports n3, which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi
FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html :
> simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed).
ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15
Fast CUDA hashmaps
Gdlog is built on CuCollections.
GPU HashMap libs to benchmark: Warpcore, CuCollections,
https://github.com/NVIDIA/cuCollections
https://github.com/NVIDIA/cccl
https://github.com/sleeepyjack/warpcore
/? Rocm HashMap
DeMoriarty/DOKsparse:
https://github.com/topics/datalog?l=rust ... Cozo, Crepe
Crepe: https://github.com/ekzhang/crepe :
> Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs.
Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb :
> Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
Looks like roxi supports n3, which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi
FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html :
> simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed).
ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15
Fast CUDA hashmaps
Gdlog is built on CuCollections.
GPU HashMap libs to benchmark: Warpcore, CuCollections,
https://github.com/NVIDIA/cuCollections
https://github.com/NVIDIA/cccl
https://github.com/sleeepyjack/warpcore
/? Rocm HashMap
DeMoriarty/DOKsparse:
https://github.com/topics/datalog?l=rust ... Cozo, Crepe
Crepe: https://github.com/ekzhang/crepe :
> Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs.
Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb :
> Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
Looks like roxi supports n3, which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi
FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html :
> simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed).
ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15
Fast CUDA hashmaps
Gdlog is built on CuCollections.
GPU HashMap libs to benchmark: Warpcore, CuCollections,
https://github.com/NVIDIA/cuCollections
https://github.com/NVIDIA/cccl
https://github.com/sleeepyjack/warpcore
/? Rocm HashMap
DeMoriarty/DOKsparse:
https://github.com/topics/datalog?l=rust ... Cozo, Crepe
Crepe: https://github.com/ekzhang/crepe :
> Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs.
Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb :
> Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
Looks like roxi supports n3, which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi
FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html :
> simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed).
ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15
Fast CUDA hashmaps
Gdlog is built on CuCollections.
GPU HashMap libs to benchmark: Warpcore, CuCollections,
https://github.com/NVIDIA/cuCollections
https://github.com/NVIDIA/cccl
https://github.com/sleeepyjack/warpcore
/? Rocm HashMap
DeMoriarty/DOKsparse:
https://github.com/topics/datalog?l=rust ... Cozo, Crepe
Crepe: https://github.com/ekzhang/crepe :
> Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs.
Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb :
> Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
Looks like roxi supports n3, which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi
FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html :
> simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed).
ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15
Fast CUDA hashmaps
Gdlog is built on CuCollections.
GPU HashMap libs to benchmark: Warpcore, CuCollections,
https://github.com/NVIDIA/cuCollections
https://github.com/NVIDIA/cccl
https://github.com/sleeepyjack/warpcore
/? Rocm HashMap
DeMoriarty/DOKsparse:
https://github.com/topics/datalog?l=rust ... Cozo, Crepe
Crepe: https://github.com/ekzhang/crepe :
> Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs.
Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb :
> Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
Looks like roxi supports n3, which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi
FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html :
> simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed).
ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15
Fast CUDA hashmaps
Gdlog is built on CuCollections.
GPU HashMap libs to benchmark: Warpcore, CuCollections,
https://github.com/NVIDIA/cuCollections
https://github.com/NVIDIA/cccl
https://github.com/sleeepyjack/warpcore
/? Rocm HashMap
DeMoriarty/DOKsparse:
https://github.com/topics/swisstable
rust-lang/hashbrown: https://github.com/rust-lang/hashbrown
CuPy has array but not yet hashmaps, or (GPU) SIMD FWICS?
NumPy does SIMD:
https://github.com/xtensor-stack/xsimd
GH topics > HashMap:
https://www.researchgate.net/figure/Parameter-Settings-of-th...
"Deep learning for noise-tolerant RDFS reasoning" (2018) > NMT4RDFS: http://www.semantic-web-journal.net/content/deep-learning-no... :
> This paper documents a novel approach that extends noise-tolerance in the SW to full RDFS reasoning. Our embedding technique— that is tailored for RDFS reasoning— consists of layering RDF graphs and encoding them in the form of 3D adjacency matrices where each layer layout forms a graph word. Each input graph and its entailments are then represented as sequences of graph words, and RDFS inference can be formulated as translation 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://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...
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