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SHACL 1.2 Rules

This document defines SHACL Rules, a language for describing the structure of RDF graphs.

SHACL 1.2 Rules is a specification that defines a language for describing the structure of RDF graphs and provides inferencing with the generation of new RDF data from a combination of rules and a base data graph. The document defines the syntax and semantics of rule-based inference, including basic patterns, recursion, filtering, negation, assignment, and importing rules. It also covers the evaluation of a rule set and the relationship between SHACL Rules and SPARQL.

Based on: SHACL 1.2 Rules · w3.org

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Vault-LD: an open spec for Markdown vaults as linked data

An open format for knowledge that enables two-way conversion between Markdown notes and RDF graphs.

Vault-LD is a specification for converting Markdown notes into RDF graphs, enabling the sharing of knowledge between humans and machines. It uses YAML frontmatter to map onto YAML-LD, allowing for round-trip conversions between Markdown and RDF. This approach enables business semantics to be integrated into existing wiki systems, and vice versa.

Based on: GitHub - The-Knowledge-Graph-Guys/vault-ld: Vault-LD: an open spec for Markdown vaults as linked data. YAML-LD frontmatter + a shared @context = an RDF knowledge graph. Prose for humans and LLMs, triples for machines. · github.com

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Ontology Alignment Evaluation Initiative::2025

Evaluation campaign for ontology matching technologies.

The Ontology Alignment Evaluation Initiative (OAEI) is a yearly evaluation campaign that assesses the performance of ontology matching systems. The 2025 campaign includes various tracks, such as T-Box/Schema matching, Multifarm, and Knowledge Graph Track, which evaluate different aspects of ontology alignment. The goal is to provide a comprehensive assessment of ontology matching technologies.

Based on: Ontology Alignment Evaluation Initiative::2025 · oaei.ontologymatching.org

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SHACLens: a visualization workflow for SHACL violation exploration in knowledge graphs

A visualization workflow for exploring SHACL violations in large knowledge graphs.

The paper presents SHACLens, an interactive visualization workflow that links ontology, instance data, and violation reports across multiple coordinated views. The workflow is designed to help analysts identify co-occurring errors and their likely upstream causes. An evaluation of the workflow using a transcriptomics dataset showed that it efficiently surfaced repeated sets of errors due to missing objects and schema inconsistencies.

Based on: Frontiers | SHACLens: a visualization workflow for SHACL violation exploration in knowledge graphs · frontiersin.org

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How the Open Knowledge Format can improve data sharing

Google introduces the Open Knowledge Format (OKF) to standardize knowledge representation for AI systems.

The Open Knowledge Format is an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. It represents knowledge as a directory of markdown files with YAML frontmatter and allows for standardized documentation and data sharing across teams and organizations. The OKF aims to solve the problem of fragmented context landscapes by providing a vendor-neutral, agent- and human-friendly standard for representing metadata, context, and curated knowledge.

Based on: How the Open Knowledge Format can improve data sharing | Google Cloud Blog · cloud.google.com

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Intentional Arrangement

A Substack publication by Jessica Talisman, MLS, on information architecture and semantic engineering.

The resource covers topics such as ontologies, knowledge graphs, AI, and semantic interoperability. It includes essays and articles on various aspects of digital knowledge ecosystems and their organization. The author shares her expertise in information architecture and semantic engineering, with a focus on intentional arrangement and its applications.

Based on: Intentional Arrangement | Jessica Talisman, MLS | Substack · jessicatalisman.substack.com

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DuckDB RDF Extension

A DuckDB extension to read and write RDF files directly.

This extension allows reading and writing RDF files in DuckDB, supporting various formats such as Turtle, NTriples, NQuads, and TriG. It uses the SERD library for parsing and writing RDF data. The extension also supports WebAssembly (WASM) builds and compression formats like Gzip and Zst.

Based on: GitHub - nonodename/duck_rdf: RDF file extension for DuckDB. Reads and writes supported · github.com

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GitHub - DataTreehouse/maplib

A high-performance RDF knowledge graph construction library in Python.

maplib is a Rust-based library for constructing and querying knowledge graphs. It supports SHACL validation, SPARQL and Datalog queries, and can read knowledge graphs from various serialization formats. The library allows users to leverage their existing skills with Pandas or Polars to extract and wrangle data before building a knowledge graph.

Based on: GitHub - DataTreehouse/maplib · github.com

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The Ontology Layer of Design

An article discussing the importance of ontology in design, particularly in the context of AI and large language models.

The author argues that designers must adapt to the changing landscape of AI by defining a product's ontology, which is the basic structure of objects, relationships, and concepts. This involves understanding how to spot good ontologies from bad ones and leveraging language as a way of shaping the worlds our new AI tools will inhabit.

Based on: The Ontology Layer of Design · figureandground.substack.com

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Converting from OWL to SHACL, Part I

An article discussing the principles and benefits of converting from OWL to SHACL.

The author explores the reasons for converting from OWL to SHACL, including improved validation, support for reification, and better alignment with tabular data sources. The article also delves into design considerations, such as the differences between rdfs:subClassOf and sh:node in a NodeShape.

Based on: Converting from OWL to SHACL, Part I · ontologist.substack.com

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Skill Retrieval Augmentation for Agentic AI

A paper proposing a paradigm for dynamically retrieving and incorporating skills in large language models.

The authors introduce Skill Retrieval Augmentation (SRA), a method for agents to retrieve relevant skills from external corpora on demand. They construct a benchmark, SRA-Bench, to evaluate the full SRA pipeline. The paper shows that retrieval-based skill augmentation can improve agent performance and highlights the need for more efficient skill incorporation.

Based on: Skill Retrieval Augmentation for Agentic AI · arXiv

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Separating Semantic Competition from Context Length in RAG Reading

Paper on evaluating Retrieval-Augmented Generation (RAG) systems.

The authors introduce a matched-control protocol to isolate the effect of semantic competition on RAG reading performance. They apply this protocol to two compact open models on SQuAD and report improvements in F1, answer inclusion, and exact match scores. The results suggest that the competition effect is distinct from context length.

Based on: Separating Semantic Competition from Context Length in RAG Reading · arXiv