Efficient Estimation of Word Representations in Vector Space

Proposes two new architectures for learning continuous word vector representations efficiently from very large datasets.

The paper proposes two novel architectures for computing continuous vector representations of words from very large datasets. Representation quality is measured on a word similarity task and compared against prior neural-network-based techniques. The authors report large accuracy gains at much lower computational cost, learning high-quality word vectors from a 1.6 billion word dataset in under a day. These vectors also achieve state-of-the-art results on a test set measuring syntactic and semantic word similarities.

Based on: Efficient Estimation of Word Representations in Vector Space · International Conference on Learning Representations

GloVe: Global Vectors for Word Representation

Introduces GloVe, a global log-bilinear regression model unifying matrix factorization and context-window methods for word vectors.

Prior word-vector methods captured semantic and syntactic regularities through vector arithmetic, but why these regularities arose was unclear. The authors make explicit the properties needed for such structure to emerge and propose GloVe, a global log-bilinear regression model combining matrix factorization with local context-window approaches. It trains only on nonzero entries of a word-word co-occurrence matrix. The resulting vectors score 75% on a word analogy task and outperform related models on similarity and named entity recognition.

Based on: GloVe: Global Vectors for Word Representation · Conference on Empirical Methods in Natural Language Processing

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin et al. present BERT, a bidirectional Transformer pretraining method that set new state-of-the-art results on eleven NLP tasks.

BERT pre-trains deep bidirectional representations by jointly conditioning on left and right context in every layer, unlike prior left-to-right language models. A single pretrained BERT model can be fine-tuned with one extra output layer for many tasks, pushing GLUE to 80.5, MultiNLI accuracy to 86.7%, and SQuAD v1.1 F1 to 93.2 — new state-of-the-art results across eleven NLP benchmarks.

Based on: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding · North American Chapter of the Association for Computational Linguistics

Attention is All you Need

Vaswani et al. propose the Transformer, an architecture built solely on attention, replacing recurrence and convolution.

The Transformer dispenses with recurrent and convolutional layers entirely, relying only on attention mechanisms. It is more parallelizable and faster to train than prior encoder-decoder models, reaching 28.4 BLEU on WMT 2014 English-to-German and a new state-of-the-art 41.8 BLEU on English-to-French after 3.5 days of training on eight GPUs.

Based on: Attention is All you Need · Neural Information Processing Systems

Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!

Paper on improving question answering systems with large language models using ontologies.

This paper presents an approach to improve the accuracy of question answering systems with large language models by leveraging ontologies. The authors propose a method that consists of ontology-based query check and LLM repair, which increases the overall accuracy to 72%. The results provide further evidence that investing knowledge graphs, namely the ontology, provides higher accuracy for LLM-powered question-answering systems.

Based on: Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue! · arxiv.org

• Knowledge Graphs
• Large Language Models
• Ontology & Taxonomy
• Semantic Interoperability

oxigraph

A Rust-based graph database implementing the SPARQL standard.

Oxigraph is a graph database written in Rust that implements the SPARQL standard. It provides a compliant, safe, and fast graph database based on the RocksDB key-value store. Oxigraph also includes utility functions for reading, writing, and processing RDF files.

Based on: oxigraph · git.nextgraph.org

• Knowledge Graphs
• Graph Databases
• Content Engineering
• Semantic Interoperability

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

• Knowledge Graphs
• Ontology & Taxonomy
• Schemas & Shapes
• Semantic Interoperability

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

• Knowledge Graphs
• Structured Content
• Content Engineering
• AI Agents

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

• Ontology & Taxonomy
• Semantic Interoperability
• Content Engineering

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

• Knowledge Graphs
• Schemas & Shapes
• Ontology & Taxonomy
• Content Engineering

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

• Knowledge Graphs
• Structured Content
• Content Engineering
• AI Agents

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

• Knowledge Graphs
• Ontology & Taxonomy
• Semantic Interoperability
• Content Engineering