Highlightdbt Labs

Consistent dbt structure frees teams to decide on hard problems

dbt Labs guide arguing that files, folders, naming, and patterns reduce decision fatigue in collaborative projects.

The guide says analytics engineering is collaboration at scale under limited decision bandwidth, so projects need consistent norms for folders, names, and patterns. Structure is how transformations are labeled, grouped, and combined. One shared principle is moving data from source-conformed shapes toward business-conformed ones, with consistency valued over any single style.

Based on: How we structure our dbt projects | dbt Developer Hub · dbt Labs

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dbt dimensions add categorical and time attributes to semantic models

dbt documentation for non-aggregatable Semantic Layer dimensions: name, type, optional expr and label.

Dimensions are non-aggregatable attributes in a semantic model—features that categorize data and typically appear in SQL GROUP BY. Each needs a unique name within the model and a type of categorical or time; optional expr and label control column mapping and downstream display.

Based on: Dimensions | dbt Developer Hub · dbt Labs

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dbt entities are typed join keys that link semantic models

dbt docs define entities as business concepts used as join keys—primary, unique, foreign, or natural—in the Semantic Layer.

Entities represent real-world concepts such as customers or transactions and act as join keys across semantic models in dbt’s Semantic Layer (v1.12+). Required parameters are name and type; types are primary, unique, foreign, and natural. Names must be unique within a model and may use expr; entities can also be used as dimensions.

Based on: Entities | dbt Developer Hub · dbt Labs

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dbt semantic models are MetricFlow graph nodes tied to DAG models

dbt docs explain semantic models as YAML-configured nodes and entities that MetricFlow uses for the Semantic Layer.

In dbt v1.12+, semantic models are the foundation for MetricFlow’s Semantic Layer: nodes linked by entities in a semantic graph, annotated on dbt models for metric use. Each DAG model maps to one semantic_model YAML block (or Apache Ossie docs); components include name, time dimension, and entities.

Based on: Semantic models | dbt Developer Hub · dbt Labs

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Unity Catalog: one governance layer under Databricks data and AI assets

Databricks docs on Unity Catalog—access control, lineage, and auditing for data and AI via a three-level object model.

Unity Catalog is Databricks’ unified governance layer for data and AI. When enabled, it enforces access control, tracks lineage, and logs activity under workspace interactions. Assets are securable objects in a catalog.schema.object namespace; tables and volumes may be managed or external. An open-source implementation also exists.

Based on: What is Unity Catalog? | Databricks on AWS · Databricks

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MetricFlow: YAML metrics and a semantic graph that generate the SQL

dbt Labs guide introducing MetricFlow as the engine behind the Semantic Layer for defining and querying metrics.

MetricFlow powers dbt’s Semantic Layer with opinionated abstractions for defining and managing metric logic. It builds SQL from YAML (and, from dbt v1.12, Ossie documents), linking semantic models and metrics in a semantic graph. It works with listed warehouses and dbt 1.6+, under Apache 2.0.

Based on: About MetricFlow | dbt Developer Hub · dbt Labs

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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

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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

Highlightoaei.ontologymatching.org

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

Highlightjessicatalisman.substack.com

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

Highlightfigureandground.substack.com

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