HighlightCube Dev

Cube’s semantic layer as the shared surface for BI, embed, and AI agents

Cube docs: agentic analytics on an open-source semantic layer for internal BI and embedded analytics.

Cube is positioned as an agentic analytics platform on a semantic layer for internal BI and embedded analytics. Cube Core centralizes metrics, joins, access rules, and caching upstream of BI tools, apps, and agents. Agents query via Semantic SQL through the semantic layer runtime with validation and access policies, not by writing free-form warehouse SQL.

Based on: Introduction - Cube Documentation · Cube Dev

Highlightdbt Labs

Centralize metrics in dbt so every BI tool reads the same definitions

Overview of the dbt Semantic Layer: MetricFlow-backed metrics defined once in the modeling layer for consistent downstream use.

The dbt Semantic Layer, powered by MetricFlow, lets teams define metrics on existing models, handle joins, and expose those definitions to downstream tools. Moving metrics out of BI into the modeling layer keeps business units on the same definitions; a change in dbt refreshes everywhere the metric is invoked. Access permissions control who can use it; Starter or Enterprise-tier accounts are required.

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

Highlightdbt Labs

dbt model contracts: build-time guarantees on column shape before consumers break

dbt Labs docs on model contracts—upfront shape guarantees verified at build, plus governance caveats and support limits.

A dbt model contract is a set of upfront guarantees on the shape of a model’s dataset. At build time dbt checks that the transformation matches the contract or fails. Governance features add trust but can harden rollbacks if adopted too early, and contracts apply only to supported SQL materializations—not Python models, ephemeral models, or several other resource types.

Based on: Model contracts | dbt Developer Hub · dbt Labs

HighlightSalesforce

Salesforce describe API: pull object fields, URLs, and relationships as metadata

REST reference for sObject Describe—full object metadata via GET, with conditional If-Modified-Since headers.

The sObject Describe resource returns complete metadata for a named Salesforce object, including fields, URLs, and child relationships. It is a GET endpoint returning JSON or XML, authenticated with a Bearer token. Optional If-Modified-Since and If-Unmodified-Since headers support conditional responses, including 304 when nothing changed.

Based on: sObject Describe | Reference | REST API Developer Guide | Salesforce Developers · Salesforce

HighlightIEEE Communications Magazine

Proof of Unlearning for Semantic Knowledge Bases in Large Language Models-Enabled Semantic Communication

A framework for efficiently and verifiably updating large language model-enabled semantic knowledge bases.

The authors propose a proof-of-unlearning framework for updating large language models (LLMs) used in semantic knowledge bases. The framework tracks the evolution of unlearning by measuring drifts in the LoRA adapter subspace. Experimental results demonstrate its effectiveness. This work addresses the challenge of removing outdated, malicious, or privacy-sensitive content from LLMs without retraining.

Based on: Proof of Unlearning for Semantic Knowledge Bases in Large Language Models-Enabled Semantic Communication · IEEE Communications Magazine

HighlightLNCS / ISWC

Ontology Repositories and Semantic Artefact Catalogues with OntoPortal Technology

A paper on the OntoPortal Alliance consortium's platforms for ontology management.

The OntoPortal Alliance consortium provides common platforms for receiving, hosting, serving, aligning, and enabling reuse of ontologies and semantic artefacts.,These platforms make ontologies FAIR (Findable, Accessible, Interoperable, Reusable).,They address the growing need for governance of exploding number of semantic artefacts in science.

Based on: Ontology Repositories and Semantic Artefact Catalogues with OntoPortal Technology · LNCS / ISWC

HighlightData Intelligence (MIT Press)

The W3C Data Catalog Vocabulary Version 2: Rationale Design Principles and Uptake

W3C RDF vocabulary for data catalog interoperability.

DCAT v2 addresses gaps identified through implementation experience across communities, building on the 2014 W3C Recommendation. It supports wide adoption, especially with FAIR data principles. The vocabulary enables data catalog interoperability.

Based on: The W3C Data Catalog Vocabulary Version 2: Rationale Design Principles and Uptake · Data Intelligence (MIT Press)

HighlightJournal of Biomedical Semantics

DCSO: towards an ontology for machine-actionable data management plans

Paper proposing the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts.

The authors propose the DMP Common Standard Ontology (DCSO) to represent Data Management Plans as machine-actionable artefacts. The ontology is based on the Research Data Alliance DMP Common Standard and aims to provide a standardised way to describe controlled vocabularies. The paper reports on a community effort to create the DCSO, which can become a suitable candidate for a reference serialisation of the DMP Common Standard.

Based on: DCSO: towards an ontology for machine-actionable data management plans · Journal of Biomedical Semantics

HighlightGhent University Academic Bibliography (Ghent University)

Improving and assessing data quality of knowledge graphs

A dissertation on improving and assessing data quality in knowledge graphs.

This dissertation focuses on improving data quality and assessing semantic quality of knowledge graphs. It investigates two challenges: including data transformations to clean the data, and evaluating the quality of knowledge graphs on both data values and semantic relationships.

Based on: Improving and assessing data quality of knowledge graphs · Ghent University Academic Bibliography (Ghent University)