Learn how TypeDB combines graph semantics with strong schema and reasoning to model complex domains.

I am excited to announce the formation of a Data Façades W3C Community Group. Façade-X, initially introduced at SEMANTICS 2021 and successfully implemented by the SPARQL Anything project, provides a simple yet powerful, homogeneous view over diverse and heterogeneous data sources (e.g., CSV, JSON, XML, and many others). With the recent v1.0.0 release of SPARQL Anything, the time was right to work on the long-term stability and widespread adoption of this approach by developing an open, vendor-neutral technology. The Façade-X concept was born to allow SPARQL users to query data in any structured format in plain SPARQL. Therefore, the choice of a W3C community group to lead efforts on specifications is just natural. Specifications will enhance its reliability, foster innovation, and encourage various vendors and projects—including graph database developers — to provide their own compatible implementations. The primary goals of the Data Façades Community Group is to: Define the core specification of the Façade-X method. Define Standard Mappings: Formalize the required mappings and profiles for connecting Façade-X to common data formats. Define the specification of the query dialect: Provide a reference for the SPARQL dialect, configuration conventions (like SERVICE IRIs), and the functions/magic properties used. Establish Governance: Create a monitored, robust process for adding support for new data formats. Foster Collaboration: Build connections with relevant W3C groups (e.g., RDF & SPARQL, Data Shapes) and encourage involvement from developers, businesses, and adopters. Join us! With Luigi Asprino Ivo Velitchkov Justin Dowdy Paul Mulholland Andy Seaborne Ryan Shaw ... CG: https://lnkd.in/eSxuqsvn Github: https://lnkd.in/dkHGT8N3 SPARQL Anything #RDF #SPARQL #W3C #FX

Agentic Ontology Assisted Framework for Semantic Triple Extraction - growgraph/ontocast

Just released a new notebook exploring Semantic Entity Resolution & Extraction using DSPy (Community) and Google's new LangExtract library. Inspired by Russell Jurney’s excellent work on semantic entity resolution, this demo follows his approach of combining: ✅ embeddings, ✅ kNN blocking, ✅ and LLM matching with DSPy (Community). On top of that, I added a general extraction layer to test-drive LangExtract, a Gemini-powered, open-source Python library for reliable structured information extraction. The goal? Detect and merge mentions of the same real-world entities across text. It’s an end-to-end flow tackling one of the most persistent data challenges. Check it out, experiment with your own data, 𝐞𝐧𝐣𝐨𝐲 𝐭𝐡𝐞 𝐬𝐮𝐦𝐦𝐞𝐫 and let me know your thoughts! cc Paco Nathan you might like this 😉 https://wor.ai/8kQ2qa

Ever wish you could make an ontology right from your spreadsheet? A lot of my ontology drafting work begins with a spreadsheet: a lexicon, a catalog of important concepts or subject-matter expert t…

Cellosaurus is now available in RDF format, with a triple store that supports SPARQL queries If this sounds a bit abstract or unfamiliar… 1) RDF stands for Resource Description Framework. Think of RDF as a way to express knowledge using triplets: Subject – Predicate – Object. Example: HeLa (subject) – is_transformed_by (predicate) – Human papillomavirus type 18 (object) These triplets are like little facts that can be connected together to form a graph of knowledge. 2) A triple store is a database designed specifically to store and retrieve these RDF triplets. Unlike traditional databases (tables, rows), triple stores are optimized for linked data. They allow you to navigate connections between biological entities, like species, tissues, genes, diseases, etc. 3) SPARQL is a query language for RDF data. It lets you ask complex questions, such as: - Find all cell lines with a *RAS (HRAS, NRAS, KRAS) mutation in p.Gly12 - Find all Cell lines from animals belonging the order "carnivora" More specifically we now offer from the Tool - API submenu 6 new options: 1) SPARQL Editor (https://lnkd.in/eF2QMsYR). The SPARQL Editor is a tool designed to assist users in developing their SPARQL queries. 2) SPARQL Service (https://lnkd.in/eZ-iN7_e). The SPARQL service is the web service that accepts SPARQL queries over HTTP and returns results from the RDF dataset. 3) Cellosaurs Ontology (https://lnkd.in/eX5ExjMe). An RDF ontology is a formal, structured representation of knowledge. It explicitly defines domain-specific concepts - such as classes and properties - enabling data to be described with meaningful semantics that both humans and machines can interpret. The Cellosaurus ontology is expressed in OWL. 4) Cellosaurus Concept Hopper (https://lnkd.in/e7CH5nj4). The Concept Hopper, is a tool that provides an alternative view of the Cellosaurus ontology. It focuses on a single concept at a time - either a class or a property - and shows how that concept is linked to others within the ontology, as well as how it appears in the data. 5) Cellosaurus dereferencing service (https://lnkd.in/eSATMhGb). The RDF dereferencing service is the mechanism that, given a URI, returns an RDF description of the resource identified by that URI, enabling clients to retrieve structured, machine-readable data about the resource from the web in different formats. 6) Cellosaurus RDF files download (https://lnkd.in/emuEYnMD). This allows you to download the Cellosaurus RDF files in Turtle (ttl) format.

Ontology Alignment (OA) is fundamental for achieving semantic interoperability across diverse knowledge systems. We present OntoAligner, a comprehensive, modular, and robust Python toolkit for...

Repository for the Procedural Knowledge Ontology (PKO) - GitHub - perks-project/pk-ontology: Repository for the Procedural Knowledge Ontology (PKO)

Transform UML into a formal OWL ontology and SHACL shapes - OP-TED/model2owl

We contributed recently to the "awesome semantic shapes" repository. This is a community-curated list of RDF shape resources, be it validators, generators…

What if creating Linked Open Data was less like coding and more like writing? Could anyone extend the Semantic Web by sharing a document? Publish a knowledge… | 13 comments on LinkedIn