BioHackathon Europe Find out about the 2026 BioHackathon Europe, where hackers get together to make life science research easier.

📣 BioHackathon Europe 2026 Call for project proposals is now open!
Submit your #bioinformatics project 🧑‍💻 for #BioHackEU26 by 15 April: biohackathon-europe.org
🙌 We welcome proposals that focus on areas supporting the work of ELIXIR – #FAIRdata, #metadata standards, #ontologies and more.

Original post on fedihum.org

We’re looking for a Research Knowledge Engineer (m/f/x) (full time, 3,5 years). If you have a #DigitalHumanities profile with experience in #Ontologies, #KnowledgeGraphs and #LinkedOpenData, then we’re looking for you! Boosts welcome […]

KMi Seminar - Embedding Complex Knowledge: From Geometric to Language Models The University of Manchester

KMi seminar - Embedding Complex Knowledge: From Geometric to Language Models

3 March 2026, 11:30 (GMT)

#AI #SemanticWeb #Ontologies #KnowledgeRepresentation #MachineLearning #ResearchTalk

kmi.open.ac.uk/seminars/4001

KMi Seminar - Embedding Complex Knowledge: From Geometric to Language Models The University of Manchester

KMi seminar - Embedding Complex Knowledge: From Geometric to Language Models

3 March 2026, 11:30 (GMT)

#AI #SemanticWeb #Ontologies #KnowledgeRepresentation #MachineLearning #ResearchTalk

kmi.open.ac.uk/seminars/4001

TransiT researcher and computer scientist Joe Preece.

A train service in the West Midlands. Photo by the West Midlands Combined Authority.

A bus crossing a bridge in Birmingham. Photo by Christian Mackie on Unsplash.

#Transport data is complex - our computer scientist Joe Preece discusses how to link it all together in a network of connected digital twins

More here: tinyurl.com/msjrtbhu

#TransiT #NetZero #DigitalTwin #Ontologies @unibirmingham.bsky.social

#IoT #Ecosystems require #Ontologies of your #Products and #Services - Karl A L Smith Karl A L Smith Blog, UX, CX, Agility, AI, IoT. Leadership, Author, Agile World

#IoT #Ecosystems require #Ontologies of your #Products and #Services. Products and services associated with the IoT currently operate in closed ecosystems like Home Automation, in effect, they are simply networked products with linking software.

karlsmith.info/iot-ecosyste...

KMi seminar - Embedding Complex Knowledge: From Geometric to Language Models

3 March 2026, 11:30 (GMT)

#AI #SemanticWeb #Ontologies #KnowledgeRepresentation #MachineLearning #ResearchTalk

kmi.open.ac.uk/seminars/4001

How do you turn static EA diagrams into dynamic #Ontologies? 🕸️💻

Video: Deep dive into #ArchiMate 3.2 objectives, meta-models, and the Open Exchange XML format.

Watch now: youtu.be/m2hSn6KXVVE

Join our membership for exclusive EA resources! 💎

#EnterpriseArchitecture #KnowledgeGraph #TechTutorial

Announcing Ontology Support, Improved Data Model Visualization, Entra ID auth Support & More

Dive into this month's G.V() release: gdotv.com/blog/gdotv-r...

#RDF #ontologies #graphdatabases #dataviz

Structured vocabularies are doing a lot of heavy lifting behind the scenes. From taxonomies to thesauri to full ontologies, the right structure makes data more accurate and trustworthy. #Ontologies #Taxonomies

If you have ever used - or wished to use - #ontologies or other #FAIRdata teminologies to represent #errors, #uncertainty or #doubt, please get in touch, as I'd like to get a bit more systematic about doing these things as well, particularly in the context of #nanopublications .

#openresearch

Mapping from SSSOM to Wikidata At the 4th Ontologies4Chem Workshop in Limburg an der Lahn, I proposed an initial crosswalk between the Simple Standard for Sharing Ontological Mappings (SSSOM) and the Wikidata semantic mapping data model. This post describes the motivation for this proposal and the concrete implementation I’ve developed in `sssom-pydantic`. This work is part of the NFDI’s Ontology Harmonization and Mapping Working Group, which is interested in enabling interoperability between SSSOM and related data standards that encode semantic mappings. The TL;DR for this post is that I implemented a mapping from SSSOM to Wikidata in `sssom-pydantic` in cthoyt/sssom-pydantic#32. One high-level entrypoint is the following function, which reads an SSSOM file and prepares QuickStatements which can be reviewed in the web browser, then uploaded to Wikidata. This script can be run from Gist with `uv run https://gist.github.com/cthoyt/f38d37426a288989158a9804f74e731a#file-sssom-wikidata-demo-py` ## Semantic Mappings in SSSOM The Simple Standard for Sharing Ontological Mappings (SSSOM) is a community-driven data standard for semantic mappings, which are necessary to support (semi-)automated data integration and knowledge integration, such as in the construction of knowledge graphs. While SSSOM primary a tabular data format that is best serialized in TSV, it uses LinkML to formalize the semantics of each field such that SSSOM can be serialized to and read from OWL, RDF, and JSON-LD. Here’s a brief example: subject_id | subject_label | predicate_id | object_id | object_label | mapping_justification ---|---|---|---|---|--- wikidata:Q128700 | cell wall | skos:exactMatch | GO:0005618 | cell wall | semapv:ManualMappingCuration wikidata:Q47512 | acetic acid | skos:exactMatch | CHEBI:15366 | acetic acid | semapv:ManualMappingCuration ## Semantic Mappings in Wikidata Wikidata has two complementary formalisms for representing semantic mappings. The first uses the exact match (P2888) property with a URI as the object. For example, cell wall (Q128700) maps to the Gene Ontology (GO) term for cell wall by its URI `http://purl.obolibrary.org/obo/GO_0005618`. The second formalism uses semantic space-specific properties (e.g. P683 for ChEBI) with local unique identifiers as the object. For example, acetic acid (Q47512) maps to the ChEBI term for acetic acid using the P683 property for ChEBI and local unique identifier for acetic acid (within ChEBI) `15366`. Wikidata has a data structure that enables annotating qualifiers onto triples. Therefore, other parts of semantic mappings modeled in SSSOM can be ported: 1. Authors and reviewers can be mapped from ORCiD identifiers to Wikidata identifiers, then encoded using the S50 and S4032 properties, respectively 2. A SKOS-flavored mapping predicate (i.e., exact, narrow, broad, close, related) can be encoded using the S4390 property 3. The publication date can be encoded using the S577 property 4. The license can be mapped from text to a Wikidata identifier, then encoded using the S275 property Note that properties that normally start with a `P` when used in triples are changed to start with an `S` when used as qualifiers. Other fields in SSSOM could potentially be mapped to Wikidata later. ### Finding Wikidata Properties using the Semantic Farm The Semantic Farm (previously called the Bioregistry) maintains mappings between prefixes that appear in compact URIs (CURIEs) and their corresponding Wikidata properties. For example, the prefix `CHEBI` maps to the Wikidata property P683. These mappings can be accessed in several ways: 1. via the Semantic Farm’s SSSOM export. Note: this requires subsetting to mappings where Wikidata properties are the object. 2. via the Semantic Farm’s live API, 3. via the Bioregistry Python package (this will get renamed to match Semantic Farm, eventually) using the following code: import bioregistry # get bulk prefix_to_property = bioregistry.get_registry_map("wikidata") # get for a single resource resource = bioregistry.get_resource("chebi") chebi_wikidata_property_id = resource.get_mapped_prefix("wikidata") ## Notable Implementation Details I’ve previously built two package which were key to making this work: 1. `wikidata-client`, which interacts with the Wikidata SPARQL endpoint and has high-level wrappers around lookup functionality. I’m also aware of WikidataIntegrator - I’ve contributed several improvements, but working with its codebase doesn’t spark joy and the last time I tried to use it, it was fully broken due to some of its dependencies not working on modern Python. 2. `quickstatements-client`, which implements an object model for QuickStatements v2 and an API client. Along the way to this PR, I made improvements to the wikidata-client in cthoyt/wikidata-client#2 to add high-level functionality for looking up multiple Wikidata records based on values for a property (e.g., to support ORCID lookup in bulk). All other changes were made in `sssom-pydantic` in cthoyt/sssom-pydantic#32. The other key challenge was to avoid adding duplicate information to Wikidata - unlike a simple triple store, we could accidentally end up with duplicate statements. Therefore, the sssom-pydantic implementation looks up all existing semantic mappings in Wikidata for entities appearing in an SSSOM file, then filters appropriately to avoid uploading duplicate mappings to Wikidata. ## Pulling it All Together This new module in `sssom-pydantic` implements the following interactive workflows: 1. Read an SSSOM file, convert mappings to Wikidata schema, then open a QuickStatements tab in the web browser using `read_and_open_quickstatements()` 2. Convert in-memory semantic mappings to the Wikidata schema, then open a QuickStatements tab in the web browser using `open_quickstatements()` Here’s what the QuickStatements web interface looks like after preparing some demo mappings: It also implements the following non-interactive workflows, which should be used with caution since they write directly to Wikidata: 1. Read an SSSOM file, convert mappings to Wikidata schema, then post non-interactively to Wikidata via QuickStatements using `read_and_post()` 2. Convert in-memory semantic mappings to the Wikidata schema, then post non-interactively to Wikidata via QuickStatements using `post()` * * * I’m a bit hesitant to start uploading SSSOM content to Wikidata in bulk, because I don’t yet have a plan for how to maintain mappings that might change over time in their upstream single source of truth, e.g., mappings curated in Biomappings. Otherwise, I think this is a good proof of concept and would like to get feedback about additional qualifiers that could be added, and if the ones I chose so far were the best.

every wanted to put semantic mappings in SSSOM into @wikidata

now you can

https://cthoyt.com/2026/01/08/sssom-to-wikidata.html

#SemanticModels, #Ontologies, #DigitalTwins, call it whatever you want but the reality is that...

youtu.be/0jQ5lZg8Fjc

#ai #artificalintelligence #powerbi #microsoft #microsoftfabric

Imagine AI that talks the same language as suppliers and regulators—thanks to unified ontologies and knowledge graphs. Agentic LLMs can now learn continuously and nest insights. Dive into the future of AI reasoning! #AgenticAI #Ontologies #KnowledgeGraphs

🔗 aidailypost.com/news/unified...

RE: https://social.edu.nl/@tgx_um/115645028833529913

on Monday we have a go/no-go and we really want to go

So, are you considering sign up before Sunday evening!

#ontologies #linkeddata #sparql

A slide with the question "where are you in your data journey?", with audience votes: Junk Shop(0), Car Boot(3), Street Market (4), Local Market (8), Hypermarket (6), Amazon (0)

Slide of talk "How to implement ontology based data management" at ConnectedData London

Asking the audience where they are on their data journey, on a scale from Junk Shop -> Amazon

2025.connected-data.london/talks/moving...
#cdl25 #ConnectedData #Ontologies

At #ConnectedData in London having my mind blown on something I thought I knew something about.

Some clever people doing clever things out there. All looks so simple but there's some serious thinking going on under the hood.

#KnowledgeGraph #Ontologies #AI #Taxonomies

The Weekly Edge: Dgraph Lives, RyuGraph Rises, Gephi Lite & More [31 October 2025] Discover what’s new in the world of graph technology this week, including a Dgraph acquisition, a new fork of Kuzu, the release of Gephi Lite, and more.

This week's headlines from the world of #graphdatabases and graph tech:
* Meet #RyuGraph
* #Dgraph finds a new patron
* Dave Bechberger from #AWS talks AI memory
* Ontology design 101
* Gephi Lite v1.0 is here (ICYMI)

More here: gdotv.com/blog/weekly-...

#Kuzu #Ontologies #AmazonNeptune #Graphviz

Can lightweight open LLMs support identifying semantic relationships for ontologies?

ceur-ws.org/Vol-3780/pap...

Why read? A simple yet quite effective way of identifying relations.
What’s next? Explore other domains and LLMs (as suggested).

#ShortReview #AI #LLMs #KO #Relationships #Ontologies

Original post on scholar.social

in my first double blog post ever, I wrote about encoding databases as ontologies, the PyOBO software package, and the design choices and philosophy behind the HGNC (@genenames) to ontology converter.

1️⃣ background and software - […]

#WorldStandardsDaytoday
⠀
Our contribution:
- developing #ontologies for #chemistry,⠀
- helping on the development of the #INChI⠀
- provide a #terminology service⠀
- and we create MICHIs.
#NMR #MICHI is finished, now asking for feedback in the adoption of the U-Vis MICHI.⠀⠀
https://t1p.de/ljeii⠀

#ITByte: The Web Ontology Language (#OWL) is a family of knowledge representation languages for authoring #Ontologies.

It's primarily used in the #SemanticWeb.

knowledgezone.co.in/trends/explo...

Philippe Descola : la politique au prisme de l'anthropologie - [mcInform@ctions]

Non-Fiction
Philippe Descola : la politique au prisme de l’anthropologie
mcinformactions.net/philippe-descola-la-poli...
#Descola #anthropologie #ontologies #collectifs #cosmopolitique

Ontology: when philosophy meets science | Particle Ontologies help computers understand the world … but whose world?

How do philosophy and science relate?

Read it here: particle.scitech.org.au/people/ontol...

#ontologies #tobeornottobe

We are thrilled to announce the publication of our latest research paper, "Ontologies as the Semantic Bridge Between Artificial Intelligence and Healthcare", in Frontiers in Digital Health.

Read the full paper here: lnkd.in/g3nA5qFK

#DigitalHealth #AI #Ontologies #SemanticInteroperability

Bio-Ontologies, Epistemic Diversity, and Epistemic Justice: Data Curation and the Case Against ‘Excellence’ - Minerva Minerva - The notion of ‘excellence’ has become the key logic structuring academic funding and evaluation. This essay offers a critique of this policy logic by examining the case of...

Interesting paper on #biocuration and #ontologies from SR Davies
"In structuring how the biosciences can access, work with,
and think about biological data biocuration profoundly shapes the field as a whole. ‘Excellent’ or not, it is certainly important." doi.org/10.1007/s110...

New Journal Article: "Large Language Models For Scholarly Ontology Generation: An Extensive Analysis in the #Engineering Field" www.sciencedirect.com/science/arti... #LLMs #ontologies #AI

Interesting read here on #language #ontologies
www.sciencedirect.com/science/arti...

Original post on c.im

Looks very interesting on #ontologies of language based in practice and performance. This puts 'worlding' first in language emerging and becoming.

Sounds like #anthropology is needed for understanding how #language works. This fits our #ritual/#speech coevolution theory.

#ontologicalturn […]

🚀 #AI and semantic technologies are transforming how we study and advance scientific #research! Thrilled to share our latest paper on using #LLMs to generate structured representations of research fields. arxiv.org/abs/2412.08258
#OpenScience #Ontologies #ML #NLP #KG