every wanted to put semantic mappings in SSSOM into @wikidata
now you can
#semanticmapping
biomappings is a project for predicting and curating semantic mappings between biomedical vocabularies in SSSOM
i'm working in @NFDI with researchers from other disciplines, so I recently did a full refactor of the underlying code into a new project, SSSOM Curator (https://github.com/cthoyt/sssom-curator) to make it more accessible outside of biomedicine
Here's a screen cast describing how it works:
The EBI has recently published a preprint describing OxO2, the second major version of their ontology mapping service, now based on SSSOM: https://arxiv.org/abs/2506.04286
nice to see citation of SeMRA and reuse of the comprehensive SSSOM semantic mapping datasets we produced and archived on Zenodo: https://zenodo.org/communities/biopragmatics/records?q&f=subject%3ASemantic%20Mappings
I'm currently generating cross-lingual mappings for educational resources and found a fun non-trivial negative mapping:
kim.lp:0000122 (Gymnasium) and mesh:D020446 (Fitness Centers) aren't related, because Gymnasium is one of the types of German high school
SSSOM is the perfect place to store this (curated via Biomappings: https://github.com/biopragmatics/biomappings/pull/204)
A well-rounded paper on how to translate symbolic statements into actionable constraints for #robotics control and #motionplanning: https://www.frontiersin.org/articles/10.3389/frobt.2023.917637/full
In short: symbolic statements are produced by higher-level decision-making systems (e.g. from a #pddl planner working with #semanticmapping) and given to lower-level actions.
🇬🇧 And paper is out on HAL : "Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps"
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