every wanted to put semantic mappings in SSSOM into @wikidata
now you can
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.
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
EMBL-EBI created OxO to enable users to map between datasets that are annotated with different ontologies. Mappings identified by the first version of OxO were not necessarily logically sound, lacked important provenance information such as author and reviewer, and could timeout or crash for certain requests. In this paper we introduce OxO2 to address these concerns. Provenance is addressed by implementing SSSOM, a mapping standard that defines provenance for mappings. SSSOM defines the conditions under which logical sound mappings can be derived and is implemented in OxO2 using Nemo, a Datalog rule engine. To ensure reasoning is performant and memory efficient, Nemo implements a number of strategies that ensures OxO2 will be stable for all requests. Due to these changes, OxO2 users will be able to integrate between disparate datasets with greater confidence.
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.
Specifying and solving Constraint-based Optimization Problems (COP) has become a mainstream technology for advanced motion control of mobile robots. COP programming still requires expert knowledge to transform specific application context into the right configuration of the COP parameters (i.e., objective functions and constraints). The research contribution of this paper is a methodology to couple the context knowledge of application developers to the robot knowledge of control engineers, which, to our knowledge, has not yet been carried out. The former is offered a selected set of symbolic descriptions of the robots’ capabilities (its so-called “behavior semantics”) that are translated in control actions via “templates” in a “semantic map”; the latter contains the parameters that cover contextual dependencies in an application and robot vendor-independent way. The translation from semantics to control templates takes place in an “interaction layer” that contains 1) generic knowledge about robot motion capabilities (e.g., depending on the kinematic type of the robots), 2) spatial queries to extract relevant COP parameters from a semantic map (e.g., what is the impact of entering different types of “collision areas”), and 3) generic application knowledge (e.g., how the robots’ behavior is impacted by priorities, emergency, safety, and prudence). This particular design of, and interplay between, the application, interaction, and control layers provides a structured, concept...
🇬🇧 And paper is out on HAL : "Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps"
Charles Tapley Hoyt
Victor Paléologue
Nicolas Audebert