Assembling and inferring missing semantic mappings is a timely problem in biomedical data and knowledge integration. I’ve been developing the Semantic Mapping Assembler and Reasoner (SeMRA) as a generic toolkit for this. In this blog post, I highlight its inference capabilities.

SeMRA implements the chaining and inference rules described in the SSSOM specification. The first rule is inversions:

from semra import Mapping, EXACT_MATCH, Reference
from semra.inference import infer_reversible

r1 = Reference(prefix="chebi", identifier="107635", name="2,3-diacetyloxybenzoic")
r2 = Reference(prefix="mesh", identifier="C011748", name="tosiben")

mapping = Mapping(s=r1, p=EXACT_MATCH, o=r2)

mappings = infer_reversible([mapping])

graph LR
    A[2,3-diacetyloxybenzoic<br/>chebi:107635] -- skos:exactMatch --> B[tosiben<br/>mesh:C011748]
    B -. "skos:exactMatch<br/>(inferred)" .-> A

The second rule is about transitivity. This means some predicates apply over chains. SeMRA further implements configuration for two-length chains and could be extended to arbitrary chains.

from semra import Reference, Mapping, EXACT_MATCH
from semra.inference import infer_chains

r1 = Reference.from_curie("mesh:C406527", name="R 115866")
r2 = Reference.from_curie("chebi:101854", name="talarozole")
r3 = Reference.from_curie("chembl.compound:CHEMBL459505", name="TALAROZOLE")

m1 = Mapping(s=r1, p=EXACT_MATCH, o=r2)
m2 = Mapping(s=r2, p=EXACT_MATCH, o=r3)

mappings = infer_chains([m1, m2])

graph LR
    A[R 115866<br/>mesh:C406527] -- skos:exactMatch --> B[talarozole<br/>chebi:101854]
    B -- skos:exactMatch --> C[TALAROZOLE<br/>chembl.compound:CHEMBL459505]
    A -. "skos:exactMatch<br/>(inferred)" .-> C

The third rule is generalization, which means that a more strict predicate can be relaxed to a less specific predicate, like owl:equivalentTo to skos:exactMatch.

from semra import Reference, Mapping, EXACT_MATCH, EQUIVALENT_TO
from semra.inference import infer_generalizations

r1 = Reference.from_curie("chebi:101854", name="talarozole")
r2 = Reference.from_curie("chembl.compound:CHEMBL459505", name="TALAROZOLE")

m1 = Mapping(s=r1, p=EXACT_MATCH, o=r2)

mappings = infer_generalizations([m1])

graph LR
    A[talarozole<br/>chebi:101854] -- owl:equivalentTo --> B[TALAROZOLE<br/>chembl.compound:CHEMBL459505]
    A -. "skos:exactMatch<br/>(inferred)" .-> B

The third rule can actually be generalized to any kinds of mutation of one predicate to another, given some domain knowledge. For example, some resources curate oboInOwl:hasDbXref predicates when it’s implied that they mean skos:exactMatch because the resource is curated in the OBO flat file format.

from semra import Reference, Mapping, DB_XREF
from semra.inference import infer_dbxref_mutations

r1 = Reference.from_curie("doid:0050577", name="cranioectodermal dysplasia")
r2 = Reference.from_curie("mesh:C562966", name="Cranioectodermal Dysplasia")
m1 = Mapping(s=r1, p=DB_XREF, o=r2)

# we're 99% confident doid-mesh dbxrefs actually are exact matches
mappings = infer_dbxref_mutations([m1], {("doid", "mesh"): 0.99})

graph LR
    A[cranioectodermal dysplasia<br/>doid:0050577] -- oboInOwl:hasDbXref --> B[Cranioectodermal Dysplasia<br/>mesh:C562966]
    A -. "skos:exactMatch<br/>(inferred)" .-> B

There’s a lot more to say about semantic mappings - a good first place to look before getting into the guts of the code is the accompanying manuscript:

Assembly and reasoning over semantic mappings at scale for biomedical data integration
Hoyt, C. T., Karis K., and Gyori, B. M.
bioRxiv, 2025.04.16.649126