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SeMRA |release| Documentation

The Semantic Mapping Reasoner and Assembler (SeMRA) assembles semantic mappings to support data- and knowledge integration across domains. This Python package can be used by a variety of people:

  1. Data Scientist - someone who consumes and modifies data to suit an analysis or application. For example, they might want to integrate the Rhea Reaction Knowledgebase, which uses ChEBI identifiers for chemicals, and the Comparative Toxicogenomics Database (CTD), which uses MeSH identifiers for chemicals. :mod:`semra` assembles relevant mappings between ChEBI, MeSH, and other chemical vocabularies and can standard both to ChEBI, MeSH, or any other vocabulary.
  2. Curator - someone who creates data. For example, an ontologist may want to curate mapping from entities in their vocabulary to entities in another (or across all vocabularies for a domain). SeMRA can assemble mappings and make curation easy via a web application.
  3. Data Consumer - someone who consumes data. This kind of user likely won't interact with :mod:`semra` directly, but will likely use tools that build on top of it. For example, someone using a knowledge graph or application built on top of use this package's assembly, inference, and rewiring functionality indirectly.
  4. Software Developer - someone who develops tools to support data creators, data consumers, and other software developers. For example, a software developer might want to make their toolchain more generic for loading, assembling, processing, and outputting semantic mappings.

SeMRA is generally applicable in any domain, from biomedicine to particle physics to the digital humanities. Get started by loading external mappings:

import semra

mappings = semra.from_sssom(
    # load mappings from any standardized SSSOM file as a file path or URL
    "https://w3id.org/biopragmatics/biomappings/sssom/biomappings.sssom.tsv",
    license="spdx:CC0-1.0",
     mapping_set_title="biomappings",
)

Or by creating your own mappings:

from semra import Reference, Mapping, EXACT_MATCH, SimpleEvidence, MappingSet, MANUAL_MAPPING

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

mapping = Mapping(
    subject=r1,
    predicate=EXACT_MATCH,
    object=r2,
    evidence=[
        SimpleEvidence(
            justification=MANUAL_MAPPING,
            confidence=0.99,
            author=Reference(
                prefix="orcid", identifier="0000-0003-4423-4370", name="Charles Tapley Hoyt"
            ),
            mapping_set=MappingSet(
                name="biomappings", license="CC0", confidence=0.90,
            ),
        )
    ]
)

Features

  1. An object model for semantic mappings (based on the Simple Standard for Sharing Ontological Mappings (SSSOM) and :mod:`sssom`)
  2. Functionality for assembling and reasoning over semantic mappings at scale
  3. A provenance model for automatically generated mappings
  4. A confidence model granular at the curator-level, mapping set-level, and community feedback-level

Here's a conceptual diagram of SeMRA's architecture:

What SeMRA Isn't

SeMRA isn't a tool for predicting semantic mappings like Logmap, LOOM, or K-Boom. Further, it's not a tool for reviewing predicted semantic mappings like MapperGPT. However, any of the outputs from these workflows could be used as inputs to SeMRA's assembly and inference pipeline.

SeMRA isn't a service that lives on the web, but it does allow you to deploy a local web application for your use-case specific mapping database.

SeMRA isn't itself a curation tool, but it has the option to integrate :mod:`biomappings` in deployments of its local web application for curation purposes.

SeMRA isn't an tool for merging ontologies like CoMerger or OntoMerger, but it outputs detailed and comprehensive semantic mappings that are critical as input for such tools.

Artifacts Overview

SeMRA was used to produce the SeMRA Raw Semantic Mappings Database, a comprehensive raw semantic mappings database, and five domain-specific mapping databases (each with a landscape analysis). The results of the domain-specific landscape analyses can be found on the SeMRA GitHub repository.

Domain Docs and Reproduction Database Download Analysis
Raw :mod:`semra.database` raw N/A
Disease :mod:`semra.landscape.disease` disease Disease Analysis
Cell and Cell Line :mod:`semra.landscape.cell` cell Cell Analysis
Anatomy :mod:`semra.landscape.anatomy` anatomy Anatomy Analysis
Protein Complex :mod:`semra.landscape.complex` complex Complex Analysis
Gene :mod:`semra.landscape.gene` gene Gene Analysis

Table of Contents

.. toctree::
    :maxdepth: 2
    :caption: Getting Started
    :name: start

    installation
    pipeline
    artifacts
    tutorial
    struct
    io
    reference
    cli
    cypher

Indices and Tables