Data Structures

A semantic space is a collections of identifiers for concepts. For example, the Chemical Entities of Biomedical Interest (ChEBI) has a semantic space including identifiers for chemicals. Within ChEBI’s semantic space, 138488 corresponds to the chemical alsterpaullone.


138488 is a local unique identifier. Other semantic spaces might use the same local unique identifier to refer to a different concept in their respective domain.

Therefore, local unique identifiers should be qualified with some additional information saying what semantic space it comes from. The two common formalisms for doing this are Uniform Resource Identifiers (URIs) and Compact URIs (CURIEs):

Demo of URI and CURIE for alsterpaullone.

In many applications, it’s important to be able to convert between CURIEs and URIs. Therefore, we need a data structure that connects the CURIE prefixes like CHEBI to the URI prefixes like

Prefix Maps

A prefix map is a dictionary data structure where keys represent CURIE prefixes and their associated values represent URI prefixes. Ideally, these are constrained to be bijective (i.e., no duplicate keys, no duplicate values), but this is not always done in practice. Here’s an example prefix map containing information about semantic spaces from a small selection of OBO Foundry ontologies:

    "CHEBI": "",
    "MONDO": "",
    "GO": ""

Prefix maps have the benefit of being simple and straightforward. They appear in many linked data applications, including:

  • the @prefix declarations at the top of Turtle (RDF) documents and SPARQL queries


  • XML documents

  • OWL ontologies


Prefix maps can be loaded using curies.Converter.from_prefix_map().

However, prefix maps have the main limitation that they do not have first-class support for synonyms of CURIE prefixes or URI prefixes. In practice, a variety of synonyms are used for both. For example, the NCBI Taxonomy database appears with many different CURIE prefixes:

CURIE Prefix


taxonomy, Name-to-Thing


Gene Ontology Registry


OBO Foundry, Prefix Commons, OntoBee











Similarly, many different URIs can be constructed for the same ChEBI local unique identifier. Using alsterpaullone as an example, this includes (many omitted):

URI Prefix


ChEBI (first-party)

OBO Foundry


In practice, we need to be able to support the fact that there are many CURIE prefixes and URI prefixes for most semantic spaces as well as specify which CURIE prefix and URI prefix is the “preferred” one in a given context. Prefix maps, unfortunately, have no way to address this. Therefore, we’re going to introduce a new data structure.

Extended Prefix Maps

Extended Prefix Maps (EPMs) address the issues with prefix maps by including explicit fields for CURIE prefix synonyms and URI prefix synonyms while maintaining an explicit field for the preferred CURIE prefix and URI prefix. An abbreviated example (just containing an entry for ChEBI) looks like:

        "prefix": "CHEBI",
        "uri_prefix": "",
        "prefix_synonyms": ["chebi"],
        "uri_prefix_synonyms": [

An EPM is simply a list of records (see curies.Record and curies.Records). EPMs have the benefit that they are still encoded in JSON and can easily be encoded in YAML, TOML, RDF, and other schemata. Further, prefix maps can be automatically upgraded into EPMs (with some caveats) using curies.upgrade_prefix_map().


We are introducing this as a new standard in the curies package. They can be loaded using curies.Converter.from_extended_prefix_map(). We provide a Pydantic model representing it. Later, we hope to have an external, stable definition of this data schema.

A JSON schema for EPMs is available at It can be updated at