text2term 1.0.0

A tool for mapping free-text descriptions of (biomedical) entities to controlled terms in an ontology

text2term ontology mapper

A tool for mapping free-text descriptions of (biomedical) entities to controlled terms in an ontology.

Programmatic Usage

Install package using pip:

pip install text2term

The tool can be executed in Python with either of the two following functions: text2term.map_files(input_file, target_ontology, base_iris=(), csv_columns=(), excl_deprecated=False, max_mappings=3, mapper=Mapper.TFIDF,min_score=0.3, output_file='', save_graphs=False, save_mappings=False, separator=',')

or

map_terms(source_terms, target_ontology, base_iris=(), excl_deprecated=False, max_mappings=3, min_score=0.3, mapper=Mapper.TFIDF, output_file='', save_graphs=False, save_mappings=False, source_terms_ids=())

Arguments

For map_files, the first argument 'input_file' specifies a path to a file containing the names of every term that needs to be mapped. For map_terms, The first argument 'source_terms' takes in a list of the terms to be mapped.

All other arguments are the same, and have the same functionality:

target_ontology : str Path or URL of 'target' ontology to map the source terms to. When the chosen mapper is BioPortal or Zooma, provide a comma-separated list of ontology acronyms (eg 'EFO,HPO') or write 'all' to search all ontologies

base_iris : tuple Map only to ontology terms whose IRIs start with one of the strings given in this tuple, for example: ('http://www.ebi.ac.uk/efo','http://purl.obolibrary.org/obo/HP')

source_terms_ids : tuple Collection of identifiers for the given source terms

excl_deprecated : bool Exclude ontology terms stated as deprecated via owl:deprecated true

mapper : mapper.Mapper Method used to compare source terms with ontology terms. One of: levenshtein, jaro, jarowinkler, jaccard, fuzzy, tfidf, zooma, bioportal These can be initialized by invoking mapper.Mapper e.g. mapper.Mapper.TFIDF

max_mappings : int Maximum number of top-ranked mappings returned per source term

min_score : float Minimum similarity score [0,1] for the mappings (1=exact match)

output_file : str Path to desired output file for the mappings

save_graphs : bool Save vis.js graphs representing the neighborhood of each ontology term

save_mappings : bool Save the generated mappings to a file (specified by output_file)

All default values, if they exist, can be seen above.

Return Value

Both functions return the same value:

df : Data frame containing the generated ontology mappings

Command Line Usage

Install package using pip:

pip install .

Execute the tool as follows:

python text2term -s SOURCE -t TARGET [-o OUTPUT] [-m MAPPER] [-csv CSV_INPUT] [-top TOP_MAPPINGS] [-min MIN_SCORE] [-iris BASE_IRIS] [-d EXCL_DEPRECATED] [-g SAVE_TERM_GRAPHS]

To display a help message with descriptions of tool arguments do:

python text2term -h or python text2term --help

Required arguments

-s SOURCE Input file containing 'source' terms to map to ontology terms (list of terms or CSV file).

-t TARGET Path or URL of 'target' ontology to map source terms to. When the chosen mapper is BioPortal or Zooma, provide a comma-separated list of acronyms (eg 'EFO,HPO') or write 'all' to search all ontologies.

Optional arguments

-o OUTPUT Path to desired output file for the mappings.

-m MAPPER Method used to compare source terms with ontology terms. One of: levenshtein, jaro, jarowinkler, jaccard, indel, fuzzy, tfidf, zooma, bioportal.

-csv CSV_INPUT Indicates a CSV format input—follow with the name of the column containing terms to map, optionally followed by the name of the column containing identifiers for the terms (eg 'my terms,my term ids').

-top TOP_MAPPINGS Maximum number of top-ranked mappings returned per source term.

-min MIN_SCORE Minimum similarity score [0,1] for the mappings (1=exact match).

-iris BASE_IRIS Map only to ontology terms whose IRIs start with a value given in this comma-separated list (eg 'http://www.ebi.ac.uk/efo,http://purl.obolibrary.org/obo/HP)').

-d EXCL_DEPRECATED Exclude ontology terms stated as deprecated via owl:deprecated true.

-g SAVE_TERM_GRAPHS Save vis.js graphs representing the neighborhood of each ontology term.

Examples

Programmatic

import text2term
import pandas

df1 = text2term.map_file(unstruct_terms.txt, http://www.ebi.ac.uk/efo/efo.owl)
df2 = text2term.map_terms(["asthma", "colon cancer"], http://www.ebi.ac.uk/efo/efo.owl)

Command Line

The basic use of the tool requires a source file containing a list of terms to map to the given target ontology:
python text2term -s unstruct_terms.txt -t http://www.ebi.ac.uk/efo/efo.owl

Specify an output file where the mappings should be saved using -o:
python text2term -s unstruct_terms.txt -t efo.owl -o /Documents/my-mappings.csv

Set the minimum acceptable similarity score for mapping each given term to an ontology term using -min:
python text2term -s unstruct_terms.txt -t efo.owl -min 0.8
The mapped terms returned will have been determined to be 0.8 similar to their source terms in a 0-1 scale.

Exclude deprecated ontology terms (declared as such via owl:deprecated true) using -d:
python text2term -s unstruct_terms.txt -t efo.owl -d

Limit search to only terms whose IRIs start with any IRI given in a list specified using -iris:
python text2term.py -s unstruct_terms.txt -t efo.owl -iris http://www.ebi.ac.uk/efo/EFO,http://purl.obolibrary.org/obo/HP
Here, because EFO reuses terms from other ontologies such as HP and GO, the HP terms would be included but the GO terms would be excluded.