text2term 1.3.0

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.

Installation

Install package using pip:

pip install .

Programmatic Usage

The tool can be executed in Python with either of the two following functions:

text2term.map_file(input_file='/some/file.txt', 
                   target_ontology='http://some.ontology/v1.owl',
                   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=',', 
                   use_cache=False)

or

text2term.map_terms(source_terms=['term one', 'term two'],
                    target_ontology='http://some.ontology/v1.owl',
                    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=(),
                    use_cache=False)

Arguments

For map_file, the first argument 'input_file' specifies a path to a file containing the terms 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

Caching

As of version 1.1.0, users can now cache ontologies that they want to use regularly or quickly. Programatically, there are two steps to using the cache: creating the cache, then accessing it. First, the user can cache ontologies using either of two functions:

cache_ontology(ontology_url, ontology_acronym, base_iris=()) Or cache_ontology_set(ontology_registry_path)

The first of these will cache a single ontology from a URL or file path, with it being referenced by an acronym that will be used to reference it later. An example can be found below. The second function allows the user to cache several ontologies at once by referencing a CSV file of the format: acronym,name,version,date,url,comments

Once an ontology has been cached by either function, it is stored in a cache folder locally, and thus can be referenced even in different Python instances.

NOTE: Due to how ontologies are processed in memory, cache_ontology_set must be used to cache multiple ontologies in a single Python instance. If cache_ontology is used multiple times in one instance, the behavior is undefined and may cause visible or invisible errors.

After an ontology is cached, the user can access the cache by using the assigned acronym in the place of target_ontology and setting the use_cache flag to True. To clear the cache, one can call: clear_cache(ontology_acronym='') If no arguments are specified, the entire cache will be cleared. Otherwise, only the ontology with the given acronym will be cleared. Finally, cache_exists(ontology_acronym) is a simple program that returns True if the given acronym exists in the cache, and False otherwise. It is worth noting that while ontology URLs can repeat, acronyms must be distinct in a given environment.

Preprocessing

As of version 1.2.0, text2term now includes a simple preprocessing functionality for input. Specifically, these functions take the original input text and Regex expressions, then match each text to a regular expression to simplify the input.

Like the "map" functions above, the two functions differ on whether is input is a file or a list of strings: preprocess_file(file_path, template_path, output_file="", blacklist_path="", blacklist_char='', rem_duplicates=DupSetting.NO_REM) or preprocess_terms(terms, template_path, output_file="", blacklist_path="", blacklist_char='', rem_duplicates=DupSetting.NO_REM)

In both cases, the templates and the blacklist must be stored in a newline seperated file. If an output file is specified, the preprocessed strings are written to that file and the list is passed back regardless.

The blacklist functionality allows the user to specify another regex file. If any terms match any regex in blacklist, they are removed from the terms, or, if a blacklist character is specified, replaced with that character for placeholding.

Finally, the Remove Duplicates functionality will remove all duplicate terms before processing, after, or both depending on the setting. To represent the settings, the following emnumeration class exists in the preprocessing module:

class DupSetting(Enum):
    NO_REM = 0
    REM_BEFORE = 1
    REM_AFTER = 2
    REM_BOTH = 3

WARNING: Removing duplicates at any point does not guarantee that the order of the terms are maintained. As such, if order is important to the output, this functionality is not recommended.

Command Line Usage

After installation, execute the tool from a command line 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.

-c STORE_IN_CACHE Using this flag followed by the acronym the ontology should be stored as, the program will same the target ontology to the cache. After that, referencing the acronym in target will reference the cache. Examples are below.

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")

Below is an example of caching, assuming the same imports as above:

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

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.

Use the cache on the command line, first by flagging it, then in the future using the acronym: python text2term -s unstruct_terms.txt -t http://www.ebi.ac.uk/efo/efo.owl -c EFO Then, after running this, the following command is equivalent: python text2term -s unstruct_terms.txt -t EFO