stwfsapy 0.1.3

A library for match labels of thesaurus concepts to text and assigning scores to found occurrences.

stwfsapy

About

This library provides functionality to find the labels of SKOS thesaurus concepts in text. It is a reimplementation in Python of stwfsa combined with the concept scoring from [1]. A deterministic finite automata is constructed from the labels of the thesaurus concepts to perform the matching. In addition, a classifier is trained to score the matched occurrences of the concepts.

Data Requirements

The construction for the automaton requires a SKOS thesaurus represented as a rdflib Graph. Concepts should be related to labels by skos:prefLabel or skos:altLabel. In addition it is assumed that concepts are organized in a hierarchy that includes sub-thesauri. Concepts and sub-thesauri have to be distinguishable by rdf:type. The training of the predictor requires labeled text. Each training sample should be annotated with one or more concepts from the thesaurus.

Usage

Create predictor

First load your graph.

from rdflib import Graph

g = Graph()
g.load('/path/to/your/thesaurus')

Define the type URIs for descriptors and sub-thesauri. You also need to define the relationship that relates sub-thesauri to concepts. It is also beneficial if this relation structures the sub-thesauri. Furthermore you can indicate whether the thesaurus relation is a specialisation For the STW this would be

descriptor_type_uri = 'http://zbw.eu/namespaces/zbw-extensions/Descriptor'
thsys_type_uri = 'http://zbw.eu/namespaces/zbw-extensions/Thsys'
thesaurus_relation_type_uri = 'http://www.w3.org/2004/02/skos/core#broader'
is_specialisation = False

Create the predictor

from stwfsapy.predictor import StwfsapyPredictor
p = StwfsapyPredictor(
    g,
    descriptor_type_uri,
    thsys_type_uri,
    thesaurus_relation_type_uri,
    is_specialisation,
    langs={'en'},
    simple_english_plural_rules=True)

The next step assumes you have loaded your texts into a list X and your labels in a list of lists y, such that for all indices 0 <= i < len(X) The list at y[i] contains the URIs to the correct concepts for X[i]. The concepts should be given by their URI. Then you can train the classifier:

p.fit(X, y)

Afterwards you can get the predicted concepts and scores:

p.suggest_proba(['one input text', 'A completely different input text.']

Alternatively you can get a sparse matrix of scores by calling

p.predict_proba(['one input text', 'Another input text.']

The indices of the concepts are stored in p.concept_map_.

Save Model

A trained predictor p can be stored by calling p.store('/path/to/storage/location'). Afterwards it can be loaded as follows:

from stwfsapy.predictor import StwfsapyPredictor

StwfsapyPredictor.load('/path/to/storage/location')

References

[1] Toepfer, Martin, and Christin Seifert. "Content-based quality estimation for automatic subject indexing of short texts under precision and recall constraints." International Conference on Theory and Practice of Digital Libraries. Springer, Cham, 2018.