text2graph-API

Use this library for text-to-graph tranformations. To use the API it is necessary to install its modules and dependencies in the user’s application. Also, the corpus of text documents to be transformed into graphs has to be loaded and read.

text2graphapi is a text to graph transformation pipeline that consists of four main modules::

• Text Preprocessing and Normalization. This module aims to perform all the cleaning and pre-processing part of the text. Apply NLP methods such as POS-Tag, Lemm, Stem, etc.
• Graph Model. This module aims to define the entities/nodes and their relationships/edges according to the problem specification.
• Graph Extraction. This module aims to build the graph according to the selected model. We use third-party libraries such as NetworkX.
• Graph Transformation and Analysis. This module aims to apply vector transformations to the graph as final output, such as adjacency matrix, dense matrix, etc.

Where to get it

# from PYPI
pip install text2graphapi

Example input data

# Has to be a list of dict, where ecah dict conatins an 'id' and 'doc' text data
input_text_docs = [{"id": 1, "doc": "text_data_1"},
                   {"id": 2, "doc": "text_data_2"}]

How to use it

from text2graphapi.src.Cooccurrence import Cooccurrence

to_cooccurrence = Cooccurrence(
                graph_type = 'DiGraph', 
                apply_prep = True, 
                parallel_exec = False,
                window_size = 1, 
                language = 'en',
                output_format = 'adj_matrix')
                
output_text_graphs = to_cooccurrence.transform(corpus_docs)