src.dackar.workflows.WorkflowManager

Created on August 1, 2025 @author: wangc, mandd

Attributes

NERMapping
customPipe
logger

Classes

WorkflowManager

Workflow Manager

Module Contents

src.dackar.workflows.WorkflowManager.NERMapping

src.dackar.workflows.WorkflowManager.customPipe

src.dackar.workflows.WorkflowManager.logger

class src.dackar.workflows.WorkflowManager.WorkflowManager(config)

Workflow Manager

_nlpConfig = None

_neo4jConfig = None

_config

initializeNLP()

Initialize NLP calculation

initializeNeo4j()

Initialize NEO4j settings

runNLP()

Execute the knowledge extraction

Parameters:

doc (str) – raw text data to process

runNeo4j()

Load data into neo4j

run()

Execute the workflow

write(data, fname, style='csv')

Dump data

Parameters:

• data (pandas.DataFrame) – output data to dump

• fname (str) – file name to save the data

• style (str, optional) – type of file. Defaults to ‘csv’.

visualize(doc)

visual entities

Parameters:

doc (spacy.tokens.doc.Doc) – the processed document using nlp pipelines

reset()

_validate(config)

validate dackar input file using JSON schema

Parameters:

config (dict) – dictionary for dackar input

Raises:

IOError – error out if not valid

generatePattern(config)

Generate patterns using provided OPM and/or entity file

Parameters:

config (dict) – input dictionary

Returns:

list of patterns will be used by entity matcher

Return type:

list

processCausalEnt()

Parse causal keywords, and generate patterns for them The patterns can be used to identify the causal relationships

Returns:

list of patterns will be used by causal entity matcher

Return type:

list

preprocessing()

setup text pre-processing pipeline

Raises:

IOError – if pipeline option is not available

Returns:

Preprocessing pipeline

Return type:

Preprocessing Object

ner()

Set up NER pipelines

Raises:

NER Object – Object to conduct NER

causal()

Set up causal analysis flow

Returns:

Object to conduct causal analysis

Return type:

Workflow Object