{'fraction_non_alphanumeric': 0.023034154090548053, 'fraction_numerical': 0.0, 'mean_word_length': 5.737967914438503, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Extracting and Merging Contextualized Ontology Modules

Ontology module extraction, from a large ontology, leads to the generation of a specialized knowledge model that is pertinent to specific problems. Existing ontology module extraction methods tend to either render a too generalized or a too restricted ontology module that at times does not capture the entire semantics of the source ontology. We present an ontology module extraction method that extracts a contextualized ontology module whilst extending the semantics of the extracted concepts and their relationships in the ontology module. Our approach features the following tenets (i) identifying the user-selected concepts that are pertinent for the problem-context at hand; (ii) extracting the user-selected concepts, their roles and their individuals; and (iii) extracting other concepts, roles and individuals that are structurally-connected with the user-selected concepts. We apply our ontology module extraction method in the Healthcare domain, and demonstrate (a) extraction of ontology modules from three prostate cancer pathway ontologies; and then (b) merging of extracted ontology modules to generate a comprehensive therapeutic work-flow knowledge for prostate cancer care management.