Algorithm for Population of Object Property Assertions Derived from Telecom Contact Centre Product Support Documentation

Relay of information from technical documentation by contact center workers to assist clients is limited by industry standard storage formats and query mechanisms. Here we present and evaluate a new methodology for processing technical documents and tagging them against a Telecom Hardware domain ontology. We deploy classical ontological NLP approaches to extract information from both text segments and tables, identifying text segments, named entities and relations between named entities described by an existing T-Box. We describe a method for scoring candidate object property assertions derived from text before populating the Telecom Hardware ontology. In our algorithm we leverage customized gazetteer lists, including lists specific to object property synonyms, and use functions of distance between co-occurring terms to score candidate A-box object property assertions. We review the performance of this approach with a use case involving Tier 1 and Tier 2 call centre agents using a visual query tool, Top Braid Live, to interrogate the instantiated Telecom Hardware ontology for information relevant to the needs of clients.