DYNAMIC GENERATION OF AN ONTOLOGY-BASED AI SCHEMA FOR CHATBOTS

Strategic investments in Artificial Intelligence may enable companies to gain business advantages. There are challenges in using generic natural language processing (NLP) capabilities with complex products and with content that requires specialized domain-specific terminologies. Darwin Information Typing Architecture (DITA)-generated AI schema can leverage enterprise source code to train bots or any other conversational systems to improve the accuracy levels without any manual intervention. A well-defined AI schema is generated from the DITA source files that contain an ontology framework of Intents, Entities, Dialog nodes, along with child nodes, as a result. The schema can be depicted as a JSON file

PACKAGING AND PROPAGATION OF MOLECULAR DOCUMENTATION RESPONSES

Techniques are presented herein that support the tokenizing of semantically enriched information sets (such as the Darwin Information Typing Architecture (DITA)) to associate ontological relationships between the various entities and intents in a product information library and rendering them as API responses. The method uses an Inverted Disclosure Plot and a weighted algorithm to determine the order of kinship based on the varied relationships that occur between the extracted entities and various intents. Subsequently, a domain ontology may be generated as a Resource Description Framework (RDF) and, the RDF may be translated into a series of application programming interface (API) notations