ToCT: A task ontology to manage complex templates

Natural language interfaces are a well-known approach to grant non-experts access to semantic web technologies. A number of such systems use simple templates to achieve that for English and more elab-orate solutions for other languages. They keep being designed from scratch in an ad hoc manner, since there is no shared conceptualisation of simple templates and there is no model that is formalised using a Semantic Web language to apply the techniques to itself. We aim to address this by proposing a general-purpose solution in the form of a novel model for templates, formalised as a task ontology in OWL,calledToCT. We used it to develop an ontology-driven text generator for isiZulu, a morphologically-rich language, to test its capabilities. The generator verbalises the TBox of an ontology as validationq uestions. This evaluation showed that the task ontology is sufficiently expressive for the template design, which was subsequently verified with user evaluations who judged the texts positivel

An evaluation of template and ML-based generation of user-readable text from a knowledge graph

Typical user-friendly renderings of knowledge graphs are visualisations and natural language text. Within the latter HCI solution approach, data-driven natural language generation systems receive increased attention, but they are often outperformed by template-based systems due to su ering from errors such as content dropping, hallucination, or repetition. It is unknown which of those errors are associated signi cantly with low quality judgements by humans who the text is aimed for, which hampers addressing errors based on their impact on improving human evaluations. We assessed their possible association with an experiment availing of expert and crowdsourced evaluations of human authored text, template generated text, and sequence-to-sequence model generated text. The results showed that there was no significant association between human authored texts with errors and the low human judgements of naturalness and quality. There was also no significant association between machine learning generated texts with dropped or hallucinated slots and the low human judgements of naturalness and quality. Thus, both approaches appear to be viable options for designing a natural language interface for knowledge graph