Natural Language Generation from Ontologies

Abstract

This paper addresses the problem of automatic generation of natural language descriptions for ontology-described artifacts. The original motivation for the work is the challenge of providing textual narratives of automatically generated scientific workflows (e.g., paragraphs that scientists can include in their publications). The paper presents two systems which generate descriptions of sets of atoms derived from a collection of ontologies. The first system, called nlgPhylogeny, demonstrates the feasibility of the task in the Phylotastic project, providing evolutionary biologists with narrative for automatically generated analysis workflows. nlgPhylogeny utilizes the fact that the Grammatical Framework (GF) is suitable for the natural language generation (NLG) task; the paper shows how elements of the ontologies in Phylotastic, such as web services and information artifacts, can be encoded in GF for the NLG task. The second system, called $\mathtt{\small nlgOntology}^{A}$, is a generalization of nlgPhylogeny. It eliminates the requirement that a GF needs to be defined and proposes the use of annotated ontologies for NLG. Given a set of annotated ontologies, $\mathtt{\small nlgOntology}^{A}$ generates a GF suitable for the creation of natural language descriptions of sets of atoms derived from these ontologies. The paper describes the algorithms used in the development of nlgPhylogeny and $\mathtt{\small nlgOntology}^{A}$ and discusses potential applications of these systems.