Multi-word Entity Extraction and Rich Relationship Identification to Derive Conceptual Models from Natural Language Specifications

• Autores: Maltempo, Giuliana; Delle Ville, Juliana; Cecconato, Santiago Andrés; Pellegrino, Federico; Distante, Damiano; Antonelli, Leandro
• Año de publicación: 2024
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias de la Computación e Información; Requirements Specifications; Kernel Sentences; Conceptual Model; Natural Language
• Institución: Comisión de Investigaciones Científicas de la Provincia de Buenos Aires

Requirements engineering is a critical phase in software development. Errors in requirements specifications may become costly problems later on; therefore, such errors should be found and corrected early in the engineering process. Describing requirements in natural language is propitious for both the domain experts and the software development team. However, natural language may give rise to diverse interpretations as a consequence of the different backgrounds of the two participants involved. It is therefore necessary to provide guidance on the specification of unambiguous requirements. In previous work, we have advanced the notion of kernel sentences as an appropriate structure for the specification of knowledge. We have also discussed conceptual models as a useful technique to summarize specifications so that all participants have a concise overview of the domain. To achieve consistent and coherent specifications, we presented a two-step method: first compliance with kernel format is checked, and then a conceptual model is derived to summarize the knowledge gathered. This paper extends the conceptual model previously derived from kernel sentences by identifying multi-word entities and establishing various new relationships among entities. This is intended to help achieve better quality specifications. We also describe a prototype that uses natural language processing and artificial intelligence tools to support the method. Finally, we present the results of a preliminary evaluation of our method, which show a promising applicability.