Conservation process model (cpm). A twofold scientific research scope in the information modelling for cultural heritage

The aim of the present research is to develop an instrument able to adequately support the conservation process by means of a twofold approach, based on both BIM environment and ontology formalisation. Although BIM has been successfully experimented within AEC (Architecture Engineering Construction) field, it has showed many drawbacks for architectural heritage. To cope with unicity and more generally complexity of ancient buildings, applications so far developed have shown to poorly adapt BIM to conservation design with unsatisfactory results (Dore, Murphy 2013; Carrara 2014). In order to combine achievements reached within AEC through BIM environment (design control and management) with an appropriate, semantically enriched and flexible The presented model has at its core a knowledge base developed through information ontologies and oriented around the formalization and computability of all the knowledge necessary for the full comprehension of the object of architectural heritage an its conservation. Such a knowledge representation is worked out upon conceptual categories defined above all within architectural criticism and conservation scope. The present paper aims at further extending the scope of conceptual modelling within cultural heritage conservation already formalized by the model. A special focus is directed on decay analysis and surfaces conservation project

frances : cloud-based historical text mining with deep learning and parallel processing

frances is an advanced cloud-based text mining digital platform that leverages information extraction, knowledge graphs, natural language processing (NLP), deep learning, and parallel processing techniques. It has been specifically designed to unlock the full potential of historical digital textual collections, such as those from the National Library of Scotland, offering cloud-based capabilities and extended support for complex NLP analyses and data visualizations. frances enables realtime recurrent operational text mining and provides robust capabilities for temporal analysis, accompanied by automatic visualizations for easy result inspection. In this paper, we present the motivation behind the development of frances, emphasizing its innovative design and novel implementation aspects. We also outline future development directions. Additionally, we evaluate the platform through two comprehensive case studies in history and publishing history. Feedback from participants in these studies demonstrates that frances accelerates their work and facilitates rapid testing and dissemination of ideas.Postprin