This research revolves around utilising Named Entity Recognition (NER) to analyse and categorise data from English translations of pharmacopeias from the Abbasid era, noted for its valuable contributions to science and medicine. The main goal of this work, along with publishing this resource freely, is to assess cross-manuscript NER performance by evaluating the NER model’s performance on unseen corpora and translation styles, as well as demonstrating the transferability of the NER task on such corpora. Two distinct experiments were conducted, focusing on F1-scores differences from mixing source translators and varying training dataset sizes. In experiments involving mixing translator styles, training on a mix of all available styles while accounting for dataset size yielded the best F1-scores compared to even training on the same style as the testing data, while experiments with dataset sizes show diminishing returns of scaling training datasets compared to varying translation styles. This work attempts to enhance the exploration of the medical knowledge embodied in these texts to facilitate their analysis for knowledge extraction relevant to modern medical practices. Furthermore, this research demonstrates strategies to optimise NER results in this context, forming a juncture between digitising historical information and enabling further explorations in pharmacopeia-related Natural Language Processing research.

Deep learning approaches, given their low cost and high reliability, have gained much popularity in different subjects, such as computer vision and natural language processing, and more recently in graph data types. Spatiotemporal graph-based neural networks have been more and more developed to solve problems related to spatiotemporal data, mainly for analyzing changes over time and for provisioning purposes. In this systematic literature review, we have aimed to answer the most important questions regarding spatiotemporal graph deep learning architectures in different applications domains, such as traffic related topics, medical imaging, and geographical data analysis. We have selected more than 50 papers that cover a wide range of applications and very different architectures and innovations. We have also noticed that most of them consider the spatiotemporal graphs to be quite classic graphs without any further sophisticated modeling of temporal and spatial data evolution. Moreover, core problems (such as node classification, frequent pattern recognition, etc.) and application domains are not sufficiently addressed by the state-of-the-art. This study thus opens many perspectives to new developments in spatio-temporal graph deep learning with different strategies in order to solve various end-to-end tasks and other problems related to this special kind of graph.

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The Region Connection Calculus (RCC), and particularly its RCC8 subset, have been extensively studied and used for qualitative spatial reasoning. Some sets of computational operations have also been defined for topological relations, as the CM8 set, that allows to compute the RCC8 relationships on raster images. In this paper, we propose a reified representation of the RCC8 spatial relationships and of the CM8 primitives, within a lattice of concepts, implemented in OWL (Ontology Web Language) in order to help the interpretation of urban satellite images. Our approach allows for a straightforward representation of concepts corresponding to conjuctions or disjunctions of RCC8 spatial relationships, and thus offers the advantage to overcome some drawbacks of the existing approaches in OWL, where spatial relations are represented as roles. Indeed, the OWL language does not allow the expression of the disjunction or of the conjunction of roles. We can then implement a reasoning on the RCC8 relationships, which in particular allows to compute the composition table and its transitive closure. As the reification of roles precludes the use of role's properties, such as symmetry and transitivity, we propose to implement RCC8 inferences through SWRL rules (Semantic Web Rule Language).

This paper presents an original approach to qualitative spatial representation and reasoning with topological relations based on the use of lattices of relations. This approach has been developed for spatial reasoning in the domain of agricultural landscape analysis. The paper describes first the motivation of the present research work and the general framework of topological relations. Four different lattices of topological relations, including two Galois lattices, are introduced. The choices made for spatial representation and reasoning with these lattices are discussed, together with a thorough study and comparison of the four lattices. The implementation of one of the Galois lattices within an object-based representation system, as well as spatial lattice-based reasoning with topological relations in this system are then detailed.

This paper is concerned with the representation and classification of spatial relations and structures in an object-based knowledge representation system. In this system, spatial structures are defined as sets of spatial entities connected with topological relations. Relations are represented by objects with their own properties. We propose to define two types of properties: the first are concerned with relations as concepts while the second are concerned with relations as links between concepts. In order to represent the second type of properties, we have defined facets that are inspired from the constructors of description logics. We describe these facets and how they are used for classifying spatial structures and relations on land-use maps. Links between the present work and related work in description logics are also discussed.

This paper focuses on the implementation of domain models for problem solving. We assume that the implementation should preserve the form of the domain models, and particularly the division between the knowledge structure and its role toward the solving process. We propose therefore to implement domain models with both knowledge bases and domain agents. We define a domain agent model which is linked to a knowledge base. We describe the various abilities of this agent and how it has been implemented in a multi-agent system with the YAFOOL language.