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Abstract

The Goal in this paper is to propose a cultural heritage data model and evolve towards the creation of a framework based on MongoDB that will allow to manage a JSON model representing the cultural heritage of a city ICHC (Intelligent Cultural Heritage of a City). This manuscript per the authors noticed that during the census of cultural heritage, the presence of human resources linked to heritage is not something that is represented in a smart engine of a framework. Which is why the goal is to integrate the human resource and therefore add a relational aspect to the NoSql documents so that the resulting framework can have a smart engine to link data.This model is a set of ICHD (Intelligent Cultural Heritage Document) which are JSON documents that represent of the different types of cultural heritage entities. Those documents will be managed in a MongoDB repository architecture that will allow to them, so that the microservices-based ICHC framework can offer a big data context that can handle a huge variety, volume and velocity of data and be based on distributed operations.

This paper presents an automata-based Arabic morphology analyzer and an object-oriented data model. Arabic morphology is too complex to model exhaustively with classical approaches. Therefore, the first issue of this paper is the proposal of an adequate data model representing Arabic morphological components and related building rules. Our proposed MorphoScript model is a declarative and object-oriented language using classes, inheritance, and aggregation as basic supports to define the morphological components and all possible morphological links between them. The data model is also based on an annotation indexing system for semantic enrichment of the morphology knowledge. The other contribution of this paper is the compilation of the data model into a deterministic finite-state automaton that represents morphological knowledge. The produced AMA (Arabic Morphological Automaton) constitutes the nucleus of the final proposed morphological analyzer. As a result, the MorphoScript language allowed us to represent the morphological knowledge base in a readable and extremely optimal data model. On the other hand, the morphological automata generated from the MorphoScript database make the morphological process very fast, simple, and deterministic. Moreover, the compilation process is fully automatic, so we can update any morphological rule or component and run the compiler to automatically obtain a new version of the automaton.