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Approches sémantiques pour la prédiction de présence d'amiante dans les bâtiments : une approche probabiliste et une approche à base de règles

Abstract : Nowadays, Knowledge Graphs are used to represent all kinds of data and they constitute scalable and interoperable resources that can be used by decision support tools. The Scientific and Technical Center for Building (CSTB) was asked to develop a tool to help identify materials containing asbestos in buildings. In this context, we have created and populated the ASBESTOS ontology which allows the representation of building data and the results of diagnostics carried out in order to detect the presence of asbestos in the used products. We then relied on this knowledge graph to develop two approaches which make it possible to predict the presence of asbestos in products in the absence of the reference of the marketed product actually used.The first approach, called the hybrid approach, is based on external resources describing the periods when the marketed products are asbestos-containing to calculate the probability of the existence of asbestos in a building component. This approach addresses conflicts between external resources, and incompleteness of listed data by applying a pessimistic fusion approach that adjusts the calculated probabilities using a subset of diagnostics.The second approach, called CRA-Miner, is inspired by inductive logic programming (ILP) methods to discover rules from the knowledge graph describing buildings and asbestos diagnoses. Since the reference of specific products used during construction is never specified, CRA-Miner considers temporal data, ASBESTOS ontology semantics, product types and contextual information such as part-of relations to discover a set of rules that can be used to predict the presence of asbestos in construction elements.The evaluation of the two approaches carried out on the ASBESTOS ontology populated with the data provided by the CSTB show that the results obtained, in particular when the two approaches are combined, are quite promising.
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https://tel.archives-ouvertes.fr/tel-03676831
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Submitted on : Tuesday, May 24, 2022 - 12:15:11 PM
Last modification on : Thursday, May 26, 2022 - 3:45:40 AM

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  • HAL Id : tel-03676831, version 1

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Thamer Mecharnia. Approches sémantiques pour la prédiction de présence d'amiante dans les bâtiments : une approche probabiliste et une approche à base de règles. Intelligence artificielle [cs.AI]. Université Paris-Saclay, 2022. Français. ⟨NNT : 2022UPASG036⟩. ⟨tel-03676831⟩

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