Mining Contextual Rules to Predict Asbestos in Buildings

Thamer Mecharnia (1, 2) , Nathalie Pernelle (3) , Celine Rouveirol (3) , Fayçal Hamdi (4) , Lydia Chibout Khelifa (2)

1 LISN - Laboratoire Interdisciplinaire des Sciences du Numérique
2 CSTB - Centre Scientifique et Technique du Bâtiment
3 LIPN - Laboratoire d'Informatique de Paris-Nord
4 CEDRIC - Centre d'études et de recherche en informatique et communications

In the context of the work conducted at CSTB (French Scientific and Technical Center for Building), the need for a tool providing assistance in the identification of asbestos-containing materials in buildings was identified. To this end, we have developed an approach, named CRA-Miner, that mines logical rules from a knowledge graph that describes buildings and asbestos diagnoses. Since the specific product used is not defined, CRA-Miner considers temporal data, product types, and contextual information to find a set of candidate rules that maximizes the confidence. These rules can then be used to identify building elements that may contain asbestos and those that are asbestos-free. The experiments conducted on an RDF graph provided by the CSTB show that the proposed approach is promising and a satisfactory accuracy can be obtained.

Mots clés: Rule mining; Knowledge graph; Temporal Data; Asbestos

Domaines: Sciences de l'ingénieur [physics]

Thamer Mecharnia, Nathalie Pernelle, Celine Rouveirol, Fayçal Hamdi, Lydia Chibout Khelifa. Mining Contextual Rules to Predict Asbestos in Buildings. Graph-Based Representation and Reasoning, Sep 2021, En ligne, France. pp.170-184, ⟨10.1007/978-3-030-86982-3_13⟩. ⟨hal-03722804⟩