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i-Dataquest: A heterogeneous information retrieval tool using data graph for the manufacturing industry

Article dans une revue avec comité de lecture
Author
KIM, Lise
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
ccYAHIA, Esma
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
ccSEGONDS, Frédéric
127758 Laboratoire Conception de Produits et Innovation [LCPI]
VÉRON, Philippe
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
MALLET, Antoine
244560 Capgemini [Toulouse]

URI
http://hdl.handle.net/10985/20716
Date
2023
Journal
Computers in Industry

Abstract

Manufacturing industry needs access to the data in order to realise its activities but also to generate new value-added knowledge. Nevertheless, it is confronted with a large and growing volume of heterogeneous data which limits its ability to exploit them optimally. Moreover, the data are distributed within different heterogeneous information systems, which limits the relationship exploration under the information retrieval process. Usually, the challenge is addressed by trying to manage and normalize the data structure in order to faster searching and exploiting them in a manufacturing context. For their part, the authors present i-Dataquest, an information retrieval system supported by (i) a graph-oriented model built from the structured and unstructured data of the company and (ii) a query system answering ‘what’ and ‘about what’ and (iii) generating three different results: a list of items, a list of property values and a list of sentences. The i-Dataquest prototype is built using Neo4J for the graph system generation, ConceptNet for lexical resource management and StandfordNLP for natural language processing. An evaluation of the prototype’s performance is conducted through a data set representing a drone manufacturer. The results show that the transformation of specific content such as tables in the graph and the semantic expansion of queries significantly improves the recall and precision measures. The results also suggest improving filtering less relevant results by considering particularly queries looking for a specific value.

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