@article{fdi:010094161,
  title = {{D}empster-{S}hafer theory for object matching under data imperfection constraints : application to wastewater networks' line matching},
  author = {{B}elghaddar, {Y}. and {B}egdouri, {A}. and {C}hahinian, {N}an{\'e}e and {S}eriai, {A}. and {E}t-targuy, {O}. and {D}elenne, {C}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he goal of object matching is to identify objects representing the same real entity across multiple spatial datasets. {T}his involves comparing and linking data from different sources using similarity measures, with the final matching decision made by combining these measures. {O}bject matching is especially valuable for creating accurate and complete spatial datasets for underground networks, where data often come from various sources and may have imperfections like imprecision or incompleteness. {T}he {D}empster-{S}hafer ({DS}) theory, which uses mass functions to model data imperfections, is considered the best method for combining imperfect data. {H}owever, previous {DS}-based approaches produced highly conflicting results when many potential candidates for an object existed. {I}n this work, we present an improved {DS}-based line matching approach for wastewater networks. {O}ur key contributions include introducing candidate ranking, bidirectional measure combination, and mixed models to convert similarity measures into masses. {W}e validated our approach through experiments on both synthetic and real-world datasets. {T}he results demonstrate that our contributions significantly reduce conflict and improve the accuracy and correctness of the matching decision.},
  keywords = {{O}bject matching ; {D}empster-{S}hafer theory ; {S}imilarity measure ; {D}ata integration ; {D}ata imperfections},
  booktitle = {},
  journal = {{I}nformation {S}ciences},
  volume = {717},
  numero = {},
  pages = {122304 [24 p.]},
  ISSN = {0020-0255},
  year = {2025},
  DOI = {10.1016/j.ins.2025.122304},
  URL = {https://www.documentation.ird.fr/hor/fdi:010094161},
}