[2407.04317] Knowledge-based Drug Samples' Comparison
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Artificial Intelligence

arXiv:2407.04317 (cs)
[Submitted on 5 Jul 2024 (v1), last revised 16 Jul 2024 (this version, v2)]

Title:Knowledge-based Drug Samples' Comparison

Authors:Sébastien Guillemin (LIB), Ana Roxin (LIB), Laurence Dujourdy, Ludovic Journaux
View PDF
Abstract:Drug sample comparison is a process used by the French National police to identify drug distribution networks. The current approach is based on manual comparison done by forensic experts. In this article, we present our approach to acquire, formalise, and specify expert knowledge to improve the current process. For modelling the underlying knowledge we use an ontology coupled with logical rules. The different steps of our approach are designed to be reused in other application domains. The results obtained are explainable making them usable by experts in different fields.
Comments: 17th International Conference on Signal Image Technology & Internet based Systems (SITIS), Nov 2023, Bangkok, Thailand
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2407.04317 [cs.AI]
  (or arXiv:2407.04317v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2407.04317
Related DOI: https://doi.org/10.1109/SITIS61268.2023.00020

Submission history

From: Sebastien GUILLEMIN [view email] [via CCSD proxy]
[v1] Fri, 5 Jul 2024 07:40:25 UTC (807 KB)
[v2] Tue, 16 Jul 2024 07:16:17 UTC (783 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
view license
Current browse context:
cs.AI
< prev   |   next >
new | recent | 2024-07
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)