Mining Symbolic Rules to Explain Lung Cancer Treatments

verfasst von

Disha Purohit, Maria-Esther Vidal

Abstract

Knowledge Graphs (KGs) represent the convergence of data and knowledge as factual statements; they allow for the enrichment of decision-making semantically. Symbolic inductive learning enables uncovering relevant patterns, expressed, for example, as Horn clauses. Albeit powerful, existing symbolic inductive learning frameworks may mine many rules, being difficult for a user to extract actionable insights. This demo illustrates a pipeline to analyze mined logical rules toward discovering meaningful insights. The demo puts into perspective the role of semantic types in guiding the exploration of mined rules. Participants will observe strategies to traverse the mined logical statements and how the outcomes reveal patterns in the prescription of lung cancer treatments. A video is available online (https://www.youtube.com/watch?v=CN4a3kUjfJ4 &ab_channel=TIBSDMGroup), a Jupyter notebook executes a live demos (https://mybinder.org/v2/gh/SDM-TIB/DIGGER-ESWC2023Demo/HEAD?labpath=Mining%20Symbolic%20Rules%20To%20Explain%20Lung%20Cancer%20Treatments.ipynb), and source-code is available in GitHub (https://github.com/SDM-TIB/Mining_Symbolic_Rules_ESWC2023Demo).

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek

Typ

Beitrag in Buch/Sammelwerk

Seiten

69-74

Anzahl der Seiten

6

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1007/978-3-031-43458-7_13 (Zugang: Geschlossen)