Mining Symbolic Rules to Explain Lung Cancer Treatments

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review

Autoren

  • Disha Purohit
  • Maria-Esther Vidal

Organisationseinheiten

Externe Organisationen

  • Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des Sammelwerks The Semantic Web
UntertitelESWC 2023 Satellite Events
Herausgeber/-innenCatia Pesquita, Hala Skaf-Molli, Vasilis Efthymiou, Sabrina Kirrane, Axel Ngonga, Diego Collarana, Renato Cerqueira, Mehwish Alam, Cassia Trojahn, Sven Hertling
Seiten69-74
Seitenumfang6
ISBN (elektronisch)978-3-031-43458-7
PublikationsstatusVeröffentlicht - 2023

Publikationsreihe

NameLecture Notes in Computer Science
Band13998
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

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).

Ziele für nachhaltige Entwicklung

Zitieren

Mining Symbolic Rules to Explain Lung Cancer Treatments. / Purohit, Disha; Vidal, Maria-Esther.
The Semantic Web: ESWC 2023 Satellite Events. Hrsg. / Catia Pesquita; Hala Skaf-Molli; Vasilis Efthymiou; Sabrina Kirrane; Axel Ngonga; Diego Collarana; Renato Cerqueira; Mehwish Alam; Cassia Trojahn; Sven Hertling. 2023. S. 69-74 (Lecture Notes in Computer Science; Band 13998).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review

Purohit, D & Vidal, M-E 2023, Mining Symbolic Rules to Explain Lung Cancer Treatments. in C Pesquita, H Skaf-Molli, V Efthymiou, S Kirrane, A Ngonga, D Collarana, R Cerqueira, M Alam, C Trojahn & S Hertling (Hrsg.), The Semantic Web: ESWC 2023 Satellite Events. Lecture Notes in Computer Science, Bd. 13998, S. 69-74. https://doi.org/10.1007/978-3-031-43458-7_13
Purohit, D., & Vidal, M.-E. (2023). Mining Symbolic Rules to Explain Lung Cancer Treatments. In C. Pesquita, H. Skaf-Molli, V. Efthymiou, S. Kirrane, A. Ngonga, D. Collarana, R. Cerqueira, M. Alam, C. Trojahn, & S. Hertling (Hrsg.), The Semantic Web: ESWC 2023 Satellite Events (S. 69-74). (Lecture Notes in Computer Science; Band 13998). https://doi.org/10.1007/978-3-031-43458-7_13
Purohit D, Vidal ME. Mining Symbolic Rules to Explain Lung Cancer Treatments. in Pesquita C, Skaf-Molli H, Efthymiou V, Kirrane S, Ngonga A, Collarana D, Cerqueira R, Alam M, Trojahn C, Hertling S, Hrsg., The Semantic Web: ESWC 2023 Satellite Events. 2023. S. 69-74. (Lecture Notes in Computer Science). Epub 2023 Okt 21. doi: 10.1007/978-3-031-43458-7_13
Purohit, Disha ; Vidal, Maria-Esther. / Mining Symbolic Rules to Explain Lung Cancer Treatments. The Semantic Web: ESWC 2023 Satellite Events. Hrsg. / Catia Pesquita ; Hala Skaf-Molli ; Vasilis Efthymiou ; Sabrina Kirrane ; Axel Ngonga ; Diego Collarana ; Renato Cerqueira ; Mehwish Alam ; Cassia Trojahn ; Sven Hertling. 2023. S. 69-74 (Lecture Notes in Computer Science).
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