{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/inducing-a-decision-tree-with-discriminative","title":"Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph","arxiv_id":null,"date":"2019-08-22","proceeding":"International Workshop on Semantics-Powered Data Mining and Analytics 2019 8","authors":["Gilles Vandewiele","Bram Steenwinckel","Femke Ongenae","Filip De Turck"],"abstract":"Deep-learning based techniques are increasingly being used for different machine learning tasks on knowledge graphs. While it has been shown empirically that these techniques often achieve better predictive performances than their classical counterparts, where features are extracted from the graph, they lack interpretability. Interpretability is a vital aspect in critical domains such as the health and financial sector. In this paper, we present a technique that builds a decision tree of class-specific substructures in order to classify different entities within the knowledge graph. We show how our proposed technique is competitive to current state-of-the-art deep-learning techniques on four benchmark datasets, while being fully interpretable.","url_abs":"http://ceur-ws.org/Vol-2427/SEPDA_2019_paper_3.pdf","url_pdf":"http://ceur-ws.org/Vol-2427/SEPDA_2019_paper_3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"inducing-a-decision-tree-with-discriminative","repo_url":"https://github.com/IBCNServices/KGPTree","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/node-classification-on-aifb","task":"Node Classification","dataset":"AIFB","model":"Path Tree","rank_in_archive_order":5,"of":7,"metrics":{"Accuracy":"89.44"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-am","task":"Node Classification","dataset":"AM","model":"Path Tree","rank_in_archive_order":6,"of":8,"metrics":{"Accuracy":"86.77"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-bgs","task":"Node Classification","dataset":"BGS","model":"Path Tree","rank_in_archive_order":4,"of":7,"metrics":{"Accuracy":"86.90"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-mutag","task":"Node Classification","dataset":"MUTAG","model":"Path Tree","rank_in_archive_order":4,"of":6,"metrics":{"Accuracy":"73.82"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}