{"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/rudas-synthetic-datasets-for-rule-learning","title":"RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools","arxiv_id":"1909.07095","date":"2019-09-16","proceeding":null,"authors":["Cristina Cornelio","Veronika Thost"],"abstract":"Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form. However, rule formulation is a complex process that requires deep domain expertise,and is further challenged by today's often large, heterogeneous, and incomplete knowledge graphs. Several approaches for learning rules automatically, given a set of input example facts,have been proposed over time, including, more recently, neural systems. Yet, the area is missing adequate datasets and evaluation approaches: existing datasets often resemble toy examples that neither cover the various kinds of dependencies between rules nor allow for testing scalability. We present a tool for generating different kinds of datasets and for evaluating rule learning systems, including new performance measures.","url_abs":"https://arxiv.org/abs/1909.07095v2","url_pdf":"https://arxiv.org/pdf/1909.07095v2.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":"rudas-synthetic-datasets-for-rule-learning","repo_url":"https://github.com/IBM/RuDaS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"inductive-knowledge-graph-completion","task_name":"Inductive knowledge graph completion"},{"task_slug":"inductive-logic-programming","task_name":"Inductive logic programming"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"}],"methods":[],"datasets_introduced":[{"slug":"rudas","name":"RuDaS","full_name":"Synthetic Datasets for Rule Learning"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/inductive-logic-programming-on-rudas","task":"Inductive logic programming","dataset":"RuDaS","model":"AMIE+","rank_in_archive_order":1,"of":4,"metrics":{"H-Score":"0.2321","R-Score":"0.335"},"uses_additional_data":false},{"leaderboard":"/sota/inductive-logic-programming-on-rudas","task":"Inductive logic programming","dataset":"RuDaS","model":"FOIL","rank_in_archive_order":2,"of":4,"metrics":{"H-Score":"0.152","R-Score":"0.2728"},"uses_additional_data":false},{"leaderboard":"/sota/inductive-logic-programming-on-rudas","task":"Inductive logic programming","dataset":"RuDaS","model":"Neural-LP","rank_in_archive_order":3,"of":4,"metrics":{"H-Score":"0.1025","R-Score":"0.1906"},"uses_additional_data":false},{"leaderboard":"/sota/inductive-logic-programming-on-rudas","task":"Inductive logic programming","dataset":"RuDaS","model":"NTP","rank_in_archive_order":4,"of":4,"metrics":{"H-Score":"0.0728","R-Score":"0.1811"},"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}