{"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/general-purpose-declarative-inductive","title":"General-purpose Declarative Inductive Programming with Domain-Specific Background Knowledge for Data Wrangling Automation","arxiv_id":"1809.10054","date":"2018-09-26","proceeding":null,"authors":["Lidia Contreras-Ochando","César Ferri","José Hernández-Orallo","Fernando Martínez-Plumed","María José Ramírez-Quintana","Susumu Katayama"],"abstract":"Given one or two examples, humans are good at understanding how to solve a\nproblem independently of its domain, because they are able to detect what the\nproblem is and to choose the appropriate background knowledge according to the\ncontext. For instance, presented with the string \"8/17/2017\" to be transformed\nto \"17th of August of 2017\", humans will process this in two steps: (1) they\nrecognise that it is a date and (2) they map the date to the 17th of August of\n2017. Inductive Programming (IP) aims at learning declarative (functional or\nlogic) programs from examples. Two key advantages of IP are the use of\nbackground knowledge and the ability to synthesise programs from a few\ninput/output examples (as humans do). In this paper we propose to use IP as a\nmeans for automating repetitive data manipulation tasks, frequently presented\nduring the process of {\\em data wrangling} in many data manipulation problems.\nHere we show that with the use of general-purpose declarative (programming)\nlanguages jointly with generic IP systems and the definition of domain-specific\nknowledge, many specific data wrangling problems from different application\ndomains can be automatically solved from very few examples. We also propose an\nintegrated benchmark for data wrangling, which we share publicly for the\ncommunity.","url_abs":"http://arxiv.org/abs/1809.10054v1","url_pdf":"http://arxiv.org/pdf/1809.10054v1.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":"general-purpose-declarative-inductive","repo_url":"https://github.com/liconoc/DataWrangling-DSI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.10054","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}