{"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/probabilistic-search-for-structured-data-via","title":"Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes","arxiv_id":"1704.01087","date":"2017-04-04","proceeding":null,"authors":["Feras Saad","Leonardo Casarsa","Vikash Mansinghka"],"abstract":"Databases are widespread, yet extracting relevant data can be difficult.\nWithout substantial domain knowledge, multivariate search queries often return\nsparse or uninformative results. This paper introduces an approach for\nsearching structured data based on probabilistic programming and nonparametric\nBayes. Users specify queries in a probabilistic language that combines standard\nSQL database search operators with an information theoretic ranking function\ncalled predictive relevance. Predictive relevance can be calculated by a fast\nsparse matrix algorithm based on posterior samples from CrossCat, a\nnonparametric Bayesian model for high-dimensional, heterogeneously-typed data\ntables. The result is a flexible search technique that applies to a broad class\nof information retrieval problems, which we integrate into BayesDB, a\nprobabilistic programming platform for probabilistic data analysis. This paper\ndemonstrates applications to databases of US colleges, global macroeconomic\nindicators of public health, and classic cars. We found that human evaluators\noften prefer the results from probabilistic search to results from a standard\nbaseline.","url_abs":"http://arxiv.org/abs/1704.01087v1","url_pdf":"http://arxiv.org/pdf/1704.01087v1.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":"probabilistic-search-for-structured-data-via","repo_url":"https://github.com/probcomp/cgpm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"probabilistic-programming","task_name":"Probabilistic Programming"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}