{"id":7294,"date":"2022-01-10T15:07:52","date_gmt":"2022-01-10T15:07:52","guid":{"rendered":"https:\/\/megagon.ai\/publications\/machamp-a-generalized-entity-matching-benchmark\/"},"modified":"2025-03-26T00:07:37","modified_gmt":"2025-03-26T00:07:37","slug":"machamp-a-generalized-entity-matching-benchmark","status":"publish","type":"publications","link":"https:\/\/megagon.ai\/jp\/publications\/machamp-a-generalized-entity-matching-benchmark\/","title":{"rendered":"Machamp: A Generalized Entity Matching Benchmark"},"template":"","publications-tags":[],"conference-year":[65],"conference":[156],"class_list":["post-7294","publications","type-publications","status-publish","hentry","conference-year-65","conference-cikm"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machamp: A Generalized Entity Matching Benchmark - Megagon<\/title>\n<meta name=\"description\" content=\"Existing benchmark tasks for EM are limited to the case where the two data collections of entities are structured tables with the same schema. Meanwhile, the data collections for matching could be structured, semi-structured, or unstructured in real-world scenarios of data science. In this paper, we come up with a new research problem \u2013 Generalized Entity Matching to satisfy this requirement and create a benchmark Machamp for it. Machamp consists of seven tasks having diverse characteristics and thus provides good coverage of use cases in real applications.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/megagon.ai\/jp\/publications\/machamp-a-generalized-entity-matching-benchmark\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machamp: A Generalized Entity Matching Benchmark - Megagon\" \/>\n<meta property=\"og:description\" content=\"Existing benchmark tasks for EM are limited to the case where the two data collections of entities are structured tables with the same schema. Meanwhile, the data collections for matching could be structured, semi-structured, or unstructured in real-world scenarios of data science. In this paper, we come up with a new research problem \u2013 Generalized Entity Matching to satisfy this requirement and create a benchmark Machamp for it. Machamp consists of seven tasks having diverse characteristics and thus provides good coverage of use cases in real applications.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/megagon.ai\/jp\/publications\/machamp-a-generalized-entity-matching-benchmark\/\" \/>\n<meta property=\"og:site_name\" content=\"Megagon\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/megagonlabs\/\" \/>\n<meta property=\"article:modified_time\" content=\"2025-03-26T00:07:37+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/publications\\\/machamp-a-generalized-entity-matching-benchmark\\\/\",\"url\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/publications\\\/machamp-a-generalized-entity-matching-benchmark\\\/\",\"name\":\"Machamp: A Generalized Entity Matching Benchmark - Megagon\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/#website\"},\"datePublished\":\"2022-01-10T15:07:52+00:00\",\"dateModified\":\"2025-03-26T00:07:37+00:00\",\"description\":\"Existing benchmark tasks for EM are limited to the case where the two data collections of entities are structured tables with the same schema. Meanwhile, the data collections for matching could be structured, semi-structured, or unstructured in real-world scenarios of data science. In this paper, we come up with a new research problem \u2013 Generalized Entity Matching to satisfy this requirement and create a benchmark Machamp for it. Machamp consists of seven tasks having diverse characteristics and thus provides good coverage of use cases in real applications.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/publications\\\/machamp-a-generalized-entity-matching-benchmark\\\/#breadcrumb\"},\"inLanguage\":\"ja-JP\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/megagon.ai\\\/jp\\\/publications\\\/machamp-a-generalized-entity-matching-benchmark\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/publications\\\/machamp-a-generalized-entity-matching-benchmark\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Publications\",\"item\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/publications\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Machamp: A Generalized Entity Matching Benchmark\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/#website\",\"url\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/\",\"name\":\"Megagon Labs\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"ja-JP\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/megagon.ai\\\/jp\\\/#organization\",\"name\":\"Megagon Labs\",\"url\":\"https:\\\/\\\/megagon.ai\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https:\\\/\\\/megagon.ai\\\/wp-content\\\/uploads\\\/2025\\\/02\\\/Logo-Megagon-Labs.webp\",\"caption\":\"Megagon Labs\"},\"image\":{\"url\":\"https:\\\/\\\/megagon.ai\\\/wp-content\\\/uploads\\\/2025\\\/02\\\/Logo-Megagon-Labs.webp\"},\"description\":\"Megagon Labs is an AI research organization conducting research in compound AI systems, large language models, data-AI symbiosis, and human-centered AI. Megagon Labs shares its findings with the broader community through open-source tools, datasets, publications, workshops, and an invited speaker series.\",\"sameAs\":[\"https:\\\/\\\/github.com\\\/megagonlabs\",\"https:\\\/\\\/twitter.com\\\/megagonlabs\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/megagon-labs\\\/\",\"https:\\\/\\\/www.facebook.com\\\/megagonlabs\\\/\"],\"address\":{\"@type\":\"PostalAddress\",\"streetAddress\":\"444 Castro Street\",\"addressLocality\":\"Mountain View\",\"addressRegion\":\"CA\",\"postalCode\":\"94041\",\"addressCountry\":\"US\"},\"contactPoint\":{\"@type\":\"ContactPoint\",\"email\":\"contactus@megagon.ai\",\"contactType\":\"general inquiries\"},\"parentOrganization\":{\"@type\":\"Organization\",\"name\":\"Recruit Holdings\",\"url\":\"https:\\\/\\\/recruit-holdings.com\\\/en\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Machamp: A Generalized Entity Matching Benchmark - Megagon","description":"Existing benchmark tasks for EM are limited to the case where the two data collections of entities are structured tables with the same schema. Meanwhile, the data collections for matching could be structured, semi-structured, or unstructured in real-world scenarios of data science. In this paper, we come up with a new research problem \u2013 Generalized Entity Matching to satisfy this requirement and create a benchmark Machamp for it. Machamp consists of seven tasks having diverse characteristics and thus provides good coverage of use cases in real applications.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/megagon.ai\/jp\/publications\/machamp-a-generalized-entity-matching-benchmark\/","og_locale":"ja_JP","og_type":"article","og_title":"Machamp: A Generalized Entity Matching Benchmark - Megagon","og_description":"Existing benchmark tasks for EM are limited to the case where the two data collections of entities are structured tables with the same schema. Meanwhile, the data collections for matching could be structured, semi-structured, or unstructured in real-world scenarios of data science. In this paper, we come up with a new research problem \u2013 Generalized Entity Matching to satisfy this requirement and create a benchmark Machamp for it. Machamp consists of seven tasks having diverse characteristics and thus provides good coverage of use cases in real applications.","og_url":"https:\/\/megagon.ai\/jp\/publications\/machamp-a-generalized-entity-matching-benchmark\/","og_site_name":"Megagon","article_publisher":"https:\/\/www.facebook.com\/megagonlabs\/","article_modified_time":"2025-03-26T00:07:37+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/megagon.ai\/jp\/publications\/machamp-a-generalized-entity-matching-benchmark\/","url":"https:\/\/megagon.ai\/jp\/publications\/machamp-a-generalized-entity-matching-benchmark\/","name":"Machamp: A Generalized Entity Matching Benchmark - Megagon","isPartOf":{"@id":"https:\/\/megagon.ai\/jp\/#website"},"datePublished":"2022-01-10T15:07:52+00:00","dateModified":"2025-03-26T00:07:37+00:00","description":"Existing benchmark tasks for EM are limited to the case where the two data collections of entities are structured tables with the same schema. Meanwhile, the data collections for matching could be structured, semi-structured, or unstructured in real-world scenarios of data science. In this paper, we come up with a new research problem \u2013 Generalized Entity Matching to satisfy this requirement and create a benchmark Machamp for it. Machamp consists of seven tasks having diverse characteristics and thus provides good coverage of use cases in real applications.","breadcrumb":{"@id":"https:\/\/megagon.ai\/jp\/publications\/machamp-a-generalized-entity-matching-benchmark\/#breadcrumb"},"inLanguage":"ja-JP","potentialAction":[{"@type":"ReadAction","target":["https:\/\/megagon.ai\/jp\/publications\/machamp-a-generalized-entity-matching-benchmark\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/megagon.ai\/jp\/publications\/machamp-a-generalized-entity-matching-benchmark\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/megagon.ai\/jp\/"},{"@type":"ListItem","position":2,"name":"Publications","item":"https:\/\/megagon.ai\/jp\/publications\/"},{"@type":"ListItem","position":3,"name":"Machamp: A Generalized Entity Matching Benchmark"}]},{"@type":"WebSite","@id":"https:\/\/megagon.ai\/jp\/#website","url":"https:\/\/megagon.ai\/jp\/","name":"Megagon Labs","description":"","publisher":{"@id":"https:\/\/megagon.ai\/jp\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/megagon.ai\/jp\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"ja-JP"},{"@type":"Organization","@id":"https:\/\/megagon.ai\/jp\/#organization","name":"Megagon Labs","url":"https:\/\/megagon.ai\/","logo":{"@type":"ImageObject","url":"https:\/\/megagon.ai\/wp-content\/uploads\/2025\/02\/Logo-Megagon-Labs.webp","caption":"Megagon Labs"},"image":{"url":"https:\/\/megagon.ai\/wp-content\/uploads\/2025\/02\/Logo-Megagon-Labs.webp"},"description":"Megagon Labs is an AI research organization conducting research in compound AI systems, large language models, data-AI symbiosis, and human-centered AI. Megagon Labs shares its findings with the broader community through open-source tools, datasets, publications, workshops, and an invited speaker series.","sameAs":["https:\/\/github.com\/megagonlabs","https:\/\/twitter.com\/megagonlabs","https:\/\/www.linkedin.com\/company\/megagon-labs\/","https:\/\/www.facebook.com\/megagonlabs\/"],"address":{"@type":"PostalAddress","streetAddress":"444 Castro Street","addressLocality":"Mountain View","addressRegion":"CA","postalCode":"94041","addressCountry":"US"},"contactPoint":{"@type":"ContactPoint","email":"contactus@megagon.ai","contactType":"general inquiries"},"parentOrganization":{"@type":"Organization","name":"Recruit Holdings","url":"https:\/\/recruit-holdings.com\/en\/"}}]}},"_links":{"self":[{"href":"https:\/\/megagon.ai\/jp\/wp-json\/wp\/v2\/publications\/7294","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/megagon.ai\/jp\/wp-json\/wp\/v2\/publications"}],"about":[{"href":"https:\/\/megagon.ai\/jp\/wp-json\/wp\/v2\/types\/publications"}],"version-history":[{"count":0,"href":"https:\/\/megagon.ai\/jp\/wp-json\/wp\/v2\/publications\/7294\/revisions"}],"wp:attachment":[{"href":"https:\/\/megagon.ai\/jp\/wp-json\/wp\/v2\/media?parent=7294"}],"wp:term":[{"taxonomy":"publications-tags","embeddable":true,"href":"https:\/\/megagon.ai\/jp\/wp-json\/wp\/v2\/publications-tags?post=7294"},{"taxonomy":"conference-year","embeddable":true,"href":"https:\/\/megagon.ai\/jp\/wp-json\/wp\/v2\/conference-year?post=7294"},{"taxonomy":"conference","embeddable":true,"href":"https:\/\/megagon.ai\/jp\/wp-json\/wp\/v2\/conference?post=7294"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}