<!doctype html><html lang="en" class="no-js"><head><meta charset="utf-8"> <!-- begin SEO --><title>REMA: Graph Embeddings-based Relational Schema Matching - Marios Fragkoulis</title><meta property="og:locale" content="en-US"><meta property="og:site_name" content="Marios Fragkoulis"><meta property="og:title" content="REMA: Graph Embeddings-based Relational Schema Matching"><link rel="canonical" href="https://mfragkoulis.github.io/publication/2020-03-31-rema.md"><meta property="og:url" content="https://mfragkoulis.github.io/publication/2020-03-31-rema.md"><meta property="og:description" content="Schema matching is the process of capturing correspondence between attributes of different datasets and it is one of the most important prerequisite steps for analyzing heterogeneous data collections. State-of-the-art schema matching algorithms that use simple schema- or instance-based similarity measures struggle with finding matches beyond the trivial cases. Semantics-based algorithms require the use of domain-specific knowledge encoded in a knowledge graph or an ontology. As a result, schema matching still remains a largely manual process, which is performed by few domain experts. In this paper we present the Relational Embeddings MAtcher, or REMA, for short. REMA is a novel schema matching approach which captures semantic similarity of attributes using relational embeddings: a technique which embeds database rows, columns and schema information into multidimensional vectors that can reveal semantic similarity. This paper aims at communicating our latest findings, and at demonstrating rema’s potential with a preliminary experimental evaluation."><meta name="twitter:site" content="@mariofragkoulis"><meta name="twitter:title" content="REMA: Graph Embeddings-based Relational Schema Matching"><meta name="twitter:description" content="Schema matching is the process of capturing correspondence between attributes of different datasets and it is one of the most important prerequisite steps for analyzing heterogeneous data collections. State-of-the-art schema matching algorithms that use simple schema- or instance-based similarity measures struggle with finding matches beyond the trivial cases. Semantics-based algorithms require the use of domain-specific knowledge encoded in a knowledge graph or an ontology. As a result, schema matching still remains a largely manual process, which is performed by few domain experts. In this paper we present the Relational Embeddings MAtcher, or REMA, for short. REMA is a novel schema matching approach which captures semantic similarity of attributes using relational embeddings: a technique which embeds database rows, columns and schema information into multidimensional vectors that can reveal semantic similarity. This paper aims at communicating our latest findings, and at demonstrating rema’s potential with a preliminary experimental evaluation."><meta name="twitter:url" content="https://mfragkoulis.github.io/publication/2020-03-31-rema.md"><meta name="twitter:card" content="summary"><meta property="og:type" content="article"><meta property="article:published_time" content="2020-03-31T00:00:00-07:00"> <script type="application/ld+json"> { "@context" : "http://schema.org", "@type" : "Person", "name" : "Marios Fragkoulis", "url" : "https://mfragkoulis.github.io", "sameAs" : null } </script> <!-- end SEO --><link href="https://mfragkoulis.github.io/feed.xml" type="application/atom+xml" rel="alternate" title="Marios Fragkoulis Feed"> <!-- http://t.co/dKP3o1e --><meta name="HandheldFriendly" content="True"><meta name="MobileOptimized" content="320"><meta name="viewport" content="width=device-width, initial-scale=1.0"> <script> document.documentElement.className = document.documentElement.className.replace(/\bno-js\b/g, '') + ' js '; </script> <!-- For all browsers --><link rel="stylesheet" href="https://mfragkoulis.github.io/assets/css/main.css"><meta http-equiv="cleartype" content="on"> <!-- start custom head snippets --><link rel="apple-touch-icon" sizes="57x57" href="https://mfragkoulis.github.io/images/apple-touch-icon-57x57.png?v=M44lzPylqQ"><link rel="apple-touch-icon" sizes="60x60" href="https://mfragkoulis.github.io/images/apple-touch-icon-60x60.png?v=M44lzPylqQ"><link rel="apple-touch-icon" sizes="72x72" href="https://mfragkoulis.github.io/images/apple-touch-icon-72x72.png?v=M44lzPylqQ"><link rel="apple-touch-icon" sizes="76x76" href="https://mfragkoulis.github.io/images/apple-touch-icon-76x76.png?v=M44lzPylqQ"><link rel="apple-touch-icon" sizes="114x114" href="https://mfragkoulis.github.io/images/apple-touch-icon-114x114.png?v=M44lzPylqQ"><link rel="apple-touch-icon" sizes="120x120" href="https://mfragkoulis.github.io/images/apple-touch-icon-120x120.png?v=M44lzPylqQ"><link rel="apple-touch-icon" sizes="144x144" href="https://mfragkoulis.github.io/images/apple-touch-icon-144x144.png?v=M44lzPylqQ"><link rel="apple-touch-icon" sizes="152x152" href="https://mfragkoulis.github.io/images/apple-touch-icon-152x152.png?v=M44lzPylqQ"><link rel="apple-touch-icon" sizes="180x180" href="https://mfragkoulis.github.io/images/apple-touch-icon-180x180.png?v=M44lzPylqQ"><link rel="icon" type="image/png" href="https://mfragkoulis.github.io/images/favicon-32x32.png?v=M44lzPylqQ" sizes="32x32"><link rel="icon" type="image/png" href="https://mfragkoulis.github.io/images/android-chrome-192x192.png?v=M44lzPylqQ" sizes="192x192"><link rel="icon" type="image/png" href="https://mfragkoulis.github.io/images/favicon-96x96.png?v=M44lzPylqQ" sizes="96x96"><link rel="icon" type="image/png" href="https://mfragkoulis.github.io/images/favicon-16x16.png?v=M44lzPylqQ" sizes="16x16"><link rel="manifest" href="https://mfragkoulis.github.io/images/manifest.json?v=M44lzPylqQ"><link rel="mask-icon" href="https://mfragkoulis.github.io/images/safari-pinned-tab.svg?v=M44lzPylqQ" color="#000000"><link rel="shortcut icon" href="/images/favicon.ico?v=M44lzPylqQ"><meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://mfragkoulis.github.io/images/mstile-144x144.png?v=M44lzPylqQ"><meta name="msapplication-config" content="https://mfragkoulis.github.io/images/browserconfig.xml?v=M44lzPylqQ"><meta name="theme-color" content="#ffffff"><link rel="stylesheet" href="https://mfragkoulis.github.io/assets/css/academicons.css"/> <script type="text/x-mathjax-config"> MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: "all" } } }); </script> <script type="text/x-mathjax-config"> MathJax.Hub.Config({ tex2jax: { inlineMath: [ ['$','$'], ["\\(","\\)"] ], processEscapes: true } }); </script> <script src='https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.4/latest.js?config=TeX-MML-AM_CHTML' async></script> <!-- end custom head snippets --></head><body> <!--[if lt IE 9]><div class="notice--danger align-center" style="margin: 0;">You are using an <strong>outdated</strong> browser. 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State-of-the-art schema matching algorithms that use simple schema- or instance-based similarity measures struggle with finding matches beyond the trivial cases. Semantics-based algorithms require the use of domain-specific knowledge encoded in a knowledge graph or an ontology. As a result, schema matching still remains a largely manual process, which is performed by few domain experts. In this paper we present the Relational Embeddings MAtcher, or REMA, for short. REMA is a novel schema matching approach which captures semantic similarity of attributes using relational embeddings: a technique which embeds database rows, columns and schema information into multidimensional vectors that can reveal semantic similarity. This paper aims at communicating our latest findings, and at demonstrating rema’s potential with a preliminary experimental evaluation."><meta itemprop="datePublished" content="March 31, 2020"><div class="page__inner-wrap"><header><h1 class="page__title" itemprop="headline">REMA: Graph Embeddings-based Relational Schema Matching</h1><p>Published in <i>SEAData workshop in EDBT (short paper)</i>, 2020</p><p>Recommended citation: Christos Koutras, Marios Fragkoulis, Asterios Katsifodimos, Christoph Lofi: REMA: Graph Embeddings-based Relational Schema Matching. EDBT/ICDT Workshops 2020 <a href="http://ceur-ws.org/Vol-2578/SEAData5.pdf"><u>http://ceur-ws.org/Vol-2578/SEAData5.pdf</u></a></p></header><section class="page__content" itemprop="text"></section><footer class="page__meta"></footer><section class="page__share"><h4 class="page__share-title">Share on</h4><a href="https://twitter.com/intent/tweet?text=https://mfragkoulis.github.io/publication/2020-03-31-rema.md" class="btn btn--twitter" title="Share on Twitter"><i class="fa fa-fw fa-twitter" aria-hidden="true"></i><span> Twitter</span></a> <a href="https://www.facebook.com/sharer/sharer.php?u=https://mfragkoulis.github.io/publication/2020-03-31-rema.md" class="btn btn--facebook" title="Share on Facebook"><i class="fa fa-fw fa-facebook" aria-hidden="true"></i><span> Facebook</span></a> <a href="https://plus.google.com/share?url=https://mfragkoulis.github.io/publication/2020-03-31-rema.md" class="btn btn--google-plus" title="Share on Google Plus"><i class="fa fa-fw fa-google-plus" aria-hidden="true"></i><span> Google+</span></a> <a href="https://www.linkedin.com/shareArticle?mini=true&url=https://mfragkoulis.github.io/publication/2020-03-31-rema.md" class="btn btn--linkedin" title="Share on LinkedIn"><i class="fa fa-fw fa-linkedin" aria-hidden="true"></i><span> LinkedIn</span></a></section><nav class="pagination"> <a href="https://mfragkoulis.github.io/publication/2019-08-31-statful-functions.md" class="pagination--pager" title="Stateful functions as a service in action ">Previous</a> <a href="https://mfragkoulis.github.io/publication/2020-06-17-beyond-analytics.md" class="pagination--pager" title="Beyond Analytics: the Evolution of Stream Processing Systems ">Next</a></nav></div></article></div></script><div class="page__footer"><footer> <!-- start custom footer snippets --> <!-- end custom footer snippets --><div class="page__footer-follow"><ul class="social-icons"><li><strong>Follow:</strong></li><li><a href="https://twitter.com/mariofragkoulis"><i class="fa fa-fw fa-twitter-square" aria-hidden="true"></i> Twitter</a></li><li><a href="http://github.com/mfragkoulis"><i class="fa fa-fw fa-github" aria-hidden="true"></i> GitHub</a></li><li><a href="https://mfragkoulis.github.io/feed.xml"><i class="fa fa-fw fa-rss-square" aria-hidden="true"></i> Feed</a></li></ul></div><div class="page__footer-copyright">&copy; 2025 Marios Fragkoulis. 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