{"version":"1.0","provider_name":"Microsoft Research","provider_url":"https:\/\/www.microsoft.com\/en-us\/research","author_name":"Darren Edge","author_url":"https:\/\/www.microsoft.com\/en-us\/research\/people\/daedge\/","title":"Making Sense of Search: Using Graph Embedding and Visualization to Transform Query Understanding - Microsoft Research","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"7hX13fZmzS\"><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/making-sense-of-search-using-graph-embedding-and-visualization-to-transform-query-understanding\/\">Making Sense of Search: Using Graph Embedding and Visualization to Transform Query Understanding<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/making-sense-of-search-using-graph-embedding-and-visualization-to-transform-query-understanding\/embed\/#?secret=7hX13fZmzS\" width=\"600\" height=\"338\" title=\"&#8220;Making Sense of Search: Using Graph Embedding and Visualization to Transform Query Understanding&#8221; &#8212; Microsoft Research\" data-secret=\"7hX13fZmzS\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/* ]]> *\/\n<\/script>\n","description":"We present a suite of interfaces for the visual exploration and evaluation of graph embeddings \u2013 machine learning models that reveal implicit relationships not directly observed in the input graph. Our focus is on the embedding of navigation graphs induced from search engine query logs, and how visualization of similar queries across different embeddings, combined [&hellip;]"}