{"version":"1.0","provider_name":"Towards Data Science","provider_url":"https:\/\/towardsdatascience.com","author_name":"TDS Editors","author_url":"https:\/\/towardsdatascience.com\/author\/towardsdatascience\/","title":"How to Implement Knowledge Graphs and Large Language Models (LLMs) Together at the Enterprise Level | Towards Data Science","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"a2xFm1T7Nw\"><a href=\"https:\/\/towardsdatascience.com\/how-to-implement-knowledge-graphs-and-large-language-models-llms-together-at-the-enterprise-level-cf2835475c47\/\">How to Implement Knowledge Graphs and Large Language Models (LLMs) Together at the Enterprise Level<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/towardsdatascience.com\/how-to-implement-knowledge-graphs-and-large-language-models-llms-together-at-the-enterprise-level-cf2835475c47\/embed\/#?secret=a2xFm1T7Nw\" width=\"600\" height=\"338\" title=\"&#8220;How to Implement Knowledge Graphs and Large Language Models (LLMs) Together at the Enterprise Level&#8221; &#8212; Towards Data Science\" data-secret=\"a2xFm1T7Nw\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\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<\/script>\n","thumbnail_url":"https:\/\/towardsdatascience.com\/wp-content\/uploads\/2025\/03\/1_oGOdeRp0RUn5rw7Bop88bw.webp","thumbnail_width":720,"thumbnail_height":720,"description":"Large Language Models&nbsp;(LLMs) and&nbsp;Knowledge Graphs&nbsp;(KGs) are different ways of providing more people access to data. KGs use semantics to connect datasets via their meaning i.e. the entities they are representing. LLMs use vectors and deep neural networks to predict natural language. They are often both aimed at \u2018unlocking\u2019 data. For enterprises implementing KGs, the end [&hellip;]"}