{"id":819,"date":"2024-01-18T09:11:42","date_gmt":"2024-01-18T17:11:42","guid":{"rendered":"https:\/\/linguamonium.com\/?p=819"},"modified":"2024-01-18T09:11:42","modified_gmt":"2024-01-18T17:11:42","slug":"plugging-the-holes-of-ai-with-knowledge-graph-goodness","status":"publish","type":"post","link":"https:\/\/linguamonium.com\/?p=819","title":{"rendered":"Plugging the holes of AI with Knowledge Graph goodness"},"content":{"rendered":"<p>Think large language models know it all? Then I\u2019d like to introduce Mike Dillinger, an acquaintance of mine in the tech space. Mike is a Knowledge Graph Architect. Until recently, he worked as Technical Lead for knowledge graph (KG) initiatives at LinkedIn; he\u2019s a wiz in the world of taxonomies, ontologies, and KGs. And he\u2019s a strong proponent of <em>better <\/em>data over <em>more <\/em>data: \u201ceven the most sophisticated algorithms for machine intelligence perform unreliably if they don&#8217;t use high-quality data enriched by human expertise.\u201d<\/p>\n<p>Mike has written a bunch of great articles over the past year, and I\u2019ve found his insights illuminating in these strange ChatGPT times. Plus, his pieces are fun to read \u2013 he\u2019s a talented writer.<\/p>\n<p>Two articles I\u2019d like to highlight:<\/p>\n<ol>\n<li><a href=\"https:\/\/www.linkedin.com\/pulse\/knowledge-graphs-fancy-databases-mike-dillinger-phd-fk8jc\/?trackingId=Yxx%2FtZFxS%2BaFWrYGRX6l3A%3D%3D\" target=\"_blank\" rel=\"noopener\">Knowledge Graphs as Fancy Databases<\/a>\n<ul>\n<li>For most people (i.e. non-CEOs), you can skip to the section \u201cOK, so what is this graph thingy?\u201d<\/li>\n<li>There he breaks down, in a clear, digestible way, with examples, what a knowledge graph is and why it\u2019s useful.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<ol start=\"2\">\n<li><a href=\"https:\/\/medium.com\/@mike.dillinger\/knowledge-graphs-enable-llms-to-really-understand-9e99466e2da2\" target=\"_blank\" rel=\"noopener\">Knowledge graphs enable LLMs to really understand<\/a>\n<ul>\n<li>This article discusses the limitations of large language models (LLMs).<\/li>\n<li>It posits that knowledge graphs and other contextual domain knowledge play an important role in \u201c<em>whether and how <\/em>LLMs actually understand anything.\u201d<\/li>\n<li>One of the key takeaways is about <u>meaningless<\/u> <strong>string manipulation<\/strong>:\n<ul>\n<li>\u201cThe early versions of LLMs like ChatGPT did little more than string manipulation at a massive scale.\u201d This was a \u201cbreakthrough for computational linguistics\u201d\u2026however, we manipulate strings and other stimuli all the time \u201cwithout [\u2026] having the slightest idea of what concepts or meanings might be associated with them.\u201d<\/li>\n<\/ul>\n<\/li>\n<li>Fun examples abound: written Mongolian, chemistry-speak, a confusing-but-later-clarified story about sound and balloons.<\/li>\n<li>Finally, the article argues for the necessity of <strong>grounding<\/strong> strings in concepts and knowledge (e.g. KGs).<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<p>If you like those, you can subscribe to Mike\u2019s <a href=\"https:\/\/www.linkedin.com\/newsletters\/knowledge-architecture-7149627777497841664\/\" target=\"_blank\" rel=\"noopener\">newsletter<\/a> and see his full set of writings <a href=\"https:\/\/www.linkedin.com\/in\/mikedillinger\/recent-activity\/articles\/\" target=\"_blank\" rel=\"noopener\">here<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>Photo attribution: <a href=\"https:\/\/unsplash.com\/@alinnnaaaa?utm_content=creditCopyText&amp;utm_medium=referral&amp;utm_source=unsplash\" target=\"_blank\" rel=\"noopener\">Alina Grubnyak<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Think large language models know it all? Then I\u2019d like to introduce Mike Dillinger, an acquaintance of mine in the tech space. Mike is a Knowledge Graph Architect. Until recently, he worked as Technical Lead for knowledge graph (KG) initiatives at LinkedIn; he\u2019s a wiz in the world of taxonomies, ontologies, and KGs. And he\u2019s&#8230;<\/p>\n","protected":false},"author":1,"featured_media":821,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"footnotes":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[20],"tags":[38,239,202,63,237,238,203,165],"class_list":["post-819","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-nlu-nlp-ml-ai","tag-ai","tag-artificial-intelligence","tag-chatgpt","tag-context","tag-knowledge-graph","tag-large-language-models","tag-llms","tag-understanding"],"jetpack_publicize_connections":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Plugging the holes of AI with Knowledge Graph goodness - Linguamonium<\/title>\n<meta name=\"description\" content=\"Knowledge graphs improve LLMs by adding contextual domain knowledge. 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