<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Knowledge Rep on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/knowledge-rep/</link><description>Recent content in Knowledge Rep on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/knowledge-rep/index.xml" rel="self" type="application/rss+xml"/><item><title>Commonsense knowledge</title><link>https://terms-en.ai-term-hub.com/en/terms/commonsense_knowledge/</link><pubDate>Sat, 18 Jul 2026 09:50:01 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/commonsense_knowledge/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Commonsense knowledge refers to the vast amount of implicit information about everyday life, physics, social norms, and cause-and-effect relationships that humans acquire naturally. In AI, acquiring this type of knowledge is a significant challenge because it is rarely explicitly stated in training data yet crucial for reasoning. Systems lacking commonsense may fail at simple tasks like understanding that a glass will break if dropped. Projects like ConceptNet and ATOMIC aim to encode these facts to help AI systems interpret context, infer intentions, and make logical deductions similar to human intuition.&lt;/p></description></item><item><title>Graph</title><link>https://terms-en.ai-term-hub.com/en/terms/graph/</link><pubDate>Sat, 18 Jul 2026 09:32:53 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/graph/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>A graph is a fundamental data structure in AI comprising vertices (nodes) and edges (links) that denote relationships. Graph Neural Networks (GNNs) leverage this structure to perform learning on non-Euclidean data, such as social networks or molecular structures. Unlike grid-based data processed by CNNs, graphs allow for irregular connectivity and variable sizes. Graphs are essential for knowledge representation, reasoning, and modeling complex interactions where the relationship between items is as important as the items themselves.&lt;/p></description></item></channel></rss>