Semantic Knowledge Graphs (SKG) face challenges with scalability, flexibility, contextual understanding, and handling unstructured or ambiguous information. However, they offer formal and structured knowledge enabling highly interpretable and reliable results by means of reasoning and querying. Large Language Models (LLMs) overcome those limitations making them suitable in open-ended tasks and unstructured environments. Nevertheless, LLMs are neither interpretable nor reliable. To solve the dichotomy between LLMs and SKGs we envision Logic Augmented Generation (LAG) that combines the benefits of the two worlds. LAG uses LLMs as Reactive Continuous Knowledge Graphs that can generate potentially infinite relations and tacit knowledge on-demand. SKGs are key for injecting a discrete heuristic dimension with clear logical and factual boundaries. We exemplify LAG in two tasks of collective intelligence, i.e., medical diagnostics and climate projections. Understanding the properties and limitations of LAG, which are still mostly unknown, is of utmost importance for enabling a variety of tasks involving tacit knowledge in order to provide interpretable and effective results.

翻译：语义知识图谱（SKG）面临着可扩展性、灵活性、上下文理解以及处理非结构化或模糊信息的挑战。然而，它们提供了形式化和结构化的知识，能够通过推理和查询实现高度可解释且可靠的结果。大型语言模型（LLM）克服了这些限制，使其适用于开放式任务和非结构化环境。然而，LLM既不可解释也不可靠。为解决LLM与SKG之间的二分困境，我们提出了逻辑增强生成（LAG）的构想，它结合了两者的优势。LAG将LLM用作反应式连续知识图谱，能够按需生成潜在无限的关系和隐性知识。SKG则对于注入具有清晰逻辑与事实边界的离散启发式维度至关重要。我们通过集体智能的两个任务——医疗诊断和气候预测——来例证LAG。理解LAG的性质与局限（目前大多仍属未知）对于实现涉及隐性知识的各类任务以提供可解释且有效的结果至关重要。