  Humankind's understanding of the world is fundamentally linked to our
perception and cognition, with \emph{human languages} serving as one of the
major carriers of \emph{world knowledge}. In this vein, \emph{Large Language
Models} (LLMs) like ChatGPT epitomize the pre-training of extensive,
sequence-based world knowledge into neural networks, facilitating the
processing and manipulation of this knowledge in a parametric space. This
article explores large models through the lens of "knowledge". We initially
investigate the role of symbolic knowledge such as Knowledge Graphs (KGs) in
enhancing LLMs, covering aspects like knowledge-augmented language model,
structure-inducing pre-training, knowledgeable prompts, structured CoT,
knowledge editing, semantic tools for LLM and knowledgeable AI agents.
Subsequently, we examine how LLMs can boost traditional symbolic knowledge
bases, encompassing aspects like using LLM as KG builder and controller,
structured knowledge pretraining, and LLM-enhanced symbolic reasoning.
Considering the intricate nature of human knowledge, we advocate for the
creation of \emph{Large Knowledge Models} (LKM), specifically engineered to
manage diversified spectrum of knowledge structures. This promising undertaking
would entail several key challenges, such as disentangling knowledge base from
language models, cognitive alignment with human knowledge, integration of
perception and cognition, and building large commonsense models for interacting
with physical world, among others. We finally propose a five-"A" principle to
distinguish the concept of LKM.
