论实体与事件层级概念化在可泛化推理中的作用：任务、方法、应用与未来方向综述
On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions
AI总结：
本文针对概念化定义不一致且缺乏系统综述的问题，提出四层级分类框架，并基于150余篇论文首次系统综述实体与事件层级概念化的定义、资源、方法及应用，同时展望未来研究方向。
摘要：
概念化是人类认知的基本要素，在人类可泛化推理中发挥着关键作用。一般而言，它指的是将具体实例逐步抽象为更高层级的概念，进而形成可应用于陌生或新颖情境的抽象知识的过程。这一过程能够增强模型的推理能力，并支持知识在不同领域间的有效迁移。尽管概念化具有重要意义，但由于该术语内涵宽泛，不同研究对其理解存在不一致，因为存在不同类型的实例，且可以通过多种多样的方式进行抽象。同时，目前也缺乏一个系统性的综述，来全面审视现有工作中关于概念化的定义、执行方式及其在增强推理任务中的应用。在本文中，我们首先根据被概念化的实例类型，将不同类型的概念化划分为四个层级，以澄清该术语并界定本文的研究范围，从而填补上述空白。随后，我们呈现了首篇涵盖150余篇论文的全面综述，将与概念化相关的各种定义、资源、方法和下游应用归纳为一个统一的分类体系，并重点关注实体层级和事件层级。此外，我们阐明了该领域潜在的未来研究方向，并希望引起学术界更多的关注。
英文摘要：
Conceptualization, a fundamental element of human cognition, plays a pivotal role in human generalizable reasoning. Generally speaking, it refers to the process of sequentially abstracting specific instances into higher-level concepts and then forming abstract knowledge that can be applied in unfamiliar or novel situations. This enhances models' inferential capabilities and supports the effective transfer of knowledge across various domains. Despite its significance, the broad nature of this term has led to inconsistencies in understanding conceptualization across various works, as there exists different types of instances that can be abstracted in a wide variety of ways. There is also a lack of a systematic overview that comprehensively examines existing works on the definition, execution, and application of conceptualization to enhance reasoning tasks. In this paper, we address these gaps by first proposing a categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized, in order to clarify the term and define the scope of our work. Then, we present the first comprehensive survey of over 150 papers, surveying various definitions, resources, methods, and downstream applications related to conceptualization into a unified taxonomy, with a focus on the entity and event levels. Furthermore, we shed light on potential future directions in this field and hope to garner more attention from the community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
