PrimeNet:基于概念原型的常识性知识表示和推理框架

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qian Liu, Sooji Han, Erik Cambria, Yang Li, Kenneth Kwok
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引用次数: 0

摘要

常识性知识的获取和表征是人工智能(AI)的核心课题,对于构建更复杂、更像人类的人工智能系统至关重要。然而,现有的常识知识库以孤立的方式组织事实,就像一袋袋事实,缺乏人类通常拥有的认知层面的联系。人类有能力通过使用一套有限的概念基元(作为推理的基本构件)来连接或概括概念,从而有效地组织大量知识。这些概念基元是人类用来理解世界的基本思维元素。通过组合和重组这些基元,人们可以构建复杂的想法、解决问题并理解新概念。为了模拟这种认知机制,我们设计了一个新的常识知识库,称为 PrimeNet,以三层结构组织:一小部分概念基元核心(如 "食物")、与这些基元相连的更大概念集(如 "水果")以及与这些概念相连的更大实体层(如 "香蕉")。首先,我们收集常识性知识,并采用逐步扩展的策略进行知识整合。经过细化,PrimeNet 包含 200 万个节点之间的 600 万条边,以及 34 种不同类型的关系。然后,我们利用概率分类法设计了一种新的概念化方法,以构建 PrimeNet 的概念层。最后,我们进行基元检测以构建基元层,其中使用词汇替换任务来识别相关概念,并使用大型语言模型生成合理的基元来标记每个概念簇,同时验证基元检测过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

PrimeNet: A Framework for Commonsense Knowledge Representation and Reasoning Based on Conceptual Primitives

PrimeNet: A Framework for Commonsense Knowledge Representation and Reasoning Based on Conceptual Primitives

Commonsense knowledge acquisition and representation is a core topic in artificial intelligence (AI), which is crucial for building more sophisticated and human-like AI systems. However, existing commonsense knowledge bases organize facts in an isolated manner like bag of facts, lacking the cognitive-level connections that humans commonly possess. People have the ability to efficiently organize vast amounts of knowledge by linking or generalizing concepts using a limited set of conceptual primitives that serve as the fundamental building blocks of reasoning. These conceptual primitives are basic, foundational elements of thought that humans use to make sense of the world. By combining and recombining these primitives, people can construct complex ideas, solve problems, and understand new concepts. To emulate this cognitive mechanism, we design a new commonsense knowledge base, termed PrimeNet, organized in a three-layer structure: a small core of conceptual primitives (e.g., FOOD), a bigger set of concepts that connect to such primitives (e.g., fruit), and an even larger layer of entities connecting to the concepts (e.g., banana). First, we collect commonsense knowledge and employ a gradual expansion strategy for knowledge integration. After refinement, PrimeNet contains 6 million edges between 2 million nodes, with 34 different types of relations. Then, we design a new conceptualization method by leveraging a probabilistic taxonomy, to build the concept layer of PrimeNet. Finally, we conduct primitive detection to build the primitive layer, where a lexical substitution task is used to identify related concepts, and large language models are employed to generate a rational primitive to label each concept cluster as well as verify the primitive detection process.

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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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