Qian Liu, Sooji Han, Erik Cambria, Yang Li, Kenneth Kwok
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引用次数: 0
Abstract
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.
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.