Understanding users' intentions in e-commerce platforms requires commonsense knowledge. In this paper, we present FolkScope, an intention knowledge graph construction framework to reveal the structure of humans' minds about purchasing items. As commonsense knowledge is usually ineffable and not expressed explicitly, it is challenging to perform information extraction. Thus, we propose a new approach that leverages the generation power of large language models~(LLMs) and human-in-the-loop annotation to semi-automatically construct the knowledge graph. LLMs first generate intention assertions via e-commerce-specific prompts to explain shopping behaviors, where the intention can be an open reason or a predicate falling into one of 18 categories aligning with ConceptNet, e.g., IsA, MadeOf, UsedFor, etc. Then we annotate plausibility and typicality labels of sampled intentions as training data in order to populate human judgments to all automatic generations. Last, to structurize the assertions, we propose pattern mining and conceptualization to form more condensed and abstract knowledge. Extensive evaluations and studies demonstrate that our constructed knowledge graph can well model e-commerce knowledge and have many potential applications.

翻译：理解电商平台中用户的意图需要常识知识。本文提出FolkScope，一种意图知识图谱构建框架，旨在揭示人类关于购买物品的思维结构。由于常识知识通常难以言表且不直接表达，进行信息抽取颇具挑战。为此，我们提出一种新方法，利用大语言模型（LLMs）的生成能力与人在回路标注，半自动构建知识图谱。首先，LLMs通过电商专用提示生成意图断言以解释购物行为，其中意图可以是开放理由或18类谓词之一，这些谓词与ConceptNet对齐（如IsA、MadeOf、UsedFor等）。随后，我们标注采样意图的合理性与典型性标签作为训练数据，将人类判断推广至所有自动生成结果。最后，为将断言结构化，我们提出模式挖掘与概念化方法，形成更凝练抽象的知识。大量评估与研究表明，我们构建的知识图谱能有效建模电商知识，并具有众多潜在应用。