用轻量大模型从对话中提取用户偏好三元组，构建隐私保护推荐知识图谱
From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems
- 用Qwen和Gemma模型从对话文本中提取带Wikidata标识的结构化三元组
- 部分模型在三元组提取和推荐任务中均表现良好，且下游性能与抽取能力正相关
- 为隐私友好型个性化推荐提供可复现的构建方案，适合关注数据安全的研究者
个人知识图谱（PKGs）为建模用户偏好提供了隐私保护框架，但如何从非结构化、分散的对话数据中构建仍具挑战。本文提出一种可复现的流水线，利用轻量级大语言模型将对话中的“字符串”转化为语义“实体”，从中提取符合RDF规范、链接至Wikidata标识的用户偏好三元组，用于构建个人知识图谱。评估了基于Qwen和Gemma的模型在对话数据中提取三元组的能力，并检验生成图谱在下游推荐任务中的实用性。结果表明，某些模型在三元组提取与推荐性能之间表现出良好的一致性，其下游表现与其抽取能力成比例。该方法为隐私敏感场景下的个性化推荐提供了可行路径。
原文摘要 · Abstract (English)
Personal Knowledge Graphs (PKGs) offer a privacy-preserving framework for modeling user preferences, yet constructing them from unstructured, decentralized conversational data remains a challenge. This paper bridges the gap between conversational "strings" and semantic "things" by presenting a reproducible pipeline for extracting structured user-preference triples using lightweight Large Language Models. We evaluate Qwen- and Gemma-based models on their ability to extract RDF-compliant triples linked to Wikidata identifiers from conversational data for PKG construction. Our evaluation assesses both the semantic extraction fidelity and the utility of the resulting graphs in a downstream recommendation task. We found that certain models performed well and had proportionally high downstream performance relative to their triple extraction performance.
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