# From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems

arXiv: https://arxiv.org/abs/2607.00003 · HTML: https://mlsanity.com/paper/2607.00003/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 18 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://mlsanity.com/paper/2002.08910/) (2020)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph](https://mlsanity.com/paper/2402.13750/) (2024)
- [LKPNR: LLM and KG for Personalized News Recommendation Framework](https://mlsanity.com/paper/2308.12028/) (2023)
- [An Ecosystem for Personal Knowledge Graphs: A Survey and Research Roadmap](https://mlsanity.com/paper/2304.09572/) (2023)
- [Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?](https://mlsanity.com/paper/2309.17122/) (2023)
- [Applying Personal Knowledge Graphs to Health](https://mlsanity.com/paper/2104.07587/) (2021)
- [Personal Health Knowledge Graph for Clinically Relevant Diet Recommendations](https://mlsanity.com/paper/2110.10131/) (2021)
- [Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction](https://mlsanity.com/paper/2312.03022/) (2023)
- [Empirical Analysis of Dialogue Relation Extraction with Large Language Models](https://mlsanity.com/paper/2404.17802/) (2024)
- [Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences](https://mlsanity.com/paper/2503.22005/) (2025)
- [Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations](https://mlsanity.com/paper/2603.13264/) (2026)

## Abstract (arXiv)

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.