From "Strings" to "Things" for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems
arXiv:2607.00003v1 Announce Type: cross Abstract: 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
The proliferation of conversational AI and the need for personalized yet privacy-preserving user models are driving the necessity for efficient personal knowledge graph construction.
This work directly addresses a core challenge in making AI more personalized and useful by enabling structured data extraction from unstructured interactions, which is crucial for advanced recommendation systems and agentic AI.
The ability to reliably and efficiently convert unstructured conversational data into structured personal knowledge graphs changes how user preferences can be modeled and utilized by AI systems, moving beyond simple keyword matching.
- · AI platform developers
- · Recommendation system providers
- · Data privacy advocates
- · Personalized AI service providers
- · Traditional, privacy-invasive data collection methods
- · Static user profiling systems
More accurate and personalized AI recommendations and interactions become feasible.
Reduced reliance on centralized, opaque data silos for user preference modeling, enhancing privacy.
Accelerated development of truly autonomous AI agents capable of understanding and acting on individual user context over time.
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Read at arXiv cs.AI