Publikation
Using Semantic-based Adaptive Relevance Prediction to Enhance Entity Recommendation for Personal Knowledge Assistance
Mahta Bakhshizadeh; Heiko Maus; Andreas Dengel
In: Proceedings of the sixth Knowledge-aware and Conversational Recommender Systems Workshop. Knowledge-aware and Conversational Recommender Systems Workshop (KaRS-2024), October 18, Bari, Italy, Association for Computing Machinery, 2024.
Zusammenfassung
Personal knowledge assistance tools are designed to support knowledge work by delivering contextually relevant information and recommendations, thereby enhancing productivity and decision-making. Entity recommendation is a form of knowledge assistance that suggests relevant entities commonly sourced from public knowledge bases, like DBpedia, based on user context to improve productivity in daily digital tasks. In this study, we explore which similarity metrics within RDF2Vec knowledge graph embedding are most effective at capturing users' personal interpretations of entity similarities within their specific contexts. Accordingly, we propose a semantic-based recommendation method that includes an adaptive relevance prediction module to dynamically evaluate entity relevance by incorporating user feedback. Our approach is benchmarked on RLKWiC, a publicly available dataset of real-life knowledge work in context, and demonstrated a twenty percent improvement over the established baseline for entity recommendation, highlighting its potential to enhance knowledge work support.
Projekte
- CoMem - Corporate Memory
- Managed_Forgetting_Phase_2 - Nachhaltige evolutionäre Unternehmensgedächtnisse: Methoden und Effekte von Managed Forgetting für die administrative Wissensarbeit
- SensAI - Self-organizing Personal Knowledge Assistants in Evolving Corporate Memories