DreamKG: A KG-Augmented Conversational System for People Experiencing Homelessness

Javad M Alizadeh; Genhui Zheng; Chiu C Tan; Yuzhou Chen; Omar Martinez; Philip McCallion; Ying Ding; Chenguang Yang; AnneMarie Tomosky; Huanmei Wu

arXiv · 2604.11703

DreamKG: A KG-Augmented Conversational System for People Experiencing Homelessness

Javad M Alizadeh·Genhui Zheng·Chiu C Tan·Yuzhou Chen·Omar Martinez·Philip McCallion·Ying Ding·Chenguang Yang·AnneMarie Tomosky·Huanmei Wu

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Abstract

People experiencing homelessness (PEH) face substantial barriers to accessing timely, accurate information about community services. DreamKG addresses this through a knowledge graph-augmented conversational system that grounds responses in verified, up-to-date data about Philadelphia organizations, services, locations, and hours. Unlike standard large language models (LLMs) prone to hallucinations, DreamKG combines Neo4j knowledge graphs with structured query understanding to handle location-aware and time-sensitive queries reliably. The system performs spatial reasoning for distance-based recommendations and temporal filtering for operating hours. Preliminary evaluation shows 59% superiority over Google Search AI on relevant queries and 84% rejection of irrelevant queries. This demonstration highlights the potential of hybrid architectures that combines LLM flexibility with knowledge graph reliability to improve service accessibility for vulnerable populations effectively.

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Javad M Alizadeh, Genhui Zheng, Chiu C Tan, Yuzhou Chen, Omar Martinez, Philip McCallion, Ying Ding, Chenguang Yang, AnneMarie Tomosky, Huanmei Wu. 2026-04-13. DreamKG: A KG-Augmented Conversational System for People Experiencing Homelessness. https://arxiv.org/abs/2604.11703

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