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CogChat: Knowledge Graph-Augmented Conversational AI with Heterogeneous Graph Transformer for Cognitive Grounding in Design Generation — arXiv:2608.13216

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.13216
License (as archived): CC0-1.0
Archived: 2026-10-19T04:26:30.840Z
SHA-256 of the archived text: 933b0e26f16aed33e5c064b1e5923075d28bcedfcffbdf87f3d3bff1afcf575b
Stable URL of this page: https://metraipolis.com/documento/dc_46bfbd997cc3

How to cite

CogChat: Knowledge Graph-Augmented Conversational AI with Heterogeneous Graph Transformer for Cognitive Grounding in Design Generation — arXiv:2608.13216 — arXiv (en), https://arxiv.org/abs/2608.13216 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_46bfbd997cc3 (Metraipolis archive dc_46bfbd997cc3, sha256 933b0e26f16aed33e5c064b1e5923075d28bcedfcffbdf87f3d3bff1afcf575b)

BibTeX

@misc{dc46bfbd997cc3,
  title = {CogChat: Knowledge Graph-Augmented Conversational AI with Heterogeneous Graph Transformer for Cognitive Grounding in Design Generation — arXiv:2608.13216},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.13216},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_46bfbd997cc3; sha256 933b0e26f16aed33e5c064b1e5923075d28bcedfcffbdf87f3d3bff1afcf575b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.13216. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

LLM-based chat systems have become valuable tools for design practice, enabling rapid ideation and flexible task support. Yet these systems process designer utterances as generic sequences, maintaining context through recency rather than through any model of how the speaker organizes knowledge. In design conversation, this gap compounds as relational context decays between turns, identical words go unresolved across designers, and the conversation loops or restarts rather than deepens. We present CogChat, a real-time chat framework that grounds conversational AI in a personal heterogeneous knowledge graph constructed from each designer's input. The system extracts typed entities and relations into a heterogeneous graph, then applies a HGT (Heterogeneous Graph Transformer) to select structurally relevant nodes for response generation and to generate both intentional and exploratory probing questions. Technical evaluation shows that HGT-based entity selection outperforms both ungrounded LLM interaction and naive KG augmentation, which introduces noise that degrades response quality. A within-subjects study with nine professional designers indicates that grounding conversation in a relationally structured, designer-specific semantic context improves context retention, personalized intent interpretation, and conversational depth while reducing cognitive load. These findings suggest that structuring a designer's expressed concepts and relations as a dynamic knowledge graph can preserve relational context that fades across turns, pointing toward a graph-grounded approach to long-term context management in LLM-based interaction.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-14 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2608.13216 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T04:20:15.347Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-08-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).