mlsanityData to 14 Oct 2026

History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling

arXiv 2306.06872 ↗First posted 12 Jun 2023cs.CLcited by 4

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Abstract (arXiv)

Context information modeling is an important task in conversational KBQA. However, existing methods usually assume the independence of utterances and model them in isolation. In this paper, we propose a History Semantic Graph Enhanced KBQA model (HSGE) that is able to effectively model long-range semantic dependencies in conversation history while maintaining low computational cost. The framework incorporates a context-aware encoder, which employs a dynamic memory decay mechanism and models context at different levels of granularity. We evaluate HSGE on a widely used benchmark dataset for complex sequential question answering. Experimental results demonstrate that it outperforms existing baselines averaged on all question types.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 12 Jun 2023.

Builds on

22 of 36 references.

References · 22

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10 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2024Graph Retrieval-Augmented Generation: A Surveycited by 324
  2. 2023Towards Verifiable Text Generation with Evolving Memory and Self-Reflectioncited by 21
  3. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  4. 2023Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generationcited by 7