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Context information modeling is an important task in conversational KBQA.However, existing methods usually assume the independence of utterances andmodel them in isolation. In this paper, we propose a History Semantic GraphEnhanced KBQA model (HSGE) that is able to effectively model long-rangesemantic dependencies in conversation history while maintaining lowcomputational cost. The framework incorporates a context-aware encoder, whichemploys a dynamic memory decay mechanism and models context at different levelsof granularity. We evaluate HSGE on a widely used benchmark dataset for complexsequential question answering. Experimental results demonstrate that itoutperforms existing baselines averaged on all question types.

History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling