  Answering factual questions with temporal intent over knowledge graphs
(temporal KGQA) attracts rising attention in recent years. In the generation of
temporal queries, existing KGQA methods ignore the fact that some intrinsic
connections between events can make them temporally related, which may limit
their capability. We systematically analyze the possible interpretation of
temporal constraints and conclude the interpretation structures as the Semantic
Framework of Temporal Constraints, SF-TCons. Based on the semantic framework,
we propose a temporal question answering method, SF-TQA, which generates query
graphs by exploring the relevant facts of mentioned entities, where the
exploring process is restricted by SF-TCons. Our evaluations show that SF-TQA
significantly outperforms existing methods on two benchmarks over different
knowledge graphs.
