Towards Multi-Relational Multi-Hop Reasoning over Dense Temporal Knowledge Graphs (2024)

Abstract: Temporal knowledge graph reasoning has emerged as a crucial task for answering time-dependent questions within a knowledge graph (KG).Despite tremendous progress, the present research is impeded by the sparsity of a temporal KG and an over-reliance on simple single-relational reasoning patterns. To overcome these challenges, we introduce MulQuestions, a new temporal KG reasoning benchmark featuring over 200k entities and 960k questions designed to facilitate complex, multi-relational and multi-hop reasoning. Additionally, we propose a new model adept at conducting pattern-aware and time-sensitive reasoning across temporal KGs. The model’s efficacy is confirmed through rigorous evaluations, showcasing its effectiveness in sparse data conditions and adeptness at handling questions with long reasoning chains. We have made our benchmark and model publicly accessible at [https://anonymous].

## introduction temporal knowledge graph reasoning involves the task of answering questions related to timedependent facts within a knowledge graph (kg) @xcite @xcite @xcite . for example, given two facts with time duration: (barack obama, position, president of us, @xcite @xcite @xcite @xcite @xcite and (george w. bush, position, president of @xcite , for a time-related question "who was the president of the united states before barack obama?", a model for this task should infer that the answer is "george w. bush". recent benchmarks such as @xcite and mul-titq @xcite have greatly improved this task and expanded its applicability. despite notable progress, the current study on temporal knowledge graph reasoning faces challenges of the sparsity of kgs and oversimplified reasoning patterns. the cronquestions benchmark @xcite , for example, uses templates with only five temporal relations to generate questions -this produces a sparse reasoning graph after filtering the original kgs and can significantly ease the reasoning. furthermore, it is worth noting that a substantial proportion (84.5%) of the questions in previous benchmarks @xcite involve only single-relational reasoning patterns, as shown in the top of figure 1 . in such cases, the reasoning is simplified because only one relation is involved, and the answer is sure to have the same relation shared with the two entities that appear in the questions. there remains a lack of evaluations on complex multi-relational and multi-hop reasoning patterns, as depicted in the bottom of figure 1 . this work focuses on multi-relational multi-hop reasoning in a densely connected temporal knowledge graph. as our first contribution, we developed a new benchmark named mul 2 questions, which contains over 200k entities spanning a temporal kg that is a strongly connected graph created using a breadth-first graph-expanding strategy. moreover, the benchmark also includes an extensive collection of temporal questions (over 960k) that require complex multi-relational multi-hop reasoning patterns, as illustrated in figure 1 (bottom), by incorporating 135 distinct temporal relations. in addition to the data contribution, we present a new model for temporal knowledge graph reasoning that incorporates pattern-aware and timesensitive joint reasoning methods. through rigorous evaluations, we show that our methods outperforms existing methods by a substantial margin (+9 points) in overall accuracy. furthermore, it performs well in data-scarce situations and addressing questions with long reasoning chains. in summary, our contributions are three-fold: ## the mul 2 questions benchmark our mul 2 questions benchmark targets multirelational multi-hop reasoning particularly, and we divide the construction process by temporal kg construction ( § 3.1) and temporal question construction ( § 3.2) as visualized in figure 2 . ## our temporal reasoning model we propose a new model featuring discerning different reasoning patterns over a temporal kg. as shown in figure 4 , our model consists of three essential parts: 1) reasoning subgraph extraction, 2) reasoning pattern characterization, and 3) patternaware joint reasoning. ## conclusion in this research, we investigate multi-relational multi-hop reasoning over a dense temporal knowledge graph. we introduce mul 2 questions, a new benchmark that includes over 200k entities and 960k questions with multi-relational multi-hop patterns. in addition, we present a new model capable of performing pattern-aware and time-sensitive joint reasoning over temporal kgs. the effectiveness has been verified by extensive evaluations. in the future, we would examine its applicability in a broader range of domains/scenarios.