ChronoGrapher: Event-Centric Knowledge Graph Construction via Informed Graph Traversal

Event-centric knowledge graphs help enhance coherence to otherwise fragmented and overwhelming data by establishing causal and temporal connections using relevant data. We address the challenge of automatically constructing event-centric knowledge graphs from generic ones. We present ChronoGrapher, a two-step system to build an event-centric knowledge graph from grand events such as the French Revolution. First, a pruned, semantically informed best-first search traversal retrieves a subgraph from large, open-domain knowledge graphs. We define event-centric filters to prune the search space and a heuristic ranking to prioritize nodes like events. Second, we combine a structured triple enrichment method with a text-based triple enrichment method to build event-centric knowledge graphs. ChronoGrapher demonstrates adaptability across datasets like DBpedia and Wikidata, outperforming approaches from the literature. Furthermore, it is designed to be flexible and to operate over any knowledge graph accessible through Header, Dictionary, and Triples dumps or SPARQL endpoints. To evaluate the utility of these constructed graphs, we conduct a preliminary user study comparing different prompting techniques for event-centric question-answering. Our results demonstrate that prompts enriched with event-centric knowledge graph triples yield more factual answers, measured by how well answers are grounded in source information, than those enriched with generic triples or base prompts, while preserving succinctness and relevance.