Question answering over knowledge graphs (KG-QA) is a vital topic in IR. Questions with temporal intent are a special class of practical importance, but have not received much attention in research. This work presents EXAQT, the first end-to-end system for answering complex temporal questions that have multiple entities and predicates, and associated temporal conditions.
EXAQT answers natural language questions over KGs in two stages, one geared towards high recall, the other towards precision at top ranks. The first step computes question-relevant compact subgraphs within the KG, and judiciously enhances them with pertinent temporal facts, both using fine-tuned BERT models. The second step constructs relational graph convolutional networks (R-GCN) from the first step’s output, and enhances the R-GCNs with time-aware entity embeddings and attention over temporal relations.A simplified excerpt of the relevant zone in the Wikidata KG necessary for answering the question is shown in the figure.
Complex questions of this sort must consider multi-hop constraints (Barack Obama → Malia Obama, Sasha Obama → Sidwell Friends School, University of Chicago Laboratory School), and reason on the overlap of the intersection of the start of the presidency (2009) with the study period at the school (2009 – 2016).
To evaluate EXAQT, We leverage recent community efforts in QA benchmark, and we search through eight KG-QA datasets for time-related questions. The result is a new compilation with about 16, 181 questions called TimeQuestions.
Please refer to our paper for further details.
To know more about our group, please visit https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/question-answering/.