Question Answering Over Temporal Knowledge Graphs
By Apoorv Saxena, Soumen Chakrabarti, Partha P. Talukdar. 2021
203 citations recorded by the source corpus.
Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area. Lack of broad coverage datasets has been another factor limiting progress in this area. We address this challenge by presenting CRONQUESTIONS, the largest known Temporal KGQA dataset, clearly stratified into buckets of structural complexity. CRONQUESTIONS expands the only known previous dataset by a factor of 340x. We find that various state-of-the-art KGQA methods fall far short of the desired performance on this new dataset. In response, we also propose CRONKGQA, a transformer-based solution that exploits recent advances in Temporal KG embeddings, and achieves performance superior to all baselines, with an increase of 120% in accuracy over the next best performing method. Through extensive experiments, we give detailed insights into the workings of CRONKGQA, as well as situations where significant further improvements appear possible. In addition to the dataset, we have released our code as well.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- William W. Cohen
- Bhuwan Dhingra
- Mohit Bansal
- Eunsol Choi
- Jacob Eisenstein
- Michael J.Q. Zhang
- Luke S. Zettlemoyer
- Percy Liang
In Natural Language Processing
- William W. Cohen
- Bhuwan Dhingra
- Jacob Eisenstein
- Eunsol Choi
- Michael J.Q. Zhang
- Julian Martin Eisenschlos
- D. Gillick
- Mohit Bansal
Citations in the corpus
- Time-Aware Language Models as Temporal Knowledge Bases (2021, methodology): “T EMP LAMA is similar in spirit to KB-QA benchmarks which focus on temporal reasoning such as TempQuestions (Jia et al., 2018) and Cron-Questions (Saxena et al., 2021).”
- SituatedQA: Incorporating Extra-Linguistic Contexts into QA (2021, background): “Recent work has also studied generating temporally-dependent questions with timestamped answers from temporal knowledge-bases (Chen et al., 2021; Saxena et al., 2021).”