A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases
Knowledge Base Question Answering (KBQA) tasks that involve complex reasoning are emerging as an important research direction. However, most existing KBQA datasets focus primarily on generic multi-hop reasoning over explicit facts, largely ignoring other reasoning types such as temporal, spatial, and taxonomic reasoning. In this paper, we present a benchmark dataset for temporal reasoning, TempQA-WD, to encourage research in extending the present approaches to target a more challenging set of complex reasoning tasks. Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata. The TempQA-WD dataset is available at https://github.com/IBM/tempqa-wd.
Code (0)
등록된 구현이 없습니다.
Tasks
Knowledge Base Question AnsweringQuestion AnsweringSemantic ParsingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases
Knowledge Base Question Answering (KBQA) tasks that in-volve complex reasoning are emerging as an important re-search direction. However, most KBQA systems struggle withgeneralizability, particularly on two dimensions: (…
Knowledge Base Question AnsweringQuestion AnsweringQuestion Calibration and Multi-Hop Modeling for Temporal Question Answering
Many models that leverage knowledge graphs (KGs) have recently demonstrated remarkable success in question answering (QA) tasks. In the real world, many facts contained in KGs are time-constrained thus temporal KGQA has …
Knowledge GraphsMulti-hop Question AnsweringQuestion AnsweringTVQA+: Spatio-Temporal Grounding for Video Question Answering
We present the task of Spatio-Temporal Video Question Answering, which requires intelligent systems to simultaneously retrieve relevant moments and detect referenced visual concepts (people and objects) to answer natural…
Question AnsweringVideo Question AnsweringInterpretable Multi-hop Reasoning for Forecasting Future Links on Temporal Knowledge Graphs
Temporal knowledge graphs (KGs) have recently attracted growing attention. The temporal KG forecasting task, which plays a crucial role in applications such as event prediction, is predicting future links based on histor…
Knowledge GraphsQuestion AnsweringVideoMind: A Chain-of-LoRA Agent for Long Video Reasoning
Videos, with their unique temporal dimension, demand precise grounded understanding, where answers are directly linked to visual, interpretable evidence. Despite significant breakthroughs in reasoning capabilities within…
Grounded Video Question AnsweringQuestion AnsweringTemporal LocalizationVideo Question Answering+2