1 code implementation • 15 Nov 2023 • Zifeng Ding, Heling Cai, Jingpei Wu, Yunpu Ma, Ruotong Liao, Bo Xiong, Volker Tresp
We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods.
no code implementations • 14 Jul 2023 • Zifeng Ding, Jingcheng Wu, Jingpei Wu, Yan Xia, Volker Tresp
We develop two new benchmark hyper-relational TKG (HTKG) datasets, i. e., Wiki-hy and YAGO-hy, and propose an HTKG reasoning model that efficiently models both temporal facts and qualifiers.
1 code implementation • 2 Apr 2023 • Zifeng Ding, Jingpei Wu, Zongyue Li, Yunpu Ma, Volker Tresp
Most previous TKGC methods only consider predicting the missing links among the entities seen in the training set, while they are unable to achieve great performance in link prediction concerning newly-emerged unseen entities.
no code implementations • 15 Nov 2022 • Zifeng Ding, Jingpei Wu, Bailan He, Yunpu Ma, Zhen Han, Volker Tresp
Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities.
1 code implementation • 12 Aug 2022 • Zifeng Ding, Zongyue Li, Ruoxia Qi, Jingpei Wu, Bailan He, Yunpu Ma, Zhao Meng, Shuo Chen, Ruotong Liao, Zhen Han, Volker Tresp
To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions.