1 code implementation • 20 Dec 2024 • Tornike Tsereteli, Daniel Ruffinelli, Simone Paolo Ponzetto
By benchmarking deep learning systems for each of the two stages independently and sequentially, we demonstrate that the task is feasible, but observe that errors propagate from the first stage, leading to a lower overall task performance.
no code implementations • 23 Aug 2022 • Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim, PengFei Liu, Christopher Malon, Daniel Ruffinelli, Carolin Lawrence, Graham Neubig
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples.
1 code implementation • EMNLP 2020 • Samuel Broscheit, Daniel Ruffinelli, Adrian Kochsiek, Patrick Betz, Rainer Gemulla
LibKGE ( https://github. com/uma-pi1/kge ) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction.
2 code implementations • ICLR 2020 • Daniel Ruffinelli, Samuel Broscheit, Rainer Gemulla
A vast number of KGE techniques for multi-relational link prediction have been proposed in the recent literature, often with state-of-the-art performance.
no code implementations • WS 2019 • Yanjie Wang, Daniel Ruffinelli, Rainer Gemulla, Samuel Broscheit, Christian Meilicke
In this paper, we explore whether recent models work well for knowledge base completion and argue that the current evaluation protocols are more suited for question answering rather than knowledge base completion.