no code implementations • 28 Jun 2017 • Bhushan Kotnis, Vivi Nastase
Learning relations based on evidence from knowledge bases relies on processing the available relation instances.
1 code implementation • 22 Aug 2017 • Bhushan Kotnis, Vivi Nastase
We note a marked difference in the impact of these sampling methods on the two datasets, with the "traditional" corrupting positives method leading to best results on WN18, while embedding based methods benefiting the task on FB15k.
no code implementations • AKBC 2019 • Bhushan Kotnis, Alberto García-Durán
It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction.
no code implementations • SEMEVAL 2019 • Vivi Nastase, Bhushan Kotnis
Knowledge graphs, which provide numerous facts in a machine-friendly format, are incomplete.
1 code implementation • IJCNLP 2019 • Carolin Lawrence, Bhushan Kotnis, Mathias Niepert
Treated as a node in a fully connected graph, a placeholder token can take past and future tokens into consideration when generating the actual output token.
no code implementations • 6 Apr 2020 • Bhushan Kotnis, Carolin Lawrence, Mathias Niepert
Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries.
1 code implementation • ACL 2022 • Kiril Gashteovski, Mingying Yu, Bhushan Kotnis, Carolin Lawrence, Mathias Niepert, Goran Glavaš
In this work, we introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese, and German.
Ranked #1 on Open Information Extraction on BenchIE
1 code implementation • ACL 2022 • Niklas Friedrich, Kiril Gashteovski, Mingying Yu, Bhushan Kotnis, Carolin Lawrence, Mathias Niepert, Goran Glavaš
Open Information Extraction (OIE) is the task of extracting facts from sentences in the form of relations and their corresponding arguments in schema-free manner.
no code implementations • ACL 2022 • Bhushan Kotnis, Kiril Gashteovski, Daniel Oñoro Rubio, Vanesa Rodriguez-Tembras, Ammar Shaker, Makoto Takamoto, Mathias Niepert, Carolin Lawrence
In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction.
no code implementations • 25 May 2022 • Sascha Saralajew, Ammar Shaker, Zhao Xu, Kiril Gashteovski, Bhushan Kotnis, Wiem Ben Rim, Jürgen Quittek, Carolin Lawrence
Inspired by the Turing test, we introduce a human-centric assessment framework where a leading domain expert accepts or rejects the solutions of an AI system and another domain expert.
no code implementations • 10 Jul 2022 • Bhushan Kotnis, Kiril Gashteovski, Julia Gastinger, Giuseppe Serra, Francesco Alesiani, Timo Sztyler, Ammar Shaker, Na Gong, Carolin Lawrence, Zhao Xu
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users.
no code implementations • 27 Jul 2023 • Anna Moskvina, Bhushan Kotnis, Chris Catacata, Michael Janz, Nasrin Saef
Paraphrasing is the task of expressing an essential idea or meaning in different words.
no code implementations • EMNLP (Eval4NLP) 2020 • Kiril Gashteovski, Rainer Gemulla, Bhushan Kotnis, Sven Hertling, Christian Meilicke
First, we investigate OPIEC triples and DBpedia facts having the same arguments by comparing the information on the OIE surface relation with the KB rela- tion.