Search Results for author: Bhushan Kotnis

Found 14 papers, 5 papers with code

On Aligning OpenIE Extractions with Knowledge Bases: A Case Study

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.

Open Information Extraction

What Makes a Good Paraphrase: Do Automated Evaluations Work?

no code implementations27 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.

Human-Centric Research for NLP: Towards a Definition and Guiding Questions

no code implementations10 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.

A Human-Centric Assessment Framework for AI

no code implementations25 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.

milIE: Modular & Iterative Multilingual Open Information Extraction

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.

Open Information Extraction

Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders

1 code implementation6 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.

Knowledge Graphs Link Prediction +1

Attending to Future Tokens For Bidirectional Sequence Generation

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.

Learning Numerical Attributes in Knowledge Bases

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.

Attribute Link Prediction

Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs

1 code implementation22 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.

Knowledge Graph Embeddings Knowledge Graphs +1

Learning Knowledge Graph Embeddings with Type Regularizer

no code implementations28 Jun 2017 Bhushan Kotnis, Vivi Nastase

Learning relations based on evidence from knowledge bases relies on processing the available relation instances.

Knowledge Graph Embeddings Vocal Bursts Type Prediction

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