Search Results for author: Rainer Gemulla

Found 22 papers, 10 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

Differentiable Implicit Layers

no code implementations14 Oct 2020 Andreas Look, Simona Doneva, Melih Kandemir, Rainer Gemulla, Jan Peters

In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions.

LibKGE - A knowledge graph embedding library for reproducible research

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.

Hyperparameter Optimization Knowledge Graph Embedding +1

Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction

1 code implementation ACL 2020 Samuel Broscheit, Kiril Gashteovski, Yanjie Wang, Rainer Gemulla

An evaluation in such a setup raises the question if a correct prediction is actually a new fact that was induced by reasoning over the open knowledge graph or if it can be trivially explained.

Knowledge Graph Embeddings Link Prediction +2

You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings

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.

Hyperparameter Optimization Knowledge Graph Embedding +2

Dynamic Parameter Allocation in Parameter Servers

1 code implementation3 Feb 2020 Alexander Renz-Wieland, Rainer Gemulla, Steffen Zeuch, Volker Markl

To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks.

BIG-bench Machine Learning Management

OPIEC: An Open Information Extraction Corpus

3 code implementations AKBC 2019 Kiril Gashteovski, Sebastian Wanner, Sven Hertling, Samuel Broscheit, Rainer Gemulla

In this paper, we release, describe, and analyze an OIE corpus called OPIEC, which was extracted from the text of English Wikipedia.

Open Information Extraction Question Answering +1

A Relational Tucker Decomposition for Multi-Relational Link Prediction

no code implementations3 Feb 2019 Yanjie Wang, Samuel Broscheit, Rainer Gemulla

We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs.

Knowledge Graph Embedding Knowledge Graphs +1

On Evaluating Embedding Models for Knowledge Base Completion

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.

Knowledge Base Completion Question Answering

On Multi-Relational Link Prediction with Bilinear Models

no code implementations14 Sep 2017 Yanjie Wang, Rainer Gemulla, Hui Li

Bilinear models belong to the most basic models for this task, they are comparably efficient to train and use, and they can provide good prediction performance.

Knowledge Graph Completion Link Prediction

MinIE: Minimizing Facts in Open Information Extraction

1 code implementation EMNLP 2017 Kiril Gashteovski, Rainer Gemulla, Luciano del Corro

The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from natural-language text in an unsupervised, domain-independent manner.

Open Information Extraction Question Answering +1

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