While there are works on the automated estimation of argument strength, their scope is narrow: they focus on isolated datasets and neglect the interactions with related argument mining tasks, such as argument identification, evidence detection, or emotional appeal.
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics.
An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities.
Ranked #1 on Inductive Link Prediction on ILPC22-Small
Climate change is expected to increase the likelihood of drought events, with severe implications for food security.
In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.
1 code implementation • 11 May 2021 • Elena A. Kronberg, Tanveer Hannan, Jens Huthmacher, Marcus Münzer, Florian Peste, Ziyang Zhou, Max Berrendorf, Evgeniy Faerman, Fabio Gastaldello, Simona Ghizzardi, Philippe Escoubet, Stein Haaland, Artem Smirnov, Nithin Sivadas, Robert C. Allen, Andrea Tiengo, Raluca Ilie
The average correlation between the observed and predicted data is about 56%, which is reasonable in light of the complex dynamics of fast-moving energetic protons in the magnetosphere.
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work.
In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects.
Therefore, we first carefully examine the benchmarking process and identify several shortcomings, which make the results reported in the original works not always comparable.
Ranked #5 on Entity Alignment on dbp15k fr-en
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
Ranked #1 on Link Prediction on WN18 (training time (s) metric)
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment.
Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.
In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task.
Ranked #25 on Entity Alignment on DBP15k zh-en
We argue that depth maps can additionally provide valuable information on object relations, e. g. helping to detect not only spatial relations, such as standing behind, but also non-spatial relations, such as holding.