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Found 18 papers, 16 papers with code
Approximate Answering of Graph Queries
We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations.
Towards a Holistic View on Argument Quality Prediction
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.
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics.
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
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
On the Generalization of Agricultural Drought Classification from Climate Data
Climate change is expected to increase the likelihood of drought events, with severe implications for food security.
Improving Inductive Link Prediction Using Hyper-Relational Facts
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.
Query Embedding on Hyper-relational Knowledge Graphs
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.
Prediction of soft proton intensities in the near-Earth space using machine learning
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.
Argument Mining Driven Analysis of Peer-Reviews
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work.
Diversity Aware Relevance Learning for Argument Search
In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects.
A Critical Assessment of State-of-the-Art in Entity Alignment
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 #11 on
Entity Alignment
on dbp15k fr-en
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
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)
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
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.
Unsupervised Anomaly Detection for X-Ray Images
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.
Active Learning for Entity Alignment
In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets.
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
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 #33 on
Entity Alignment
on DBP15k zh-en
Improving Visual Relation Detection using Depth Maps
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.
Ranked #1 on
Relationship Detection
on VRD
