129 citations · 663 across the 44 of their papers we have counts for
12 papers · 1 filter
Improving Inductive Link Prediction Using Hyper-Relational Facts
Mehdi Ali, Max Berrendorf, Mikhail Galkin +4
For many years, link prediction on knowledge graphs (KGs) has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put i…
Motif Learning in Knowledge Graphs Using Trajectories Of Differential Equations
Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann +1
Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a knowledge graph into a geometric space (usu…
Knowledge Graph Embeddings in Geometric Algebras
Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen +1
Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space. Existing KG embedding approaches model entities a…
Message Passing for Hyper-Relational Knowledge Graphs
Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari +2
Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating additional key-value pairs along with the main triple to disambiguate, or restrict the validity of a fac…
MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities
Jason Armitage, Endri Kacupaj, Golsa Tahmasebzadeh +4
In this paper, we introduce the MLM (Multiple Languages and Modalities) dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three…
Training Multimodal Systems for Classification with Multiple Objectives
Jason Armitage, Shramana Thakur, Rishi Tripathi +2
We learn about the world from a diverse range of sensory information. Automated systems lack this ability as investigation has centred on processing information presented in a sing…
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt +4
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs. While each of them…
Improving the Long-Range Performance of Gated Graph Neural Networks
Denis Lukovnikov, Jens Lehmann, Asja Fischer
Many popular variants of graph neural networks (GNNs) that are capable of handling multi-relational graphs may suffer from vanishing gradients. In this work, we propose a novel GNN…
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt +6
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult. In order to as…
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury +2
Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal informatio…
Distantly Supervised Question Parsing
Hamid Zafar, Maryam Tavakol, Jens Lehmann
The emergence of structured databases for Question Answering (QA) systems has led to developing methods, in which the problem of learning the correct answer efficiently is based on…
How Complex is your classification problem? A survey on measuring classification complexity
Ana C. Lorena, Luís P. F. Garcia, Jens Lehmann +2
Characteristics extracted from the training datasets of classification problems have proven to be effective predictors in a number of meta-analyses. Among them, measures of classif…