129 citations · 663 across the 44 of their papers we have counts for
15 papers · 1 filter
SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge
Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo +3
Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given…
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation
Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury +2
In the last few years, there has been a surge of interest in learning representations of entitiesand relations in knowledge graph (KG). However, the recent availability of temporal…
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…
PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs
Debayan Banerjee, Debanjan Chaudhuri, Mohnish Dubey +1
Question Answering systems are generally modelled as a pipeline consisting of a sequence of steps. In such a pipeline, Entity Linking (EL) is often the first step. Several EL model…
Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models
Isaiah Onando Mulang', Kuldeep Singh, Chaitali Prabhu +3
Pretrained Transformer models have emerged as state-of-the-art approaches that learn contextual information from text to improve the performance of several NLP tasks. These models,…
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…
IQA: Interactive Query Construction in Semantic Question Answering Systems
Hamid Zafar, Mohnish Dubey, Jens Lehmann +1
Semantic Question Answering (SQA) systems automatically interpret user questions expressed in a natural language in terms of semantic queries. This process involves uncertainty, su…
Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings
Ariam Rivas, Irlán Grangel-González, Diego Collarana +2
Industry~4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of \emph{empowering interoperability} in smart factories. These standards enable the d…
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…
5* Knowledge Graph Embeddings with Projective Transformations
Mojtaba Nayyeri, Sahar Vahdati, Can Aykul +1
Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that ma…