activity
20152020
most citedPyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

89 citations · 203 across the 16 of their papers we have counts for

collaborators

18 papers

cs.CL2020

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…

cs.LG202089 cited

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…

cs.LG20201 cited

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…

cs.IR202021 cited

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…

cs.AI2020

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…

cs.CL202012 cited

End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings

Rostislav Nedelchev, Debanjan Chaudhuri, Jens Lehmann +1

Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs. It is often based on…