activity
20162020
most citedIntroduction to Neural Network based Approaches for Question Answering over Knowledge Graphs

42 citations · 43 across the 3 of their papers we have counts for

collaborators

6 papers

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.CL2020

Pretrained Transformers for Simple Question Answering over Knowledge Graphs

D. Lukovnikov, A. Fischer, J. Lehmann

Answering simple questions over knowledge graphs is a well-studied problem in question answering. Previous approaches for this task built on recurrent and convolutional neural netw…

cs.CL201942 cited

Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs

Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari +3

Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years. In this article, we provide an ov…

cs.AI2018

Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters

Denis Lukovnikov, Nilesh Chakraborty, Jens Lehmann +1

Translating natural language to SQL queries for table-based question answering is a challenging problem and has received significant attention from the research community. In this…

cs.LG2018

Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs

Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov +3

In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs. We experiment with s…

cs.IR2016

Question Answering on Linked Data: Challenges and Future Directions

Saeedeh Shekarpour, Denis Lukovnikov, Ashwini Jaya Kumar +4

Question Answering (QA) systems are becoming the inspiring model for the future of search engines. While recently, underlying datasets for QA systems have been promoted from unstru…