Search Results for author: Denis Lukovnikov

Found 8 papers, 2 papers with code

Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing

no code implementations Findings (EMNLP) 2021 Denis Lukovnikov, Sina Daubener, Asja Fischer

While neural networks are ubiquitous in state-of-the-art semantic parsers, it has been shown that most standard models suffer from dramatic performance losses when faced with compositionally out-of-distribution (OOD) data.

OOD Detection Semantic Parsing

Gated Relational Graph Attention Networks

no code implementations1 Jan 2021 Denis Lukovnikov, Asja Fischer

Relational Graph Neural Networks (GNN) are a class of GNN that are capable of handling multi-relational graphs.

Graph Attention Long-range modeling

Improving the Long-Range Performance of Gated Graph Neural Networks

no code implementations19 Jul 2020 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.

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

no code implementations22 Jul 2019 Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer

Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years.

Knowledge Graphs Question Answering

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

no code implementations13 Nov 2018 Denis Lukovnikov, Nilesh Chakraborty, Jens Lehmann, Asja Fischer

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

Question Answering Semantic Parsing

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

1 code implementation2 Nov 2018 Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, Jens Lehmann

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

Graph Ranking Knowledge Graphs +3

Incorporating Literals into Knowledge Graph Embeddings

1 code implementation3 Feb 2018 Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer

Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities.

Entity Embeddings Knowledge Graph Embeddings +2

Cannot find the paper you are looking for? You can Submit a new open access paper.