Search Results for author: Mikhail Galkin

Found 12 papers, 9 papers with code

Recipe for a General, Powerful, Scalable Graph Transformer

1 code implementation25 May 2022 Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini

We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks.

Graph Classification Graph Property Prediction +3

Neural-Symbolic Models for Logical Queries on Knowledge Graphs

no code implementations16 May 2022 Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, Jian Tang

Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning.

Knowledge Graph Completion

A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs

2 code implementations14 Mar 2022 Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin, Volker Tresp, Benjamin M. Gyori

The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics.

Knowledge Graph Embedding Knowledge Graphs +1

An Open Challenge for Inductive Link Prediction on Knowledge Graphs

1 code implementation3 Mar 2022 Mikhail Galkin, Max Berrendorf, Charles Tapley Hoyt

An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities.

Graph Representation Learning Inductive Link Prediction +1

Improving Inductive Link Prediction Using Hyper-Relational Facts

1 code implementation10 Jul 2021 Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, Jens Lehmann

In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.

Inductive Link Prediction Knowledge Graphs

Query Embedding on Hyper-relational Knowledge Graphs

1 code implementation ICLR 2022 Dimitrios Alivanistos, Max Berrendorf, Michael Cochez, Mikhail Galkin

Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.

Knowledge Graphs Link Prediction +1

Message Passing for Hyper-Relational Knowledge Graphs

1 code implementation EMNLP 2020 Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, Jens Lehmann

We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K.

Knowledge Graphs Link Prediction

Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework

2 code implementations23 Jun 2020 Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, Jens Lehmann

The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.

Knowledge Graph Embedding

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