Search Results for author: Veronika Thost

Found 11 papers, 4 papers with code

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.

Knowledge Graphs Link Prediction

CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks

1 code implementation25 May 2021 Ruchir Puri, David S. Kung, Geert Janssen, Wei zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, Frederick Reiss

In addition to its large scale, CodeNet has a rich set of high-quality annotations to benchmark and help accelerate research in AI techniques for a variety of critical coding tasks, including code similarity and classification, code translation between a large variety of programming languages, and code performance (runtime and memory) improvement techniques.

Code Classification Code Translation

An Experimental Study of Formula Embeddings for Automated Theorem Proving in First-Order Logic

no code implementations2 Feb 2020 Ibrahim Abdelaziz, Veronika Thost, Maxwell Crouse, Achille Fokoue

Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning.

Automated Theorem Proving

Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling

no code implementations arXiv 2020 Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth Forbus, Achille Fokoue

Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems.

Automated Theorem Proving

Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks

no code implementations5 Nov 2019 Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue

A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.

Knowledge Graphs Natural Language Inference

RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools

1 code implementation16 Sep 2019 Cristina Cornelio, Veronika Thost

Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form.

Inductive knowledge graph completion Inductive logic programming +2

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