Ralph Abboud — accepted AI-conference papers

2023

BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology

Odhran ODonoghue, Aleksandar Shtedritski, John Ginger, Ralph Abboud, Ali Essam Ghareeb, Samuel G Rodriques

EMNLP 2023long main

The ability to automatically generate accurate protocols for scientific experiments would represent a major step towards the automation of science. Large Language Models (LLMs) have impressive capabilities on a wide range of tasks, such as question answering and the generation of coherent text and c…

Cited by 0 Source ↗code ↗Scholar ↗

2023

PlanE: Representation Learning over Planar Graphs

Radoslav Dimitrov, Zeyang Zhao, Ralph Abboud, Ismail Ilkan Ceylan

NeurIPS 2023poster

Graph neural networks are prominent models for representation learning over graphs, where the idea is to iteratively compute representations of nodes of an input graph through a series of transformations in such a way that the learned graph function is isomorphism-invariant on graphs, which makes th…

Cited by 14 Source ↗PDF ↗code ↗Scholar ↗

2022

Temporal Knowledge Graph Completion Using Box Embeddings

Johannes Messner, Ralph Abboud, Ismail Ilkan Ceylan

AAAI 2022technical

Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp. Current approaches for TK…

Cited by 117 Source ↗PDF ↗Scholar ↗

2021

The Surprising Power of Graph Neural Networks with Random Node Initialization

Ralph Abboud, İsmail İlkan Ceylan, Martin Grohe, Thomas Lukasiewicz

IJCAI 2021poster

Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot distinguish graphs beyond the capability of the Weisfeiler-Leman graph isomorphism heuristic. In order to break this expressi…

Cited by 289 Source ↗PDF ↗Scholar ↗

2020

BoxE: A Box Embedding Model for Knowledge Base Completion

Ralph Abboud, Ismail Ceylan, Thomas Lukasiewicz, Tommaso Salvatori

NeurIPS 2020spotlight

Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are su…

Cited by 266 Source ↗PDF ↗code ↗Scholar ↗