Search Results for author: Charles Tapley Hoyt

Found 10 papers, 10 papers with code

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

ChemicalX: A Deep Learning Library for Drug Pair Scoring

1 code implementation10 Feb 2022 Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michael Ughetto, Yu Wang, Tyler Derr, Benjamin M Gyori

In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task.

Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery

2 code implementations17 May 2021 Stephen Bonner, Ian P Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Charles Tapley Hoyt, William L Hamilton

Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification.

Drug Discovery Knowledge Graph Embedding +2

A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

2 code implementations19 Feb 2021 Stephen Bonner, Ian P Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Andreas Bender, Charles Tapley Hoyt, William L Hamilton

We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources.

Drug Discovery Knowledge Graphs

Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis

1 code implementation13 Jan 2021 Jeremy Zucker, Kaushal Paneri, Sara Mohammad-Taheri, Somya Bhargava, Pallavi Kolambkar, Craig Bakker, Jeremy Teuton, Charles Tapley Hoyt, Kristie Oxford, Robert Ness, Olga Vitek

This manuscript proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question.

Counterfactual Inference

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

2 code implementations28 Jul 2020 Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann

Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.

 Ranked #1 on Link Prediction on WN18 (training time (s) metric)

Knowledge Graph Embedding Knowledge Graph Embeddings +1

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

The role of metadata in reproducible computational research

1 code implementation15 Jun 2020 Jeremy Leipzig, Daniel Nüst, Charles Tapley Hoyt, Stian Soiland-Reyes, Karthik Ram, Jane Greenberg

Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results.

Digital Libraries Software Engineering

The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability

1 code implementation28 Jan 2020 Mehdi Ali, Hajira Jabeen, Charles Tapley Hoyt, Jens Lehman

Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability.

Fact Checking Knowledge Graph Embedding +3

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