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Found 24 papers, 20 papers with code
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors.
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks
Pre-training large-scale language models (LMs) requires huge amounts of text corpora.
Message Passing for Hyper-Relational Knowledge Graphs
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.
Ranked #2 on
Link Prediction
on JF17K
Query Embedding on Hyper-relational Knowledge Graphs
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.
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
To this end, we propose NodePiece, an anchor-based approach to learn a fixed-size entity vocabulary.
Ranked #18 on
Link Property Prediction
on ogbl-wikikg2
Improving Inductive Link Prediction Using Hyper-Relational Facts
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.
Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application
We show that the segmentation model reduces the ratio of detection disagreements from 0. 162 to 0. 085 (p < 0. 05).
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
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.
Ranked #1 on
Inductive Link Prediction
on ILPC22-Small
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics.
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning.
Ranked #3 on
Complex Query Answering
on FB15k-237
Recipe for a General, Powerful, Scalable Graph Transformer
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.
Ranked #1 on
Graph Property Prediction
on ogbg-ppa
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Experiments on both transductive and inductive knowledge graph reasoning benchmarks show that A*Net achieves competitive performance with existing state-of-the-art path-based methods, while merely visiting 10% nodes and 10% edges at each iteration.
Ranked #10 on
Link Property Prediction
on ogbl-wikikg2
Long Range Graph Benchmark
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.
Ranked #3 on
Link Prediction
on PCQM-Contact
A Decade of Knowledge Graphs in Natural Language Processing: A Survey
In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry.
Inductive Logical Query Answering in Knowledge Graphs
Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes.
Weisfeiler and Leman Go Relational
Namely, we investigate the limitations in the expressive power of the well-known Relational GCN and Compositional GCN architectures and shed some light on their practical learning performance.
Attending to Graph Transformers
Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks.
Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
Extending the idea of graph databases (graph DBs), NGDB consists of a Neural Graph Storage and a Neural Graph Engine.
Approximate Answering of Graph Queries
We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations.
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.
Towards Foundation Models for Knowledge Graph Reasoning
The key challenge of designing foundation models on KGs is to learn such transferable representations that enable inference on any graph with arbitrary entity and relation vocabularies.
Graph Foundation Models
Graph Foundation Model (GFM) is a new trending research topic in the graph domain, aiming to develop a graph model capable of generalizing across different graphs and tasks.
Zero-shot Logical Query Reasoning on any Knowledge Graph
Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and logical operations.
