Search Results for author:
Found 8 papers, 3 papers with code
Extending TWIG: Zero-Shot Predictive Hyperparameter Selection for KGEs based on Graph Structure
First, TWIG is able to summarise KGE performance on a wide range of hyperparameter settings and KGs being learned, suggesting that it represents a general knowledge of how to predict KGE performance from KG structure.
A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
This paper provides, to the authors' knowledge, the first comprehensive survey exploring established relationships of Knowledge Graph Embedding Models and Graph structure in the literature.
AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act
With the upcoming enforcement of the EU AI Act, documentation of high-risk AI systems and their risk management information will become a legal requirement playing a pivotal role in demonstration of compliance.
TWIG: Towards pre-hoc Hyperparameter Optimisation and Cross-Graph Generalisation via Simulated KGE Models
Our experiments on the UMLS dataset show that a single TWIG neural network can predict the results of state-of-the-art ComplEx-N3 KGE model nearly exactly on across all hyperparameter configurations.
A semantic web approach to uplift decentralized household energy data
In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system's details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device.
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns
We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs.
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods
These attacks craft adversarial additions or deletions at training time to cause model failure at test time.
Using Mapping Languages for Building Legal Knowledge Graphs from XML Files
This paper presents our experience on building RDF knowledge graphs for an industrial use case in the legal domain.
