Knowledge Graph Embeddings
109 papers with code • 0 benchmarks • 4 datasets
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Latest papers with no code
RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models
For that purpose, Knowledge Graph Embeddings (KGE) and Bayesian inference are combined to allow the deployment of a fully inductive reasoning system that enables the issuing of predictions that rely on legacy information contained in the graph as well as on current evidence gathered in real time by onboard sensors.
FedCQA: Answering Complex Queries on Multi-Source Knowledge Graphs via Federated Learning
An entity can be involved in various knowledge graphs and reasoning on multiple KGs and answering complex queries on multi-source KGs is important in discovering knowledge cross graphs.
Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model
Recent advancements in generative models have established state-of-the-art benchmarks in generating molecules and novel drug candidates.
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts
We tested our approach on a dataset of 2, 000 tweets about eating disorders, finding that merging word embeddings with knowledge graph information enhances the predictive models' reliability.
Embedding Knowledge Graphs in Degenerate Clifford Algebras
We propose to consider nilpotent base vectors with a nilpotency index of two.
Capturing Knowledge Graphs and Rules with Octagon Embeddings
Region based knowledge graph embeddings represent relations as geometric regions.
Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inference
Other works assessed the risk made by the surrounding vehicles only by considering their existence around the target vehicle, or by considering the distance and relative velocities between them and the target vehicle as two separate numerical features.
EXTRACT: Explainable Transparent Control of Bias in Embeddings
We propose EXTRACT: a suite of Explainable and Transparent methods to ConTrol bias in knowledge graph embeddings, so as to assess and decrease the implicit presence of protected information.
Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph
This mechanism ensures zero incidence of corrupted paths by enforcing adherence to valid KG connections at the decoding level, agnostic of the underlying model architecture.
Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings
Moreover, the integration of traditional machine learning methods with KG embeddings, such as decision trees and graph neural networks are also explored.
