Knowledge Graph Embeddings
109 papers with code • 0 benchmarks • 4 datasets
Benchmarks
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Libraries
Use these libraries to find Knowledge Graph Embeddings models and implementations
MutualFriends
OLPBENCH
Latest papers
KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation
Parameter-efficient finetuning (PEFT) is a key technique for adapting large language models (LLMs) to downstream tasks.
Counterfactual Reasoning with Knowledge Graph Embeddings
lenazellinger/counterfactual_kgr
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We further observe that KGEs adapted with COULDD solidly detect plausible counterfactual changes to the graph that follow these patterns.
BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
These optimal features typically depend on tunable parameters of the lower-level energy in such a way that the entire bilevel pipeline can be trained end-to-end.
Pre-training and Diagnosing Knowledge Base Completion Models
vid-koci/kbctransferlearning
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The method works for both canonicalized knowledge bases and uncanonicalized or open knowledge bases, i. e., knowledge bases where more than one copy of a real-world entity or relation may exist.
Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding
The primary aim of Knowledge Graph embeddings (KGE) is to learn low-dimensional representations of entities and relations for predicting missing facts.
RDF-star2Vec: RDF-star Graph Embeddings for Data Mining
Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (<subject, predicate, object>).
Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph
davidlamprecht/linkedpaperswithcode
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In this paper, we introduce Linked Papers With Code (LPWC), an RDF knowledge graph that provides comprehensive, current information about almost 400, 000 machine learning publications.
Universal Knowledge Graph Embeddings
Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting.
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things
Graph data structures are widely used to store relational information between several entities.
Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals
Knowledge graph embeddings are dense numerical representations of entities in a knowledge graph (KG).
