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
Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals
Knowledge graph embeddings are dense numerical representations of entities in a knowledge graph (KG).
Development of a Knowledge Graph Embeddings Model for Pain
This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task.
Biomedical Knowledge Graph Embeddings with Negative Statements
Explicitly considering negative statements has been shown to improve performance on tasks such as entity summarization and question answering or domain-specific tasks such as protein function prediction.
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
zjukg/umaea
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As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information.
Explainable Representations for Relation Prediction in Knowledge Graphs
We propose SEEK, a novel approach for explainable representations to support relation prediction in knowledge graphs.
Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
nicolas-hbt/versatile-embeddings
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These models learn a vector representation of knowledge graph entities and relations, a. k. a.
What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
In this paper, we provide a similarity flooding perspective to explain existing translation-based and aggregation-based EA models.
Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks
aryopg/biokge
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We achieve a three-fold improvement in terms of performance based on the HITS@10 score over previous work on the same biomedical knowledge graph.
How to Turn Your Knowledge Graph Embeddings into Generative Models
april-tools/gekcs
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NeurIPS 2023
Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models.
HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding
We consider a contrastive learning approach to knowledge graph embedding (KGE) via InfoNCE.
