Knowledge Graph Embedding
197 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Knowledge Graph Embedding models and implementations
FB15k
KG20C
Latest papers
Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive Learning
aoluming/IDKG
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Firstly we retrieve the relevant embedding from the knowledge graph by utilizing group relations in metadata and then integrate it with other modalities.
Contextualized Structural Self-supervised Learning for Ontology Matching
Ontology matching (OM) entails the identification of semantic relationships between concepts within two or more knowledge graphs (KGs) and serves as a critical step in integrating KGs from various sources.
Model-based Subsampling for Knowledge Graph Completion
Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing overfitting caused by the sparsity in Knowledge Graph (KG) datasets.
Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference
tgebhart/sheaf_kg_transind
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In this work, we bridge the gap between traditional transductive knowledge graph embedding approaches and more recent inductive relation prediction models by introducing a generalized form of harmonic extension which leverages representations learned through transductive embedding methods to infer representations of new entities introduced at inference time as in the inductive setting.
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning
In this paper, we propose a knowledge graph embedding model for the efficient diagnosis of animal diseases, which could learn various types of literal information and graph structure and fuse them into unified representations, namely LiteralKG.
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.
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
zjukg/comprehensive-study-over-relational-patterns
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Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern.
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.
CausE: Towards Causal Knowledge Graph Embedding
zjukg/cause
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Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces, which can be employed to predict the missing triples to achieve knowledge graph completion (KGC).
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation
However, Knowledge Graphs are not just sets of links but also have semantics underlying their structure.
