PROBE is a generalized rank-based KGC evaluation framework with adjustable sharpness and bias-robustness components that satisfies six claimed key properties where prior metrics fall short.
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Embedding Entities and Relations for Learning and Inference in Knowledge Bases
14 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2% vs. 54.7% by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as "BornInCity(a,b) and CityInCountry(b,c) => Nationality(a,c)". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning.
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HetSheaf applies cellular sheaves and type-conditioned restriction maps to heterogeneous graphs, plus SheafPool for basis-invariant graph-level representations, delivering competitive accuracy with substantially reduced parameter counts.
Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7% gains over language model baselines.
Combining graph wavelet embeddings with Neural Bellman-Ford message passing reduces the layers needed for inductive logical query answering on large knowledge graphs.
Introduces the relation set completion task and RelSetE embedding model to infer missing entity-compatible relations by modeling latent patterns in observed relations, evaluated on three derived KG benchmarks.
KGEMs for link prediction exhibit high instability in predictions and embeddings from initialization, negative sampling, and other factors, with better MRR not ensuring higher stability.
Entity representations learned from text via link prediction generalize to unseen entities and transfer to classification and retrieval with reported gains of 22% MRR, 16% accuracy, and 8.8% NDCG@10.
GraphStar is a new GNN that adds star nodes and relay attention to achieve non-local representations for node, graph, and link tasks, claiming 2-5% gains over prior SOTA on benchmarks.
EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
TED is a heterogeneous GNN that uses related party transaction groups and hierarchical attention to detect tax evasion, claiming significant outperformance over prior methods on two real tax datasets.
Authors release the multimodal WJoconde knowledge graph for French cultural heritage and a LLM-VLM pipeline that extracts and validates new triples from unstructured text and images to extend the graph.
T-TExTS builds a domain ontology into a knowledge graph and tests four embedding methods, finding Node2Vec yields the highest AUC (0.9642-0.9750) while a hybrid embedding balances ranking quality with interpretability across dataset sizes of 98-351 texts.
Semantic field-level re-ranking on FSDM candidates yields 2.5% NDCG@10 and 1.2% NDCG@100 gains on DBpedia-Entity v2.
citing papers explorer
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Generalized Rank-based Evaluation for Knowledge Graph Completion: Perspectives, Framework, and Analyses
PROBE is a generalized rank-based KGC evaluation framework with adjustable sharpness and bias-robustness components that satisfies six claimed key properties where prior metrics fall short.
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Heterogeneous Sheaf Neural Networks
HetSheaf applies cellular sheaves and type-conditioned restriction maps to heterogeneous graphs, plus SheafPool for basis-invariant graph-level representations, delivering competitive accuracy with substantially reduced parameter counts.
-
Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs
Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7% gains over language model baselines.
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InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs
Combining graph wavelet embeddings with Neural Bellman-Ford message passing reduces the layers needed for inductive logical query answering on large knowledge graphs.
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Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs
Introduces the relation set completion task and RelSetE embedding model to infer missing entity-compatible relations by modeling latent patterns in observed relations, evaluated on three derived KG benchmarks.
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Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings
KGEMs for link prediction exhibit high instability in predictions and embeddings from initialization, negative sampling, and other factors, with better MRR not ensuring higher stability.
-
Inductive Entity Representations from Text via Link Prediction
Entity representations learned from text via link prediction generalize to unseen entities and transfer to classification and retrieval with reported gains of 22% MRR, 16% accuracy, and 8.8% NDCG@10.
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Graph Star Net for Generalized Multi-Task Learning
GraphStar is a new GNN that adds star nodes and relay attention to achieve non-local representations for node, graph, and link tasks, claiming 2-5% gains over prior SOTA on benchmarks.
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EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval
EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
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TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph
TED is a heterogeneous GNN that uses related party transaction groups and hierarchical attention to detect tax evasion, claiming significant outperformance over prior methods on two real tax datasets.
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Multimodal Cultural Heritage Knowledge Graph Extension with Language and Vision Models
Authors release the multimodal WJoconde knowledge graph for French cultural heritage and a LLM-VLM pipeline that extracts and validates new triples from unstructured text and images to extend the graph.
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T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation
T-TExTS builds a domain ontology into a knowledge graph and tests four embedding methods, finding Node2Vec yields the highest AUC (0.9642-0.9750) while a hybrid embedding balances ranking quality with interpretability across dataset sizes of 98-351 texts.
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Semantic Driven Fielded Entity Retrieval
Semantic field-level re-ranking on FSDM candidates yields 2.5% NDCG@10 and 1.2% NDCG@100 gains on DBpedia-Entity v2.
- When Modalities Remember: Continual Learning for Multimodal Knowledge Graphs