Deep Sparse Latent Feature Models for Knowledge Graph Completion | Paper Citation Record

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This paper cites Mixed membership stochastic blockmodels.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Mixed membership stochastic blockmodels

Reference 1

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This paper cites Dbpedia: A nucleus for a web of open data.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Dbpedia: A nucleus for a web of open data

Reference 2

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This paper cites TuckER: Tensor Factorization for Knowledge Graph Completion.

Deep Sparse Latent Feature Models for Knowledge Graph Completion TuckER: Tensor Factorization for Knowledge Graph Completion

Reference 3

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This paper cites Reverse engineering self-supervised learning.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Reverse engineering self-supervised learning

Reference 4

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This paper cites Translating embeddings for modeling multi-relational data.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Translating embeddings for modeling multi-relational data

Reference 5

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This paper cites Generating Sentences from a Continuous Space.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Generating Sentences from a Continuous Space

Reference 6

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This paper cites Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion

Reference 7

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This paper cites Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting

Reference 8

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This paper cites HittER: Hierarchical transformers for knowledge graph embeddings.

Deep Sparse Latent Feature Models for Knowledge Graph Completion HittER: Hierarchical transformers for knowledge graph embeddings

Reference 9

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This paper cites A simple frame- work for contrastive learning of visual representations.

Deep Sparse Latent Feature Models for Knowledge Graph Completion A simple frame- work for contrastive learning of visual representations

Reference 10

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This paper cites A direct formulation for sparse pca using semidefinite programming.

Deep Sparse Latent Feature Models for Knowledge Graph Completion A direct formulation for sparse pca using semidefinite programming

Reference 11

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This paper cites Inductive entity representations from text via link prediction.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Inductive entity representations from text via link prediction

Reference 12

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Observation 0293d121-b2c1-4713-82cf-db6e4a3055e0 · outbound

This paper cites Convolutional 2d knowledge graph embeddings.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Convolutional 2d knowledge graph embeddings

Reference 13

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This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

Deep Sparse Latent Feature Models for Knowledge Graph Completion BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 14

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This paper cites Knowledge vault: A web-scale approach to proba- bilistic knowledge fusion.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Knowledge vault: A web-scale approach to proba- bilistic knowledge fusion

Reference 15

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This paper cites Implicit reparameterization gradients.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Implicit reparameterization gradients

Reference 16

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This paper cites Infinite latent feature models and the indian buffet process.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Infinite latent feature models and the indian buffet process

Reference 17

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This paper cites The indian buffet process: An introduction and review.

Deep Sparse Latent Feature Models for Knowledge Graph Completion The indian buffet process: An introduction and review

Reference 18

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This paper cites Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics

Reference 19

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This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework.

Deep Sparse Latent Feature Models for Knowledge Graph Completion beta-vae: Learning basic visual concepts with a constrained variational framework

Reference 20

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This paper cites Learning deep representations by mutual information estimation and maximization.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Learning deep representations by mutual information estimation and maximization

Reference 21

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This paper cites Stochastic blockmodels: First steps.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Stochastic blockmodels: First steps

Reference 22

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This paper cites Categorical Reparameterization with Gumbel-Softmax.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Categorical Reparameterization with Gumbel-Softmax

Reference 23

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This paper cites A modified principal component technique based on the lasso.

Deep Sparse Latent Feature Models for Knowledge Graph Completion A modified principal component technique based on the lasso

Reference 24

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This paper cites Supervised contrastive learning.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Supervised contrastive learning

Reference 25

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This paper cites Multi-task learning for knowl- edge graph completion with pre-trained language models.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Multi-task learning for knowl- edge graph completion with pre-trained language models

Reference 26

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Observation d37aaec4-32a5-4464-8f4b-8b5a5893dc4f · outbound

This paper cites Auto-Encoding Variational Bayes.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Auto-Encoding Variational Bayes

Reference 27

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Semi-Supervised Classification with Graph Convolutional Networks

Reference 28

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This paper cites Statistical predicate invention.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Statistical predicate invention

Reference 29

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This paper cites Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs

Reference 30

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This paper cites Bayesian methods for graph clustering.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Bayesian methods for graph clustering

Reference 31

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This paper cites Overlapping stochastic block models with application to the french political blogosphere.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Overlapping stochastic block models with application to the french political blogosphere

Reference 32

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This paper cites KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation.

Deep Sparse Latent Feature Models for Knowledge Graph Completion KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation

Reference 33

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Deep Sparse Latent Feature Models for Knowledge Graph Completion Unresolved cited work

Reference 34

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This paper cites Learning entity and relation embeddings for knowledge graph completion.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Learning entity and relation embeddings for knowledge graph completion

Reference 35

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This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

Deep Sparse Latent Feature Models for Knowledge Graph Completion The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 36

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This paper cites A* sampling.Advances in neural information processing systems, 27, 2014.

Deep Sparse Latent Feature Models for Knowledge Graph Completion A* sampling.Advances in neural information processing systems, 27, 2014

Reference 37

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This paper cites Stochastic blockmodels meet graph neural networks.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Stochastic blockmodels meet graph neural networks

Reference 38

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This paper cites Nonparametric latent feature models for link prediction.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Nonparametric latent feature models for link prediction

Reference 39

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Observation 9ff25a3a-d98e-4262-b42c-2cef8cd05743 · outbound

This paper cites Stick-Breaking Variational Autoencoders.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Stick-Breaking Variational Autoencoders

Reference 40

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Observation e3b4ce15-6271-4acb-89be-3f9a257b2780 · outbound

This paper cites Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

Reference 41

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This paper cites Communities in networks.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Communities in networks

Reference 42

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Observation d6f90500-0a7c-48ec-a3e9-1b083cc2f15a · outbound

This paper cites Improving knowledge graph completion with generative hard negative mining.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Improving knowledge graph completion with generative hard negative mining

Reference 43

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This paper cites Variational inference with normalizing flows.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Variational inference with normalizing flows

Reference 44

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This paper cites Modeling relational data with graph convolutional networks.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Modeling relational data with graph convolutional networks

Reference 45

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This paper cites Reasoning with neural tensor networks for knowledge base completion.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Reasoning with neural tensor networks for knowledge base completion

Reference 46

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This paper cites Stochas- tic block models with multiple continuous attributes.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Stochas- tic block models with multiple continuous attributes

Reference 47

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This paper cites Rotate: Knowledge graph em- bedding by relational rotation in complex space.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Rotate: Knowledge graph em- bedding by relational rotation in complex space

Reference 48

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This paper cites Kracl: Contrastive learning with graph context modeling for sparse knowledge graph completion.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Kracl: Contrastive learning with graph context modeling for sparse knowledge graph completion

Reference 49

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This paper cites Stick-breaking construction for the indian buffet process.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Stick-breaking construction for the indian buffet process

Reference 50

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Observation 23ddcdde-fb2c-4ec5-82b2-251929acb04e · outbound

This paper cites Representing text for joint embedding of text and knowledge bases.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Representing text for joint embedding of text and knowledge bases

Reference 51

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This paper cites Composition-based Multi-Relational Graph Convolutional Networks.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Composition-based Multi-Relational Graph Convolutional Networks

Reference 52

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This paper cites Wikidata: a free collaborative knowledgebase.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Wikidata: a free collaborative knowledgebase

Reference 53

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This paper cites Structure- augmented text representation learning for efficient knowledge graph completion.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Structure- augmented text representation learning for efficient knowledge graph completion

Reference 54

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This paper cites Simkgc: Simple contrastive knowledge graph completion with pre-trained language models.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Simkgc: Simple contrastive knowledge graph completion with pre-trained language models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:07:58.556490Z

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Observation 0bb263bc-04f4-4e83-b6c2-4a058b0e31fb · outbound

This paper cites Kepler: A unified model for knowledge embedding and pre-trained language representation.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Kepler: A unified model for knowledge embedding and pre-trained language representation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:07:58.535009Z

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Observation ebb73c37-1618-4f8d-8095-52aab0fccff2 · outbound

This paper cites KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion.

Deep Sparse Latent Feature Models for Knowledge Graph Completion KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion

Reference 57

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This paper cites Representation learning of knowledge graphs with entity descriptions.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Representation learning of knowledge graphs with entity descriptions

Reference 58

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This paper cites Embedding Entities and Relations for Learning and Inference in Knowledge Bases.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Embedding Entities and Relations for Learning and Inference in Knowledge Bases

Reference 59

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Deep Sparse Latent Feature Models for Knowledge Graph Completion Enhancing text-based knowledge graph completion with zero-shot large language models: A focus on semantic enhancement

Reference 60

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Deep Sparse Latent Feature Models for Knowledge Graph Completion Knowledge graph embedding and completion based on entity community and local importance

Reference 61

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Deep Sparse Latent Feature Models for Knowledge Graph Completion KG-BERT: BERT for Knowledge Graph Completion

Reference 62

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Deep Sparse Latent Feature Models for Knowledge Graph Completion Exploring Large Language Models for Knowledge Graph Completion

Reference 63

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Deep Sparse Latent Feature Models for Knowledge Graph Completion Native: Multi-modal knowledge graph completion in the wild

Reference 64

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Deep Sparse Latent Feature Models for Knowledge Graph Completion Making large language models perform better in knowledge graph completion

Reference 65

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This paper cites Rethinking graph convolutional networks in knowledge graph completion.

Deep Sparse Latent Feature Models for Knowledge Graph Completion Rethinking graph convolutional networks in knowledge graph completion

Reference 66

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Deep Sparse Latent Feature Models for Knowledge Graph Completion Max-Margin Nonparametric Latent Feature Models for Link Prediction

Reference 67

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Deep Sparse Latent Feature Models for Knowledge Graph Completion location-scale

Reference 68

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