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Knowledge Base Completion

Knowledge base completion is the task which automatically infers missing facts by reasoning about the information already present in the knowledge base. A knowledge base is a collection of relational facts, often represented in the form of "subject", "relation", "object"-triples.

Papers

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Showing 101–150 of 156 papers

TitleStatusHype
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs—0
Interpretable and Compositional Relation Learning by Joint Training with an AutoencoderCode0
Learning Conceptual Space Representations of Interrelated Concepts—0
Revisiting the Task of Scoring Open IE Relations—0
Commonsense mining as knowledge base completion? A study on the impact of novelty—0
Towards a Continuous Knowledge Learning Engine for Chatbots—0
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search—0
Generalized Graph Embedding Models—0
Combining Representation Learning with Logic for Language Processing—0
Context-aware Path Ranking for Knowledge Base Completion—0
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural NetworkCode0
Revisiting Simple Neural Networks for Learning Representations of Knowledge GraphsCode0
KBGAN: Adversarial Learning for Knowledge Graph EmbeddingsCode0
Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning—0
Fast Linear Model for Knowledge Graph EmbeddingsCode0
Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection—0
KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical FeaturesCode0
Compositional Approaches for Representing Relations Between Words: A Comparative Study—0
Corpus-level Fine-grained Entity Typing—0
NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents—0
Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible—0
Modeling Large-Scale Structured Relationships with Shared Memory for Knowledge Base Completion—0
Feature-Rich Networks for Knowledge Base Completion—0
Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network ApproachCode0
Joint Matrix-Tensor Factorization for Knowledge Base InferenceCode0
Knowledge Base Completion: Baselines Strike Back—0
An Interpretable Knowledge Transfer Model for Knowledge Base Completion—0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
A survey of embedding models of entities and relationships for knowledge graph completionCode0
Compositional Learning of Relation Path Embedding for Knowledge Base Completion—0
Link Prediction using Embedded Knowledge Graphs—0
Mining Inference Formulas by Goal-Directed Random Walks—0
Improved Knowledge Base Completion by Path-Augmented TransR Model—0
Incorporating Relational Knowledge into Word Representations using Subspace Regularization—0
Commonsense Knowledge Base Completion—0
Knowledge Base Completion via Coupled Path Ranking—0
STransE: a novel embedding model of entities and relationships in knowledge basesCode0
Corpus-level Fine-grained Entity Typing Using Contextual Information—0
Neighborhood Mixture Model for Knowledge Base Completion—0
Knowledge-Guided Linguistic Rewrites for Inference Rule VerificationCode0
Incorporating Selectional Preferences in Multi-hop Relation Extraction—0
A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion—0
Representing Text for Joint Embedding of Text and Knowledge Bases—0
Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction—0
Observed versus latent features for knowledge base and text inference—0
Traversing Knowledge Graphs in Vector SpaceCode0
Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge BasesCode0
Modeling Relation Paths for Representation Learning of Knowledge BasesCode0
Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction—0
Probabilistic Belief Embedding for Knowledge Base Completion—0
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