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Knowledge Base Completion

21 papers with code ยท Knowledge Base
Subtask of Knowledge Base

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.

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Latest papers without code

Projected Canonical Decomposition for Knowledge Base Completion

ICLR 2020

However, as we show in this paper through experiments on standard benchmarks of link prediction in knowledge bases, ComplEx, a variant of CP, achieves similar performances to recent approaches based on Tucker decomposition on all operating points in terms of number of parameters.

KNOWLEDGE BASE COMPLETION LINK PREDICTION

Tensor Decompositions for Temporal Knowledge Base Completion

ICLR 2020

Additionally, we propose a new dataset for knowledge base completion constructed from Wikidata, larger than previous benchmarks by an order of magnitude, as a new reference for evaluating temporal and non-temporal link prediction methods.

KNOWLEDGE BASE COMPLETION LINK PREDICTION RECOMMENDATION SYSTEMS REPRESENTATION LEARNING

Exploiting Structural and Semantic Context for Commonsense Knowledge Base Completion

7 Oct 2019

Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1. 5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LANGUAGE MODELLING LINK PREDICTION TRANSFER LEARNING

Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning

25 Sep 2019

We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning.

KNOWLEDGE BASE COMPLETION

Populating Web Scale Knowledge Graphs using Distantly Supervised Relation Extraction and Validation

21 Aug 2019

In addition to that, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS RELATION EXTRACTION

Distributional Negative Sampling for Knowledge Base Completion

16 Aug 2019

State-of-the-art approaches for Knowledge Base Completion (KBC) exploit deep neural networks trained with both false and true assertions: positive assertions are explicitly taken from the knowledge base, whereas negative ones are generated by random sampling of entities.

KNOWLEDGE BASE COMPLETION

HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion

14 Aug 2019

Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in data.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDINGS LINK PREDICTION

Meta Reasoning over Knowledge Graphs

13 Aug 2019

The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations.

FEW-SHOT LEARNING KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS META-LEARNING

On Evaluating Embedding Models for Knowledge Base Completion

WS 2019

We propose an alternative entity-pair ranking protocol that considers all model predictions as a whole and is thus more suitable to the task.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDING

Modeling Paths for Explainable Knowledge Base Completion

WS 2019

A common approach in knowledge base completion (KBC) is to learn representations for entities and relations in order to infer missing facts by generalizing existing ones.

KNOWLEDGE BASE COMPLETION