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Knowledge Base Completion

14 papers with code · Knowledge Base
Subtask of Knowledge Base

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Greatest papers with code

Modeling Relational Data with Graph Convolutional Networks

17 Mar 2017tkipf/gae

Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION

Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases

2 Jun 2015glorotxa/SME

This paper tackles the problem of endogenous link prediction for Knowledge Base completion. Knowledge Bases can be represented as directed graphs whose nodes correspond to entities and edges to relationships.

KNOWLEDGE BASE COMPLETION LINK PREDICTION

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

HLT 2018 cai-lw/KBGAN

Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a non-trivial task. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features

14 Sep 2017nle-ml/mmkb

We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models.

KNOWLEDGE BASE COMPLETION REPRESENTATION LEARNING

An overview of embedding models of entities and relationships for knowledge base completion

23 Mar 2017datquocnguyen/STransE

Knowledge bases (KBs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform knowledge base completion or link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true.

KNOWLEDGE BASE COMPLETION LINK PREDICTION

STransE: a novel embedding model of entities and relationships in knowledge bases

HLT 2016 datquocnguyen/STransE

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true.

KNOWLEDGE BASE COMPLETION LINK PREDICTION

Embedding Multimodal Relational Data for Knowledge Base Completion

EMNLP 2018 pouyapez/multim-kb-embeddings

In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data. Further, using these learned embedings and different neural decoders, we introduce a novel multimodal imputation model to generate missing multimodal values, like text and images, from information in the knowledge base.

KNOWLEDGE BASE COMPLETION LINK PREDICTION

Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder

ACL 2018 tianran/glimvec

Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base. Intuitively, a relation can be modeled by a matrix mapping entity vectors.

DIMENSIONALITY REDUCTION KNOWLEDGE BASE COMPLETION

End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion

11 Nov 2018JD-AI-Research-Silicon-Valley/SACN

The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. Node attributes in the graph are represented as additional nodes in the WGCN.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPHS LINK PREDICTION

Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs

15 Nov 2017Srinivas-R/AKBC-2017-Paper-14

We address the problem of learning vector representations for entities and relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This problem has received significant attention in the past few years and multiple methods have been proposed.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS