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Knowledge Base Completion

23 papers with code · 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 with code

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

ACL 2019 deepakn97/relationPrediction

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

164
04 Jun 2019

Neural Consciousness Flow

30 May 2019netpaladinx/NeuCFlow

Instead, inspired by the consciousness prior proposed by Yoshua Bengio, we explore reasoning with the notion of attentive awareness from a cognitive perspective, and formulate it in the form of attentive message passing on graphs, called neural consciousness flow (NeuCFlow).

DECISION MAKING KNOWLEDGE BASE COMPLETION

6
30 May 2019

Path Ranking with Attention to Type Hierarchies

26 May 2019wliu88/AttentivePathRanking

The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS

3
26 May 2019

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

Association of Computational Linguistics (ACl) 2019 2019 deepakn97/relationPrediction

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH COMPLETION LINK PREDICTION

164
13 May 2019

Scene Graph Prediction with Limited Labels

ICCV 2019 vincentschen/limited-label-scene-graphs

All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each.

KNOWLEDGE BASE COMPLETION QUESTION ANSWERING TRANSFER LEARNING VISUAL QUESTION ANSWERING

21
25 Apr 2019

Fact Discovery from Knowledge Base via Facet Decomposition

NAACL 2019 thunlp/FFD

We also propose a novel auto-encoder based facet component to estimate some facets of the fact.

KNOWLEDGE BASE COMPLETION

4
21 Apr 2019

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.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPHS LINK PREDICTION

35
11 Nov 2018

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.

IMPUTATION KNOWLEDGE BASE COMPLETION LINK PREDICTION

38
05 Sep 2018