Open-World Knowledge Graph Completion for Unseen Entities and Relations via Attentive Feature Aggregation

2021 ◽  
Author(s):  
Byungkook Oh ◽  
Seungmin Seo ◽  
Jimin Hwang ◽  
Dongho Lee ◽  
Kyong-Ho Lee
Keyword(s):  
Knowledge Graph ◽  
World Knowledge ◽  
Open World ◽  
Feature Aggregation

SDT: An integrated model for open-world knowledge graph reasoning

2020 ◽  
Vol 162 ◽  
pp. 113889
Author(s):  
Xiaojun Chen ◽  
Shengbin Jia ◽  
Ling Ding ◽  
Hong Shen ◽  
Yang Xiang
Keyword(s):  
Integrated Model ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Open World

scholarly journals Caps-OWKG: a capsule network model for open-world knowledge graph

2021 ◽  
Author(s):  
Yuhan Wang ◽  
Weidong Xiao ◽  
Zhen Tan ◽  
Xiang Zhao
Keyword(s):  
Representation Learning ◽  
Graph Representation ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Relational Structures ◽  
Open World ◽  
Latent Features ◽  
Knowledge Graphs ◽  
Low Dimensional ◽  
Better Than

AbstractKnowledge graphs are typical multi-relational structures, which is consisted of many entities and relations. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is utilized to embed entities and relations into low-dimensional spaces. Many existing knowledge graphs embedding models focus on learning latent features in close-world assumption but omit the changeable of each knowledge graph.In this paper, we propose a knowledge graph representation learning model, called Caps-OWKG, which leverages the capsule network to capture the both known and unknown triplets features in open-world knowledge graph. It combines the descriptive text and knowledge graph to get descriptive embedding and structural embedding, simultaneously. Then, the both above embeddings are used to calculate the probability of triplet authenticity. We verify the performance of Caps-OWKG on link prediction task with two common datasets FB15k-237-OWE and DBPedia50k. The experimental results are better than other baselines, and achieve the state-of-the-art performance.

Neighborhood aggregation based graph attention networks for open-world knowledge graph reasoning

2021 ◽  
pp. 1-12
Author(s):  
Xiaojun Chen ◽  
Ling Ding ◽  
Yang Xiang
Keyword(s):  
Knowledge Graph ◽  
World Knowledge ◽  
Training Time ◽  
Attention Networks ◽  
Convolutional Networks ◽  
Open World ◽  
Closed World ◽  
Proposed Model ◽  
Reasoning Tasks ◽  
Weighted Combination

Knowledge graph reasoning or completion aims at inferring missing facts based on existing ones in a knowledge graph. In this work, we focus on the problem of open-world knowledge graph reasoning—a task that reasons about entities which are absent from KG at training time (unseen entities). Unfortunately, the performance of most existing reasoning methods on this problem turns out to be unsatisfactory. Recently, some works use graph convolutional networks to obtain the embeddings of unseen entities for prediction tasks. Graph convolutional networks gather information from the entity’s neighborhood, however, they neglect the unequal natures of neighboring nodes. To resolve this issue, we present an attention-based method named as NAKGR, which leverages neighborhood information to generate entities and relations representations. The proposed model is an encoder-decoder architecture. Specifically, the encoder devises an graph attention mechanism to aggregate neighboring nodes’ information with a weighted combination. The decoder employs an energy function to predict the plausibility for each triplets. Benchmark experiments show that NAKGR achieves significant improvements on the open-world reasoning tasks. In addition, our model also performs well on the closed-world reasoning tasks.

Open-world knowledge graph completion with multiple interaction attention

2020 ◽  
Author(s):  
Lei Niu ◽  
Chenpeng Fu ◽  
Qiang Yang ◽  
Zhixu Li ◽  
Zhigang Chen ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
World Knowledge ◽  
Open World ◽  
Multiple Interaction

scholarly journals Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion

2020 ◽  
Author(s):  
Rajarshi Das ◽  
Ameya Godbole ◽  
Nicholas Monath ◽  
Manzil Zaheer ◽  
Andrew McCallum
Keyword(s):  
Knowledge Graph ◽  
Case Based Reasoning ◽  
World Knowledge ◽  
Open World ◽  
Case Based

scholarly journals Entity Alignment between Knowledge Graphs Using Attribute Embeddings

2019 ◽  
Vol 33 ◽  
pp. 297-304 ◽  
Author(s):  
Bayu Distiawan Trisedya ◽  
Jianzhong Qi ◽  
Rui Zhang
Keyword(s):  
Real World ◽  
Graph Embedding ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Large Numbers ◽  
Proposed Model ◽  
Alignment Task ◽  
Transitivity Rule ◽  
Knowledge Graphs

The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the knowledge graphs and generates attribute character embeddings. The attribute character embedding shifts the entity embeddings from two knowledge graphs into the same space by computing the similarity between entities based on their attributes. We use a transitivity rule to further enrich the number of attributes of an entity to enhance the attribute character embedding. Experiments using real-world knowledge bases show that our proposed model achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.

Sign in / Sign up

Export Citation Format

Share Document