Service Clustering Method Based on Knowledge Graph Representation Learning

2021 ◽  
pp. 17-31
Author(s):  
Bo Jiang ◽  
Xuejun Xu ◽  
Junchen Yang ◽  
Tian Wang
Keyword(s):  
Representation Learning ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Clustering Method ◽  
Service Clustering

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.

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