[PDF] Top 20 Relation Embedding with Dihedral Group in Knowledge Graph
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Relation Embedding with Dihedral Group in Knowledge Graph
... In RESCAL (Nickel et al., 2011) each relation is represented by a full-rank matrix. As a downside, there is a huge number of parameters in RESCAL making the model prone to overfitting. A totally symmetric DistMult ... See full document
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Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
... (subject, relation, object) (or (s, r, o) for short), where s and o are two entities and r is a relation the fact ...KG embedding models are proposed to facilitate KG com- pletion tasks, ... See full document
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TransGate: Knowledge Graph Embedding with Shared Gate Structure
... on embedding knowledge graphs into continuous vector spaces for knowledge graph ...optimize embedding and reduce parameters ...improve embedding and the great ability of gate ... See full document
8
Transition-based Knowledge Graph Embedding with Relational Mapping Properties
... ONE-TO-ONE relation instances, as minimizing the global loss function will impose h + r close to ...er relation instances with multi-mapping properties, ...the embedding space and also hard to be ... See full document
10
Long tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
... transfer knowledge from data-rich and semantically similar head classes to data-poor tail classes (Wang et ...long-tail relation /peo- ple/deceased person/place of burial and head relation ... See full document
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Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
... isting knowledge graphs (KGs), and the com- pletion of KG which aims to predict links be- tween entities is ...rect relation between nodes and ignore the re- lation paths which contain useful information ... See full document
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Knowledge Graph Embedding via Dynamic Mapping Matrix
... and relation in knowledge ...every relation is re- garded as translation in the embedding ...the embedding h is close to the embedding t by adding the embedding r, that is ... See full document
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Improving Knowledge Graph Embedding Using Simple Constraints
... Recently, the concept of knowledge graph em- bedding has been presented and quickly become a hot research topic. The key idea there is to embed components of a KG (i.e., entities and relations) into a ... See full document
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Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process
... This paper presents TMKGE, a Bayesian nonpara- metric model based on hierarchical Dirichlet pro- cess for incorporation of entity embeddings from external knowledge graphs into topic modeling. The proposed method ... See full document
11
A Translation Based Knowledge Graph Embedding Preserving Logical Property of Relations
... various knowledge-embedding models shows the state-of- the-art performance of knowledge graph completion (Bordes et ...of knowledge triples (h, r, t) composed of a relation (r) ... See full document
10
Improved Knowledge Graph Embedding Using Background Taxonomic Information
... In relational learning, embeddings for entities and rela- tionships are used to generalize from existing data. These embeddings are often formulated in terms of tensor factor- ization (Nickel, Tresp, and Kriegel 2012; ... See full document
8
A Capsule Network based Embedding Model for Knowledge Graph Completion and Search Personalization
... an embedding model, named CapsE, exploring a capsule net- work to model relationship triples (subject, re- lation, ...the embedding of an element in the ...for knowledge graph comple- tion on ... See full document
10
TransG : A Generative Model for Knowledge Graph Embedding
... the embedding space to make it semantically ...between knowledge and texts, with a loss function for jointly modeling knowledge graph and text re- ...with relation types such as 1-N and ... See full document
10
Knowledge Graph and Text Jointly Embedding
... both knowledge and text, which is composed of three components: the knowledge model, text model, and alignment ...the knowledge model and text model use the same core transla- tion assumption for the ... See full document
11
Knowledge graph embedding by dynamic translation
... ABSTRACT Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued ...in knowledge graphs, and improve ... See full document
10
Semantically Smooth Knowledge Graph Embedding
... bedding Knowledge Graphs (KGs) con- sisting of entities and relations into low- dimensional vector ...the embedding space, we propose Semantically Smooth Embedding ...the embedding space to be ... See full document
11
GAKE: Graph Aware Knowledge Embedding
... entity, relation, tail entity). These knowledge bases have benefited many applications, such as web search and question ...meanwhile, knowledge base embedding, which aims to learn a ... See full document
11
Context Dependent Knowledge Graph Embedding
... Perozzi et al. (2014) and Goikoetxea et al. (2015) have proposed similar ideas, i.e., to gener- ate random walks from online social networks or from the WordNet knowledge base, and then em- ploy word ... See full document
6
Knowledge Graph Embedding with Numeric Attributes of Entities
... inal knowledge in the KG. KG embedding models achieve good performance in KG completion in terms of efficiency and ...KG embedding approach (Bordes et ...entity, relation, tail entityi ... See full document
5
Knowledge Graph Embedding for Ecotoxicological Effect Prediction
... Risk assessment methods require large amounts of effect data to efficiently predict long term risk for the ecosystems. The data must cover a minimum of the chemicals found when analysing water samples from the ecosystem, ... See full document
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