To address this challenge, a number of knowledge graph completion (KGC) methods have been developed using low-dimensional graph embeddings. Most existing ...
Leveraging Lexical Semantic Information for Learning Concept ...
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Jul 18, 2019 · The paper studies aiming at combing the encyclopedic KG and the lexical KG for knowledge graph completion. The paper constructs multiple ...
Aug 1, 2019 · To address this challenge, a number of knowledge graph completion (KGC) methods have been developed using low-dimensional graph embeddings. Most ...
A novel KGC method is proposed, that integrates the structured information in encyclopaedia KG and the entity concepts in lexical KG, which describe the ...
To address this challenge, a number of knowledge graph completion (KGC) methods have been developed using low-dimensional graph embeddings. Most existing ...
Bibliographic details on Leveraging Lexical Semantic Information for Learning Concept-Based Multiple Embedding Representations for Knowledge Graph ...
Recent work in learning vector-space em- beddings for multi-relational data has fo- cused on combining relational information derived from knowledge bases ...
This work proposes an approach that integrates the structured information and entity types which describe the categories of entities and utilizes type-based ...
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May 17, 2024 · Representation learning aims to learn a model that extracts relevant and semantically rich features from the underlying data. ... In other words, ...
A scalable commonsense-aware framework for multi-view knowledge graph completion (CAKE) [19] leverages commonsense concepts of entities to improve the quality ...