More and more work has focused on incorporating different kinds of literals into Knowledge Graph to promote the performance of knowledge embedding.
This paper proposes two methods to incorporate attributes semantics into knowledge graph embeddings from two perspectives: LiteralEAN and literalE-AT, ...
In this paper, we propose two methods to incorporate attributes semantics into knowledge graph embeddings from two perspectives: LiteralE-. AN and literalE-AT.
The representation learning of heterogeneous graphs (HGs) embeds the rich structure and semantics of such graphs into a low-dimensional space and facilitates ...
Jul 27, 2024 · This paper investigates the advantages of representing and processing semantic knowledge extracted into graphs within the emerging paradigm of semantic ...
Knowledge graphs are composed of different elements: en- tity nodes, relation edges, and literal nodes. Each literal node contains an entity's attribute value ( ...
Knowledge graphs are composed of different elements: entity nodes, relation edges, and literal nodes. Each literal node contains an entity's attribute value ...
This paper proposes two methods to incorporate attributes semantics into knowledge graph embeddings from two perspectives: LiteralEAN and literalE-AT.
We propose a Knowledge Graph Embedding Based on Semantic Hierarchy (SHKE), which fully considers the information of knowledge graph.
Missing: Incorporating | Show results with:Incorporating
In this paper, we propose a shared learning approach to learn semantic attributes of images by combining a knowledge graph embedding model with the recognized ...