Deep learning techniques are increasingly being applied to solve various machine learning tasks that use Knowledge Graphs as input data. However, these tec
Quaternion Knowledge Graph Embeddings(2019)[相比RotatE更进了一步,通过4元组的Hamilton Product来表示关系变换,比复数具备了更多的自由度。] Knowledge Graph Embeddings and Explainable AI(2020)[一篇综述性质文章,介绍了目前sota的KGE方法,并分析embedding与可解释性的关系和联系。] ...
Quaternion Knowledge Graph Embeddings(2019)[相比RotatE更进了一步,通过4元组的Hamilton Product来表示关系变换,比复数具备了更多的自由度。] Knowledge Graph Embeddings and Explainable AI(2020)[一篇综述性质文章,介绍了目前sota的KGE方法,并分析embedding与可解释性的关系和联系。] Reasoning(知识推理) 听起来高大上...
You can even generate embeddings from this graph (encompassing both its data and its structure) that can be used in machine learning pipelines or as an integration point to LLMs. Using knowledge graphs with large language models But a knowledge graph is only half the story. LLMs are the ...
You can even generate embeddings from this graph (encompassing both its data and its structure) that can be used in machine learning pipelines or as an integration point to LLMs. Using knowledge graphs with large language models But a knowledge graph is only half the story. LLMs are the ...
Natural Language Processing (NLP) techniques. Recently, considerable literature in this space has centered around the use of Graph Neural Networks (GNNs) to learn powerful embeddings which leverage topological structures in the KGs. Despite the successes this existing research has achieved, deep ...
+ Knowledge Graph Embeddings + Graph Neural Networks + Knowledge Graph Construction + Knowledge Graph Population and Information Extraction + Knowledge Graph Completion + Knowledge Graph Quality Assessment and Refinement + Knowledge Representation and Semantic Reasoning ...
Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user, viz. as a tool for personalized explainability. An important class of concept-based explainability methods is constructed with empirically defined concepts, indirectly ...
Then we apply the KG embedding method to obtain the embeddings of all entities and relations in ElementKG. b, Contrastive-based pre-training. We use an element-guided graph augmentation strategy based on element knowledge of ElementKG to convert the original molecular graph G into the augmented ...
In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to ...