Review on Knowledge Graph Techniques 来自 知网 喜欢 0 阅读量: 548 作者:Z Xu,Y Sheng,L He,Y Wang 摘要: Knowledge graph technology is a critical part of artificial intelligence research. It establishes a knowledge base with the capacity of semantic processing and open interconnection in order to...
Section 5 discusses reasoning techniques that further utilize representation learning. Section 6 focuses on the techniques that perform reasoning based on neural network and reinforcement learning. Section 7 further explores the applications of such knowledge in downstream tasks, such as knowledge graph ...
The terms and background ideas relevant to graph deep learning are summarized in this section. Graph deep learning taxonomy in RS The present section introduces a taxonomy of GDL for RS, illustrated in Fig. 8, which classifies various techniques based on the features of the input data and the...
In order to cope with that, Graph Neural Network (GNN) methods have been proposed. GNNs learn entity embeddings, by recursively aggregating the representations of neighboring nodes. They essentially rely onmessage passing, according to which, each graph node recursively receives and aggregates features...
“ refers to the name of BioNTech vaccine that protects against COVID-19. The techniques used in NER can be classified into (a) knowledge-based techniques that rely on domain-specific knowledge and (b) advanced machine learning techniques that benefit from annotated data (in case of supervised...
By using machine learning techniques, thermodynamic and microstructural features, key features can be automatically extracted from large data sets with high predictive power. However, as the predictive power of machine learning strongly depends on the quantity and quality of labelled data sets, ways of...
(ILP)14, which has an explicit role for domain-knowledge being incorporated into learning. The simplest use of ILP14to incorporaten-ary relations in domain knowledge into a neural network relies on techniques that automatically “flatten” the domain-knowledge into a set of domain-specific ...
Integrating Context Knowledge :(1)Conditional Random Fields (例如CRFasRNN);(2)Dilated Convolutions (例如DeepLab);(3)Multi-scale Prediction ;(4)Feature Fusion ;(5)Recurrent Neural Networks。 一图胜千言。 用到的评价基准: 1.Execution Time 2. Memory Footprint 3.Accuracy :Pixel Accuracy (PA) ...
“ refers to the name of BioNTech vaccine that protects against COVID-19. The techniques used in NER can be classified into (a) knowledge-based techniques that rely on domain-specific knowledge and (b) advanced machine learning techniques that benefit from annotated data (in case of supervised...
This subsection introduces three types of KG reasoning approaches based on logic rules, specifically logic-based reasoning, statistics-based reasoning, and graph structure-based reasoning. 3.1.1. Reasoning based on logic Logic-based knowledge reasoning refers to directly using first-order logic (FOL) ...