MI-KGNN: Exploring Multi-dimension Interactions for Recommendation Based on Knowledge Graph Neural Networks

2020 ◽  
pp. 155-170
Author(s):  
Zilong Wang ◽  
Zhu Wang ◽  
Zhiwen Yu ◽  
Bin Guo ◽  
Xingshe Zhou
Keyword(s):  
Neural Networks ◽  
Knowledge Graph ◽  
Graph Neural Networks

scholarly journals Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation

2020 ◽  
Vol 34 (01) ◽  
pp. 222-229
Author(s):  
Zequn Sun ◽  
Chengming Wang ◽  
Wei Hu ◽  
Muhao Chen ◽  
Jian Dai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Attention Mechanism ◽  
Knowledge Graph ◽  
Neighborhood Information ◽  
Graph Alignment ◽  
Gating Mechanism ◽  
Neighborhood Structures ◽  
Knowledge Graphs ◽  
End To End ◽  
Graph Neural Networks

Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart entities usually have non-isomorphic neighborhood structures, which easily causes GNNs to yield different representations for them. To tackle this problem, we propose a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner. As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures. It employs an attention mechanism to highlight helpful distant neighbors and reduce noises. Then, it controls the aggregation of both direct and distant neighborhood information using a gating mechanism. We further propose a relation loss to refine entity representations. We perform thorough experiments with detailed ablation studies and analyses on five entity alignment datasets, demonstrating the effectiveness of AliNet.

scholarly journals BERT-INT:A BERT-based Interaction Model For Knowledge Graph Alignment

2020 ◽  
Author(s):  
Xiaobin Tang ◽  
Jing Zhang ◽  
Bo Chen ◽  
Yang Yang ◽  
Hong Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Side Information ◽  
Interaction Model ◽  
Experimental Results ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Graph Alignment ◽  
Knowledge Graphs ◽  
Graph Neural Networks

Knowledge graph alignment aims to link equivalent entities across different knowledge graphs. To utilize both the graph structures and the side information such as name, description and attributes, most of the works propagate the side information especially names through linked entities by graph neural networks. However, due to the heterogeneity of different knowledge graphs, the alignment accuracy will be suffered from aggregating different neighbors. This work presents an interaction model to only leverage the side information. Instead of aggregating neighbors, we compute the interactions between neighbors which can capture fine-grained matches of neighbors. Similarly, the interactions of attributes are also modeled. Experimental results show that our model significantly outperforms the best state-of-the-art methods by 1.9-9.7% in terms of HitRatio@1 on the dataset DBP15K.

scholarly journals Cybersecurity Knowledge Graph Improvement with Graph Neural Networks

2021 ◽  
Author(s):  
Soham Dasgupta ◽  
Aritran Piplai ◽  
Priyanka Ranade ◽  
Anupam Joshi
Keyword(s):  
Neural Networks ◽  
Knowledge Graph ◽  
Graph Neural Networks

scholarly journals Multi-hop Attention Graph Neural Networks

2021 ◽  
Author(s):  
Guangtao Wang ◽  
Rex Ying ◽  
Jing Huang ◽  
Jure Leskovec
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Performance Metrics ◽  
State Of The Art ◽  
Structural Information ◽  
Representation Learning ◽  
Attention Mechanism ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Graph Neural Networks

Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes. However, such attention mechanism does not account for nodes that are not directly connected but provide important network context. Here we propose Multi-hop Attention Graph Neural Network (MAGNA), a principled way to incorporate multi-hop context information into every layer of attention computation. MAGNA diffuses the attention scores across the network, which increases the receptive field for every layer of the GNN. Unlike previous approaches, MAGNA uses a diffusion prior on attention values, to efficiently account for all paths between the pair of disconnected nodes. We demonstrate in theory and experiments that MAGNA captures large-scale structural information in every layer, and has a low-pass effect that eliminates noisy high-frequency information from graph data. Experimental results on node classification as well as the knowledge graph completion benchmarks show that MAGNA achieves state-of-the-art results: MAGNA achieves up to 5.7% relative error reduction over the previous state-of-the-art on Cora, Citeseer, and Pubmed. MAGNA also obtains the best performance on a large-scale Open Graph Benchmark dataset. On knowledge graph completion MAGNA advances state-of-the-art on WN18RR and FB15k-237 across four different performance metrics.

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Conversational Emotion Recognition Using Self-Attention Mechanisms and Graph Neural Networks

2020 ◽  
Author(s):  
Zheng Lian ◽  
Jianhua Tao ◽  
Bin Liu ◽  
Jian Huang ◽  
Zhanlei Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Graph Neural Networks
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