A Persistent Naming System Based on Graph Transformation Rules
CSRN, 2018 ISBN : 978-80-86943-38-1; International ...
M. Weinstein David +3 more
openalex +4 more sources
Neighbor Contrastive Learning on Learnable Graph Augmentation [PDF]
AAAI Conference on Artificial Intelligence, 2023 Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph ...
X. Shen +4 more
semanticscholar +1 more source
Modelling Calculi with Name Mobility using Graphs with Equivalences [PDF]
Electronic Notes in Theoretical Computer Science, 2007 AbstractIn the theory of graph rewriting, the use of coalescing rules, i.e., of rules which besides deleting and generating graph items, can coalesce some parts of the graph, turns out to be quite useful for modelling purposes, but, at the same time, problematic for the development of a satisfactory partial order concurrent semantics for rewrites ...
Paolo Baldan +2 more
openalex +2 more sources
DOZEN: Cross-Domain Zero Shot Named Entity Recognition with Knowledge Graph
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021 With the new developments of natural language processing, increasing attention has been given to the task of Named Entity Recognition (NER). However, the vast majority of work focus on a small number of large-scale annotated datasets with a limited ...
Hoang Van Nguyen +2 more
semanticscholar +1 more source
HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer [PDF]
The Web Conference, 2023 Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc.
Qiheng Mao +3 more
semanticscholar +1 more source
Dictionary-based matching graph network for biomedical named entity recognition
Scientific Reports, 2023 Biomedical named entity recognition (BioNER) is an essential task in biomedical information analysis. Recently, deep neural approaches have become widely utilized for BioNER.
Yinxia Lou, Xun Zhu, Kai Tan
doaj +1 more source
Multi-Behavior Recommendation with Cascading Graph Convolution Networks [PDF]
The Web Conference, 2023 Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users’ potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues
Zhiyong Cheng +5 more
semanticscholar +1 more source
A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation
Information, 2022 Knowledge graph (KG) helps to improve the accuracy, diversity, and interpretability of a recommender systems. KG has been applied in recommendation systems, exploiting graph neural networks (GNNs), but most existing recommendation models based on GNNs ...
Xi Liu, Rui Song, Yuhang Wang, Hao Xu
doaj +1 more source
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning [PDF]
Knowledge Discovery and Data Mining, 2021 Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN).
Xiao Wang, Nian Liu, Hui-jun Han, C. Shi
semanticscholar +1 more source
AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021 Artefacts that differentiate spoofed from bona-fide utterances can reside in specific temporal or spectral intervals. Their reliable detection usually depends upon computationally demanding ensemble systems where each subsystem is tuned to some specific ...
Jee-weon Jung +7 more
semanticscholar +1 more source