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Accurate Portraits of Scientific Resources and Knowledge Service Components [article]

Yue Wang and Zhe Xue and Ang Li
2022 arXiv   pre-print
There are a large number of management and classification standards for existing scientific and technological resources, but these standards are difficult to completely cover all entities and associations  ...  There is a rich relationship network between resources, from which a large amount of cutting-edge scientific and technological information can be mined.  ...  Hierarchical multi-label classification is a special form of multi-label classification.  ... 
arXiv:2204.04883v1 fatcat:slsvxnz2obf2fc7imiol7skxce

Research on Domain Information Mining and Theme Evolution of Scientific Papers [article]

Changwei Zheng, Zhe Xue, Meiyu Liang, Feifei Kou, Zeli Guan
2022 arXiv   pre-print
How to effectively use the huge number of scientific papers to help researchers becomes a challenge.  ...  scientific and technological papers.  ...  Unlike traditional multi-label classification tasks, in hierarchical multi-label tasks the labels are organized into a hierarchy.  ... 
arXiv:2204.08476v1 fatcat:7cte3exhajbilbkvhktjgyvqha

A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation [article]

Dong Zhang, Shu Zhao, Zhen Duan, Jie Chen, Yangping Zhang, Jie Tang
2019 arXiv   pre-print
In this paper, we propose a Multi-Label Classification method using a hierarchical and transparent Representation named Hiepar-MLC.  ...  Further, we propose a simple multi-label-based reviewer assignment MLBRA strategy to select the appropriate reviewers.  ...  Natural Science Foundation of China (Grants #61876001, #61602003 and #61673020), the Provincial Natural Science Foundation of Anhui Province (#1708085QF156), and the Recruitment Project of Anhui University for  ... 
arXiv:1912.08976v1 fatcat:c2sb7yupjrgz3alzfudcflup2y

IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing

Yongqiang Zhao, Shirui Pan, Jia Wu, Huaiyu Wan, Huizhi Liang, Haishuai Wang, Huawei Shen
2020 IEEE Access  
., ''Neural tensor network for multi-label classification,'' proposes a new approach to multilabel classification, which employs a neural tensor network to explore the relations among the labels of neighbors  ...  ., ''MsCoa: Multi-step co-attention model for multi-label classification,'' proposes an improved multistep multiclassification model to mitigate the phenomenon of error prediction, label repetition, and  ... 
doi:10.1109/access.2020.3043060 fatcat:qbqk5f4ojvadlazhk2mc343sra

An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction [article]

Kyudam Choi, Yurim Lee, Cheongwon Kim, Minsung Yoon
2021 arXiv   pre-print
Our method consists of a language model for encoding the protein sequence and a Graph Convolutional Network (GCN) for representing GO terms.  ...  Our algorithm shows effectiveness in a large-scale graph by expanding the GO graph compared to previous models. Experimental results show that our method outperformed state-of-the-art PFP approaches.  ...  DEEPred created a multi-label classification model using deep neural network for each GO hierarchical level [10]. Each model could carry out five GO terms in most labels.  ... 
arXiv:2112.02810v1 fatcat:lhxbqku5y5gwhonv4g2sktm37a

Representation Learning for Recommender Systems with Application to the Scientific Literature

Robin Brochier
2019 Companion Proceedings of The 2019 World Wide Web Conference on - WWW '19  
This later offers a scientific watch tool, Peerus, addressing various issues, such as the real time recommendation of newly published papers or the search for active experts to start new collaborations  ...  However, most works on attributed network embedding pay too little attention to textual attributes and do not fully take advantage of recent natural language processing techniques.  ...  1 Digital Scientific Research Technology (DSRT ) and its web application Peerus: https: //  ... 
doi:10.1145/3308560.3314195 dblp:conf/www/Brochier19 fatcat:f3mxil6pj5aq3ofggkhqb6rvam

An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers [article]

Jie Song and Meiyu Liang and Zhe Xue and Junping Du and Kou Feifei
2022 arXiv   pre-print
Learning knowledge representation of scientific paper data is a problem to be solved, and how to learn the representation of paper nodes in scientific paper heterogeneous network is the core to solve this  ...  Based on the heterogeneous graph representation, this paper performs link prediction on the entire heterogeneous graph and obtains the relationship between the edges of the nodes, that is, the relationship  ...  This paper constructs a heterogeneous graph including 3025 papers (P), 5835 authors (A), and 56 topics (S). Experiment with the meta-path set {PAP, PSP}.  ... 
arXiv:2203.16751v1 fatcat:xtelw33xjbdwpmukle5m3zvrr4

Exploiting Global and Local Hierarchies for Hierarchical Text Classification [article]

Ting Jiang, Deqing Wang, Leilei Sun, Zhongzhi Chen, Fuzhen Zhuang, Qinghong Yang
2022 arXiv   pre-print
Hierarchical text classification aims to leverage label hierarchy in multi-label text classification.  ...  Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels.  ...  As a special case of multi-label text classification, HTC has various applications such as news categorization (Kowsari et al., 2017) and scientific paper classification (Lewis et al., 2004b) .  ... 
arXiv:2205.02613v1 fatcat:7sbc7wexbfc6raa57xvwcdrj6i

MATCH: Metadata-Aware Text Classification in A Large Hierarchy [article]

Yu Zhang, Zhihong Shen, Yuxiao Dong, Kuansan Wang, Jiawei Han
2021 arXiv   pre-print
In this paper, we bridge the gap by formalizing the problem of metadata-aware text classification in a large label hierarchy (e.g., with tens of thousands of labels).  ...  Multi-label text classification refers to the problem of assigning each given document its most relevant labels from the label set.  ...  To illustrate the scenario, Figure 1 takes a scientific paper on PubMed as an example.  ... 
arXiv:2102.07349v2 fatcat:tawngtzaj5d7dnmb43dz2ggxpy

LASIGE-BioTM at MESINESP2: entity linking with semantic similarity and extreme multi-label classification on Spanish biomedical documents

Pedro Ruas, Vitor D. T. Andrade, Francisco M. Couto
2021 Conference and Labs of the Evaluation Forum  
multi-label classification algorithm.  ...  The first module uses the entities recognized in text and then applies a graph-based entity linking model to link them to the DeCS vocabulary.  ...  The approach then applies a hierarchical multi-label model, which is a generalisation of the multi-class hierarchical softmax model.  ... 
dblp:conf/clef/RuasAC21 fatcat:simen6mt6bcj5mchl4dhx54mwq

Semi-supervised learning and graph neural networks for fake news detection

Adrien Benamira, Benjamin Devillers, Etienne Lesot, Ayush K. Ray, Manal Saadi, Fragkiskos D. Malliaros
2019 Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining  
To this extend, we opted for semi-supervised learning approaches. In particular, our work proposes a graph-based semi-supervised fake news detection method, based on graph neural networks.  ...  In this work, we focus on content-based methods for detecting fake news -casting the problem to a binary text classification one (an article corresponds to either fake news or not).  ...  Attention-based Graph Neural Network (AGNN) The attention-based Graph Neural Network (AGNN) [2] corresponds to a novel graph neural network architecture that removes all the intermediate fully-connected  ... 
doi:10.1145/3341161.3342958 dblp:conf/asunam/BenamiraDLRSM19 fatcat:llv66xw5bvcjbfb7n7ijb4s6ce

Structural Optimization Makes Graph Classification Simpler and Better [article]

Junran Wu, Jianhao Li, Yicheng Pan, Ke Xu
2021 arXiv   pre-print
Here, based on an optimization method, we investigate the feasibility of improving graph classification performance while simplifying the model learning process.  ...  We then present an implementation of the scheme in a tree kernel and a convolutional network to perform graph classification.  ...  Based on simplified encoding trees, we propose a novel feature combination scheme for graph classification, termed hierarchical reporting.  ... 
arXiv:2109.02027v1 fatcat:vah7q4dnyveflpxm4h6snopz2e

Reinforcement learning on graphs: A survey [article]

Nie Mingshuo, Chen Dongming, Wang Dongqi
2022 arXiv   pre-print
Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic  ...  As far as we know, this is the latest work on a comprehensive survey of GRL, this work provides a global view and a learning resource for scholars.  ...  On the other hand, PAAR [98] employs a multi-hop reasoning model based on hyperbolic knowledge graph embedding and RL for the hierarchical reasoning.  ... 
arXiv:2204.06127v2 fatcat:7wf6qxnxzza7xbiwjgjmrsrdjq

Learning Hierarchical Multi-label Classification Trees from Network Data [chapter]

Daniela Stojanova, Michelangelo Ceci, Donato Malerba, Sašo Džeroski
2013 Lecture Notes in Computer Science  
We present an algorithm for hierarchical multi-label classification (HMC) in a network context.  ...  Combining the hierarchical multi-label classification task with network prediction is thus not trivial and requires the introduction of the new concept of network autocorrelation for HMC.  ...  the method NHMC for hierarchical multi-label classification from network data.  ... 
doi:10.1007/978-3-642-40897-7_16 fatcat:uxyyuv6jmnc7dknbkga6nbeuaa

Representation Learning with Dual Autoencoder for Multi-label Classification

Yi Zhu, Yang Yang, Yun Li, Jipeng Qiang, Yunhao Yuan, Runmei Zhang
2021 IEEE Access  
[17] proposed a label graph superimposing method based on graph convolution network (GCN) for multi-label recognition, the knowledge graph is superimposed into statistical graph for label correlation  ...  [44] proposed a hierarchical graph transformer method for multi-label text classification, a multi-layer transformer structure and the hierarchical relationship of the labels are used for feature representations  ...  He is the author or co-author of more than 60 scientific papers. His research interests include pattern recognition, machine learning, multimedia search, and information fusion.  ... 
doi:10.1109/access.2021.3096194 fatcat:tukpui6eczdjnixhs2lbokmfum
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