A Web service clustering method based on topic enhanced Gibbs sampling algorithm for the Dirichlet Multinomial Mixture model and service collaboration graph

Hu, Qiang; Shen, Jiaji; Wang, Kun; Du, Junwei; Du, Yuyue

doi:10.1016/j.ins.2021.11.087

Cited by 21 publications

(9 citation statements)

References 32 publications

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“…In the preliminary work, it was verified through experiments that K-means++ algorithm is better than AGNES, BIRCH, GMM and other common clustering algorithms in Web services clustering under the same set of SFVs [25]. K-means++ algorithm shows excellent clustering performance with low requirements for computing resources.…”

Section: Web Service Clustering Algorithm Based On K-means++mentioning

confidence: 91%

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A Method to Enhance Web Service Clustering by Integrating Label-Enhanced Functional Semantics and Service Collaboration

Liu,

Wang,

et al. 2024

IEEE Access

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In Web service clustering, the service function vector (SFV) directly determines the quality of service (QoS) clustering. To improve service clustering performance, a method is proposed in this paper by integrating label-enhanced functional semantics and service collaboration. It improves the SFV from three aspects: generation model, corpus, and structural auxiliary information. At the generation model level, a Sentence-BERT is constructed based on singular value decomposition (SVD), to alleviate the anisotropy problem of BERT in vectorizing service descriptions. For corpus, the semantic features of SFV are supplemented by extracting specific named entities from service descriptions. Meanwhile, the service collaboration graph is established according to the collaboration relationship among Web services, which is conducive to the variational graph auto-encoders (VGAE) to realize service collaboration feature aggregation and further improve the SFV. Experiments show that the improved model, corpus and structural auxiliary information effectively enhance the SFV clustering. The proposed Web service clustering method is superior to the state-of-the-art methods.

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Section: Web Service Clustering Algorithm Based On K-means++mentioning

confidence: 91%

“…Kmeans was employed to cluster services based on the service embeddings. Hu et al [25] proposed a service clustering method combining service function similarity and collaboration similarity. They used the improved GSDMM model to generate high-quality SFV to enhance the accuracy of service function similarity analysis.…”

Section: Related Workmentioning

confidence: 99%

A Method to Enhance Web Service Clustering by Integrating Label-Enhanced Functional Semantics and Service Collaboration

Liu,

Wang,

et al. 2024

IEEE Access

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“…This differs from LDA which assumes that a document can have multiple topics in the beginning. Hu et al [ 56 ] showed that GSDMM has better performance than related methods for Web service clustering. The generative process for GSDMM can be expanded for the whole corpus as follows:…”

Section: Methodsmentioning

confidence: 99%

Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron

Lande,

Pillay,

Chandra

2023

PLoS ONE

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Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. It can provide, psychological, social and cultural insights for understanding human behaviour in extreme events such as the COVID-19 pandemic. In this paper, we use prominent deep learning-based language models for COVID-19 topic modelling taking into account data from the emergence (Alpha) to the Omicron variant in India. Our results show that the topics extracted for the subsequent waves had certain overlapping themes such as governance, vaccination, and pandemic management while novel issues aroused in political, social and economic situations during the COVID-19 pandemic. We also find a strong correlation between the major topics with news media prevalent during the respective time period. Hence, our framework has the potential to capture major issues arising during different phases of the COVID-19 pandemic which can be extended to other countries and regions.

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“…The output result corresponding to the ith node L (K) The loss at Kth time step rely on graph structure information and effectively captures correlations between node features [19]. However, GAT solely considers the connectivity of edges and does not fully leverage edge features.…”

Section: Incorporating Co-occurrence For Recognizing Disease Symptom ...mentioning

confidence: 99%

Recognition of Hotspot Words for Disease Symptoms Incorporating Contextual Weight and Co-Occurrence Degree

Liu,

Wang,

Chang

et al. 2024

Scientific Programming

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Identifying hotspot words associated with disease symptoms is paramount for disease prevention and diagnosis. In this study, we propose a novel method for hotspot word recognition in disease symptoms, integrating contextual weights and co-occurrence information. First, we establish the MDERank model, which incorporates contextual weights. This model identifies words that align well with comprehensive weights, forming a collection of disease symptom words. Next, we construct a graph network for disease symptom words within each time period. Utilizing the graph attention network model, we incorporate word co-occurrence degree to identify potential hotspot words associated with disease symptoms. We conducted experiments using user-generated posts from the Dingxiangyuan Forum as our data source. The results demonstrate that our proposed method significantly improves the extraction quality of disease symptom words compared to other existing methods. Furthermore, the performance of our constructed recognition model for disease symptom hotspot words surpasses that of alternative models.

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A Web service clustering method based on topic enhanced Gibbs sampling algorithm for the Dirichlet Multinomial Mixture model and service collaboration graph

Cited by 21 publications

References 32 publications

A Method to Enhance Web Service Clustering by Integrating Label-Enhanced Functional Semantics and Service Collaboration

A Method to Enhance Web Service Clustering by Integrating Label-Enhanced Functional Semantics and Service Collaboration

Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron

Recognition of Hotspot Words for Disease Symptoms Incorporating Contextual Weight and Co-Occurrence Degree

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