Attentional Neural Factorization Machine for Web Services Classification via Exploring Content and Structural Semantics

Liang, Bowen; Kang, Guosheng; Liu, Jianxun; Cao, Buqing; Xiang, Jiayan

doi:10.1109/ijcnn55064.2022.9892320

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Structural feature-based service classification approaches focus on mining the structural relationships of Web services (Gao et al , 2022), such as service composition (Liang et al , 2022), tag sharing (Shi et al , 2021) and textual syntactic structure (Zhao et al , 2022). These relationships are represented as graphs, and structural features are extracted by network representation algorithms such as Node2vec (Grover and Leskovec, 2016), DeepWalk (Perozzi et al , 2014), GCN, GNN (Zhou et al , 2020) and other neural network-based graph embedding approaches.…”

Section: Related Workmentioning

confidence: 99%

LCPCWSC: a Web service classification approach based on label confusion and priori correction

Xue,

Zhang

2024

IJWIS

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Purpose With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue. Design/methodology/approach This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness. Findings Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively. Originality/value This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

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