SRaSLR: A Novel Social Relation Aware Service Label Recommendation Model

Zhu, Yeqi; Liu, Mingyi; Tu, Zhiying; Su, Tonghua; Wang, Zhongjie

doi:10.1109/icws53863.2021.00024

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…The reason is that BERT is context-aware to extract word-level and sentence-level representations and then output better semantic features. Additionally, BERT(cls) outperforms ServeNet-BERT because neural networks such as CNN may influence the overall semantic representations of service descriptions in the BERT model, thereby affecting its classification performance (Zhu et al , 2021). Finally, it is observed from the experiment results that LCPCWSC achieves the best service classification performance across three evaluation metrics compared with eight state-of-the-art approaches.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

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

Xue,

Zhang

2024

IJWIS

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“…However, many other social information, such as social relations which can promote users to mine latent knowledge, have not been considered. Zhu et al (2021) proposed a new model SRaSLR, which is a type of social-aware service label recommendation model. There are invocation and dependency relations between services, and these relations make services naturally constitute a service social network.…”

Section: Social-aware Workflow Fragment Discoverymentioning

confidence: 99%

“…While workflow repositories, such as myExperiment, have been constructed for decades, there still have insufficient socially relevant data on developers. As a result, current techniques focus on gathering and applying certain social information, such as developer reputation, to facilitate the discovery accuracy of appropriate workflows and services (Qiao et al, 2019;Khelloufi et al, 2021;Zhu et al, 2021). In fact, more relations between services (Herbold et al, 2021), and their positive or negative links on workflow fragments discovery and recommendation, have not been explored extensively.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph

et al. 2022

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Constructing a novel bioinformatic workflow by reusing and repurposing fragments crossing workflows is regarded as an error-avoiding and effort-saving strategy. Traditional techniques have been proposed to discover scientific workflow fragments leveraging their profiles and historical usages of their activities (or services). However, social relations of workflows, including relations between services and their developers have not been explored extensively. In fact, current techniques describe invoking relations between services, mostly, and they can hardly reveal implicit relations between services. To address this challenge, we propose a social-aware scientific workflow knowledge graph (S2KG) to capture common types of entities and various types of relations by analyzing relevant information about bioinformatic workflows and their developers recorded in repositories. Using attributes of entities such as credit and creation time, the union impact of several positive and negative links in S2KG is identified, to evaluate the feasibility of workflow fragment construction. To facilitate the discovery of single services, a service invoking network is extracted form S2KG, and service communities are constructed accordingly. A bioinformatic workflow fragment discovery mechanism based on Yen’s method is developed to discover appropriate fragments with respect to certain user’s requirements. Extensive experiments are conducted, where bioinformatic workflows publicly accessible at the myExperiment repository are adopted. Evaluation results show that our technique performs better than the state-of-the-art techniques in terms of the precision, recall, and F1.

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TagTag: A Novel Framework for Service Tags Recommendation and Missing Tag Prediction

Chen

Liu

et al. 2022

Service-Oriented Computing

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SRaSLR: A Novel Social Relation Aware Service Label Recommendation Model

Cited by 7 publications

References 20 publications

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

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

Bioinformatic workflow fragment discovery leveraging the social-aware knowledge graph

TagTag: A Novel Framework for Service Tags Recommendation and Missing Tag Prediction

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