Web Service Clustering Approaches to Enhance Service Discovery: A Review

Agarwal, Nidhi; Sikka, Geeta; Awasthi, Lalit Kumar

doi:10.1007/978-981-15-8297-4_3

Cited by 2 publications

(3 citation statements)

References 40 publications

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“…pos_tag and stanfordcorenlp are used to determine the part-of-speech for every word in a service description text. The TCD value is computed based on the word's context weight and POS weight in line (9). It should be noted that the POS weight is set as a super parameter.…”

Section: Topic Contribution Degreementioning

confidence: 99%

“…For the word n in s.d, we first find its the maximum topic probability distribution argmax(W d,n ) and its related topic (denoted as kmax-topic). Then the correction factor for the word n is presented as PICF(d, n) in line (9). Here, we introduce λ to balance the order of magnitude for PICF.…”

Section: Picf-ldamentioning

confidence: 99%

“…The developers presented a short piece of text to describe the function, performance and usage of the Web API service. It is difficult to obtain the feature information about Web API services for the lack of tags in the service description texts [9]. To get the key feature of service description text, many researchers have applied topic models to generate topic vectors for Web API services [10][11][12][13].…”

Section: Introducementioning

confidence: 99%

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PICF-LDA: a topic enhanced LDA with probability incremental correction factor for Web API service clustering

Shen

Wen

2022

J Cloud Comp

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Web API is a popular way to organize network services in cloud computing environment. However, it is a challenge to find an appropriate service for the requestor from massive Web API services. Service clustering can improve the efficiency of service discovery for its ability of reducing search space. Latent Dirichlet Allocation (LDA) is the most frequently used topic model in service clustering. To further improve the topic representation ability of LDA, we propose a new variant model of LDA with probability incremental correction factor (PICF-LDA) to generate the high-quality service representation vectors (SRVs) for Web API services. We first compute the words’ topic contribution degree (TCD) in the service description text by its context weight and part-of-speech (POS) weight. Then the probability incremental correction factor (PICF) for a word is designed based on TCD and the word’s maximum topic probability value. PICF is used to correct the probability distributions in SRVs. Experiments show that PICF-LDA has a better performance than LDA, the variant LDA models and other state-of-the-art topic models in service clustering.

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Section: Topic Contribution Degreementioning

confidence: 99%

Section: Picf-ldamentioning

confidence: 99%