Mining and clustering service goals for RESTful service discovery

Zhang, Neng; Wang, Jian; He, Ke; Zheng, Li; Huang, Yiwang

doi:10.1007/s10115-018-1171-4

Cited by 26 publications

(9 citation statements)

References 41 publications

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“…We compute TCD for each word form line (6) to line (12). The word's context weight is obtained by its context fitness and TF-IDF in line (8). The tool NLTK.…”

Section: Topic Contribution Degreementioning

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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“…We compute TCD for each word form line (6) to line (12). The word's context weight is obtained by its context fitness and TF-IDF in line (8). The tool NLTK.…”

Section: Topic Contribution Degreementioning

confidence: 99%

“…Currently, the function descriptions of Web API services are mostly organized by natural language [8]. The developers presented a short piece of text to describe the function, performance and usage of the Web API service.…”

Section: Introducementioning

confidence: 99%

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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“…Many efforts have been devoted to cluster Web services to facilitate service discovery [4], [22]- [25]. Platzer et al [24] proposed a statistical clustering approach that can improve an existing distributed vector space search engine for Web services based on the possibility of dynamically calculating clusters of similar services.…”

Section: Related Workmentioning

confidence: 99%

Exploiting User Tagging for Web Service Co-Clustering

et al. 2019

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We propose a novel Web services clustering framework by considering the word distribution of WSDL documents and tags. Typically, tags are annotated to Web services by users for organization. In this paper, four strategies are proposed to integrate the tagging data and WSDL documents in the process of service clustering. Tagging data is inherently uncontrolled, ambiguous, and overly personalized. Two tag recommendation approaches are proposed to improve the tagging data quality and service clustering performance. Comprehensive experiments demonstrate the effectiveness of the proposed framework using a real-world dataset. INDEX TERMS Web service, WSDL documents clustering, co-clustering, tag recommendation.

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“…rough the rapid development of Internet technology [1], clustering web services has become an effective method to solving service discovery [2][3][4], service composition [5,6], and service recommendation [7]. Firstly, there are increasing enterprises and institutions that encapsulate software functions or data into web services and publish them to the network.…”

Section: Introductionmentioning

confidence: 99%

A Web Service Clustering Method Based on Semantic Similarity and Multidimensional Scaling Analysis

Shan

2021

Scientific Programming

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Clustering web services is an effective method to solving service computing problems. The key insight behind it is to extract the vectors based on the service description documents. However, the brevity of natural language service description documents typically complicates the vector construction process. To circumvent the difficulty, we propose a novel web service clustering method to vectorize documents based on the semantic similarity, which can be calculated via WordNet and multidimensional scaling (WMS) analysis. We utilize the dataset from the ProgrammableWeb to conduct extensive experiments and achieve prominent advances in precision, recall, and F-measure.

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Mining and clustering service goals for RESTful service discovery

Cited by 26 publications

References 41 publications

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

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

Exploiting User Tagging for Web Service Co-Clustering

A Web Service Clustering Method Based on Semantic Similarity and Multidimensional Scaling Analysis

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