WE-LDA: A Word Embeddings Augmented LDA Model for Web Services Clustering

Shi, Min; Liu, Jianxun; Zhou, Dong; Tang, Mingdong; Cao, Buqing

doi:10.1109/icws.2017.9

Cited by 77 publications

(38 citation statements)

References 16 publications

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“…Till now, lots of service clustering approaches have been proposed. According to the information that they used to characterize service similarity, they can be roughly divided into two categories: function-based service clustering approaches [1,[7][8][9][10][11] and nonfunction-based clustering approaches [12][13][14][15][16].…”

Section: Related Workmentioning

confidence: 99%

“…ey think that tags represent the functional characteristics of services, which can be combined with WSDL documents to much more accurately determine the functional category of services. Shi et al [9] first organized words in the service description of all services according to semantics using the Word2Vec, and then they proposed an improved clustering approach by considering the auxiliary function of the words which belong to the same cluster with the words in the service description document. Liu et al [10] first trained a preliminary SVM classifier based on a small set of manually labeled samples.…”

Section: Related Workmentioning

confidence: 99%

“…Chen et al [8] proposed an enhanced probabilistic topic model (WT-LDA), which can model Web service description documents and service tag information simultaneously so as to group services into clusters. Shi et al [9] proposed a Mashup-API-Tag probabilistic topic model to model the service description, label information, and the composite relationship information among services, so as to improve the accuracy of topic information extraction. Pan and Chai [1] proposed a novel mashup service clustering approach based on a structural similarity and a genetic algorithmbased clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Clustering Services Based on Community Detection in Service Networks

Zhou

Wang

2019

Mathematical Problems in Engineering

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Service-oriented computing has become a promising way to develop software by composing existing services on the Internet. However, with the increasing number of services on the Internet, how to match requirements and services becomes a difficult problem. Service clustering has been regarded as one of the effective ways to improve service matching. Related work shows that structure-related similarity metrics perform better than semantic-related similarity metrics in clustering services. erefore, it is of great importance to propose much more useful structure-related similarity metrics to improve the performance of service clustering approaches. However, in the existing work, this kind of work is very rare. In this paper, we propose a SCAS (service clustering approach using structural metrics) to group services into different clusters. SCAS proposes a novel metric A2S (atomic service similarity) to characterize the atomic service similarity as a whole, which is a linear combination of C2S (compositesharing similarity) and A3S (atomic-service-sharing similarity). en, SCAS applies a guided community detection algorithm to group atomic services into clusters. Experimental results on a real-world data set show that our SCAS performs better than the existing approaches. Our A2S metric is promising in improving the performance of service clustering approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Clustering Services Based on Community Detection in Service Networks

Zhou

Wang

2019

Mathematical Problems in Engineering

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“…However, the description documents of Mashup and service usually are short, and their corpuses are insufficient. In the field of information retrieval, some researchers exploit Word2vec [8] to expand short text into long text, in order that topic model can effectively estimate the latent topics of text for more accurate information searching. e Word2vec model is proposed by Google [8], which can process large-scale text corpus and generate word embeddings vector with high efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…In the field of information retrieval, some researchers exploit Word2vec [8] to expand short text into long text, in order that topic model can effectively estimate the latent topics of text for more accurate information searching. e Word2vec model is proposed by Google [8], which can process large-scale text corpus and generate word embeddings vector with high efficiency. In this paper, we exploit Word2vec to extend the description document of Mashup and mobile service to build a dense word embeddings vector representation for more accurate topic modelling.…”

Section: Introductionmentioning

confidence: 99%

Mobile Service Recommendation via Combining Enhanced Hierarchical Dirichlet Process and Factorization Machines

Cao

Liu

et al. 2019

Mobile Information Systems

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Recently, Mashup is becoming a promising software development method in the mobile service computing environment, which enables software developers to compose existing mobile services to create new or value-added composite RESTful web application. Due to the rapid increment of mobile services on the Internet, it is difficult to find the most suitable services for building user-desired Mashup application. In this paper, we integrate word embeddings enhanced hierarchical Dirichlet process and factorization machines to recommend mobile services to build high-quality Mashup application. This method, first of all, extends the description documents of Mashup applications and mobile services by using Word2vec tool and derives latent topics from the extended description documents of Mashup and mobile services by exploiting the hierarchical Dirichlet process. Secondly, the factorization machine is applied to train these latent topics to predict the probability of mobile services invoked by Mashup and recommend mobile services with high-quality for Mashup development. Finally, the performance of the proposed method is comprehensively evaluated. The experimental results indicate that compared with the existing recommendation methods, the proposed method has significant improvements in MAE and RMSE.

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Web service classification based on information gain theory and bidirectional long short‐term memory with attention mechanism

Zhang

Liu

Cao

et al. 2021

Concurrency and Computation

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Summary With the increasing number of Web services, Web service discovery for service‐oriented application development has become more important. Clustering or classifying Web services according to their functionalities is an effective way for Web service discovery. Extracting latent topic features from service description by exploiting topic model can improve the accuracy of service classification. However, most of them simply treat the description document as a set of flat word features without considering the varying importance of different features as well as sequential relations between features. In this article, we proposed a Web service classification approach based on information gain theory and bidirectional long short‐term memory with attention mechanism for accuracy Web service classification by considering fine‐grained factors implicit in Web service description. The comparative experiments are performed on ProgrammableWeb dataset, and show that the proposed method achieves a significant improvement compared with baseline methods.

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WE-LDA: A Word Embeddings Augmented LDA Model for Web Services Clustering

Cited by 77 publications

References 16 publications

Clustering Services Based on Community Detection in Service Networks

Clustering Services Based on Community Detection in Service Networks

Mobile Service Recommendation via Combining Enhanced Hierarchical Dirichlet Process and Factorization Machines

Web service classification based on information gain theory and bidirectional long short‐term memory with attention mechanism

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