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Clustering web services would greatly boost the ability of web service search engine to retrieve relevant ones. An important restriction of traditional studies on web service clustering is that researchers focused on utilizing web services' WSDL (Web Service Description Language) documents only. The singleness of data source limits the accuracy of clustering. Recently, web service search engines such as Seekda! allow users to manually annotate web services using so called tags, which describe the function of the web service or provide additional contextual and semantical information. In this paper, we propose a novel approach called WTCluster, in which both WSDL documents and tags are utilized for web service clustering. Furthermore, we present and evaluate two tag recommendation strategies to improve the performance of WTCluster. The comprehensive experiments based on a dataset consists of 15,968 real web services demonstrate the effectiveness of WTCluster and tag recommendation strategies.

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• It uses service ontology to enhance service clustering e±ciency, and the accuracy of clustering will be determined by the accuracy of service ontology. Chen et al. propose a service clustering method called WTCluster [5]. It mainly uses the WSDL document and tags to cluster services and two kinds of labels evaluation and recommendation strategies are given.

[Show abstract] [Hide abstract] ABSTRACT: In the era of service-oriented software engineering (SOSE), service clustering is used to organize Web services, and it can help to enhance the efficiency and accuracy of service discovery.

In order to improve the efficiency and accuracy of service clustering, this paper uses the self-join operation in relational database (RDB) to realize Web service clustering. Based on storing service information, it does the self-join operation towards the Input, Output, Precondition, Effect (IOPE) tables of Web services, which can enhance the efficiency of computing services similarity. The semantic reasoning relationship between concepts and the concept status path are used to do the calculation, which can improve the calculation accuracy. Finally, we use experiments to validate the effectiveness of the proposed methods.

Full-text · Article · Oct 2015

• In our prior work [31], we propose two simple tag recommendation strategies, i.e. Sum and Vote. In this section, we compare the performance of WSTRec with Sum and Vote.Table 4 shows the S@K performance of three tag recommendation strategies, where G means the number of initial tags which the target Web service contains.

[Show abstract] [Hide abstract] ABSTRACT: Clustering Web services would greatly boost the ability of Web service search engine to retrieve relevant services. The performance of traditional Web service description language (WSDL)-based Web service clustering is not satisfied, due to the singleness of data source. Recently, Web service search engines such as Seekda! allow users to manually annotate Web services using tags, which describe functions of Web services or provide additional contextual and semantical information.

In this paper, we cluster Web services by utilizing both WSDL documents and tags. To handle the clustering performance limitation caused by uneven tag distribution and noisy tags, we propose a hybrid Web service tag recommendation strategy, named WSTRec, which employs tag co-occurrence, tag mining, and semantic relevance measurement for tag recommendation. Extensive experiments are conducted based on our real-world dataset, which consists of 15,968 Web services. The experimental results demonstrate the effectiveness of our proposed service clustering and tag recommendation strategies. Specifically, compared with traditional WSDL-based Web service clustering approaches, the proposed approach produces gains in both precision and recall for up to 14 % in most cases.

Full-text · Article · Jan 2014

• 3 These approaches and websites largely exploit keyword-based search techniques and are insufficient to fully describe the functionalities of web services. Furthermore, accommodation for non-functional characteristics such as quality of service (QoS) of web services during the service selection and recommendation are very limited [16], [17]. In a recent work by Zheng et al. [8], [18], a web service search engine is designed and developed that ranks web services not only by functional similarities to a user's query, but also by non-functional QoS characteristics of web services.

[Show abstract] [Hide abstract] ABSTRACT: The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With increasing adoption and presence of web services, designing novel approaches for efficient and effective web service recommendation has become of paramount importance. Most existing web service discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. It would be desirable for a system to recommend web services that align with users’ interests without requiring the users to explicitly specify queries. Recent research efforts on web service recommendation center on two prominent approaches: collaborative filtering and content-based recommendation. Unfortunately, both approaches have some drawbacks, which restrict their applicability in web service recommendation. In this paper, we propose a novel approach that unifies collaborative filtering and content-based recommendations. In particular, our approach considers simultaneously both rating data (e.g. QoS) and semantic content data (e.g. functionalities) of web services using a probabilistic generative model. In our model, unobservable user preferences are represented by introducing a set of latent variables, which can be statistically estimated. To verify the proposed approach, we conduct experiments using 3,693 real-world web services. The experimental results show that our approach outperforms the state-of-the-art methods on recommendation performance.

Full-text · Article · Jan 2014