Web service clustering using text mining techniques

Liu, Wei; Wong, Wilson

doi:10.1504/ijaose.2009.022944

Cited by 112 publications

(61 citation statements)

References 18 publications

(20 reference statements)

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“…Cosine similarity usually focuses on plain text, whereas Web services can contain much more complex structures, often with very little textual description. (Elgazzar, Hassan et al, 2010;Liu & Wong, 2009) combined string-based similarity methods such as structure matching with a corpus-based method based on NGD to measure the similarity of Web service features and to cluster them appropriately. However, structure matching may not accurately identify the semantic similarity among terms because of the heterogeneity and independence of service sources.…”

Section: Web Service Clusteringmentioning

confidence: 99%

“…Several methods have been used to compute the feature similarity in current functionally based clustering approaches, such as those using string-based methods like cosine similarity (Platzer, Rosenberg et. al., 2009), the corpus-based methods like normalized Google distance (NGD) (Elgazzar, Hassan et al, 2010;Liu & Wong, 2009), knowledge-based methods like ontology methods (Xie, Chen et al, 2011;Wagner, Ishikawa et al, 2011) and hybrid term similarity (HTS) (kumara, paik et al, 2013) methods. These methods have their own drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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Improving Web Service Clustering Through Post Filtering to Bootstrap the Service Discovery

Kumara¹,

Paik²,

Koswatte³

et al. 2014

STSC

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Web service clustering is one of a very efficient approach to discover Web services efficiently. Current approaches use similarity-distance measurement methods such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information, shortage of high-quality ontologies and encoding fine-grained information. Thus, the approaches couldn't identify the correct clusters for some services and placed them in wrong clusters. As a result of this, cluster performance is reduced. This paper proposes post-filtering approach to increase the performance of clusters by rearranging services incorrectly clustered. Our approach uses context aware similarity method that learns domain context by machine learning to produce models of context for terms retrieved from the Web in the filtering process to calculate the service similarity. We applied post filtering approach to hybrid term similarity based clustering approach that we proposed in our previous work. Experimental results show that our post-filtering approach works efficiently.

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Section: Web Service Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Web Service Clustering Through Post Filtering to Bootstrap the Service Discovery

Kumara¹,

Paik²,

Koswatte³

et al. 2014

STSC

View full text Add to dashboard Cite

show abstract

“…A lot of research efforts have been devoted in utilizing WSDL documents and Web service clustering [28], [19], [18], [12], [11] has been demonstrated as an effective mechanism to boost the performance of Web services discovery. Dong et al [11] proposed the Web services search engine Woogle that is capable of providing Web services similarity search.…”

Section: Related Workmentioning

confidence: 99%

“…However, their engine does not adequately consider data types, which usually reveal important information about the functionalities of Web services [18]. Liu and Wong [19] apply text mining techniques to extract features such as service content, context, host name, and service name, from Web service description files in order to cluster Web services. They proposed an integrated feature mining and clustering approach for Web services as a predecessor to discovery, hoping to help in building a search engine to crawl and cluster non-semantic Web services.…”

Section: Related Workmentioning

confidence: 99%

Multilabel Learning for Automatic Web Services Tagging

Aznag¹,

Quafafou²,

Jarir³

2014

ijacsa

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Abstract-Recently, some web services portals and search engines as Biocatalogue and Seekda!, have allowed users to manually annotate Web services using tags. User Tags provide meaningful descriptions of services and allow users to index and organize their contents. Tagging technique is widely used to annotate objects in Web 2.0 applications. In this paper we propose a novel probabilistic topic model (which extends the CorrLDA model -Correspondence Latent Dirichlet Allocation-) to automatically tag web services according to existing manual tags. Our probabilistic topic model is a latent variable model that exploits local correlation labels. Indeed, exploiting label correlations is a challenging and crucial problem especially in multi-label learning context. Moreover, several existing systems can recommend tags for web services based on existing manual tags. In most cases, the manual tags have better quality. We also develop three strategies to automatically recommend the best tags for web services. We also propose, in this paper, WS-Portal; An Enriched Web Services Search Engine which contains 7063 providers, 115 sub-classes of category and 22236 web services crawled from the Internet. In WS-Portal, severals technologies are employed to improve the effectiveness of web service discovery (i.e. web services clustering, tags recommendation, services rating and monitoring). Our experiments are performed out based on real-world web services. The comparisons of Precision@n, Normalised Discounted Cumulative Gain (NDCGn) values for our approach indicate that the method presented in this paper outperforms the method based on the CorrLDA in terms of ranking and quality of generated tags.

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“…A lot of research efforts have been devoted in utilizing WSDL documents [9], [3], [14], [15], [8], [16], [20]. Dong et al [9] proposed the Web services search engine Woogle that is capable of providing Web services similarity search.…”

Section: Related Workmentioning

confidence: 99%

Correlated Topic Model for Web Services Ranking

Aznag¹,

Quafafou²,

Jarir³

2013

IJACSA

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Abstract-With the increasing number of published Web services providing similar functionalities, it's very tedious for a service consumer to make decision to select the appropriate one according to her/his needs. In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) to extract latent factors from web service descriptions. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. To address the limitation of keywords-based queries, we represent web service description as a vector space and we introduce a new approach for discovering and ranking web services using latent factors. In our experiment, we evaluated our Service Discovery and Ranking approach by calculating the precision (P@n) and normalized discounted cumulative gain (NDCGn).

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Web service clustering using text mining techniques

Cited by 112 publications

References 18 publications

Improving Web Service Clustering Through Post Filtering to Bootstrap the Service Discovery

Improving Web Service Clustering Through Post Filtering to Bootstrap the Service Discovery

Multilabel Learning for Automatic Web Services Tagging

Correlated Topic Model for Web Services Ranking

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