Leveraging Formal Concept Analysis with Topic Correlation for Service Clustering and Discovery

Aznag, Mustapha; Quafafou, Mohamed; Jarir, Zahi

doi:10.1109/icws.2014.33

Cited by 32 publications

(20 citation statements)

References 15 publications

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“…Our WS-Portal to improve the effectiveness of web services discovery. Specifically, probabilistic topics models are utilized for clustering, services/topics and tags recommendation [3], [4], [5]. We use probabilistic topic models to extract topic from semantic service descriptions and search for services in a topics space where heterogeneous service descriptions are all represented as a probability distribution over topics.…”

Section: Ws-portal; An Enriched Web Services Search Enginementioning

confidence: 99%

“…A user query represented by a set of words is represented as a distribution over topics [3], [4], [5]. The service discovery is based on computing the similarity between retrieved topic's services and a user's query.…”

Section: B Service Discoverymentioning

confidence: 99%

“…In fact, it converts service description to vector form in order to facilitate the computational analysis of data. In information retrieval, VSM 5 http://nlp.stanford.edu/software/tagger.shtml is identified as the most widely used representation for documents and is a very useful method for analyzing service descriptions. The TF-IDF algorithm [25] is used to represent a dataset of WSDL documents and convert it to VSM form.…”

Section: A Web Services Representation and Tags Extractionmentioning

confidence: 99%

“…Indeed, we use the extracted topics from services descriptions to calculate the similarity between the selected service and others web services in our repository. For this, we compute the similarity score, using some probability metrics such as Cosine Similarity and Symmetric KL Divergence [5], between the vectors containing the service's distribution over topics. Finally, similar services are ranked in order of their similarity score to the selected service.…”

Section: G User Interfacementioning

confidence: 99%

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Multilabel Learning for Automatic Web Services Tagging

Aznag¹,

Quafafou²,

Jarir³

2014

ijacsa

Self Cite

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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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Section: Ws-portal; An Enriched Web Services Search Enginementioning

confidence: 99%

Section: B Service Discoverymentioning

confidence: 99%

Section: A Web Services Representation and Tags Extractionmentioning

confidence: 99%

Section: G User Interfacementioning

confidence: 99%

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Multilabel Learning for Automatic Web Services Tagging

Aznag¹,

Quafafou²,

Jarir³

2014

ijacsa

Self Cite

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“…II. RELATED WORK Web service discovery and management has been a heated topic in recent years [2]- [7]. Skoutas et al [8] proposed a methodology for ranking and clustering the relevant web services based on the notion of dominance, which apply multiple matching criteria without aggregating the match scores of individual service parameters.…”

mentioning

confidence: 99%

Clustering Service Networks with Entity, Attribute, and Link Heterogeneity

Zhou

Liu

et al. 2015

2015 IEEE International Conference on Web Services

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Abstract-Many popular web service networks are contentrich in terms of heterogeneous types of entities and links, associated with incomplete attributes. Clustering such heterogeneous service networks demands new clustering techniques that can handle two heterogeneity challenges: (1) multiple types of entities co-exist in the same service network with multiple attributes, and (2) links between entities have diverse types and carry different semantics. Existing heterogeneous graph clustering techniques tend to pick initial centroids uniformly at random, specify the number k of clusters in advance, and fix k during the clustering process. In this paper, we propose SERVICECLUSTER, a novel heterogeneous SERVICE network CLUSTERing algorithm with four unique features. First, we incorporate various types of entity, attribute and link information into a unified distance measure. Second, we design a Discrete Steepest Descent method to naturally produce initial k and initial centroids simultaneously. Third, we propose a dynamic learning method to automatically adjust the link weights towards clustering convergence. Fourth, we develop an effective optimization strategy to identify new suitable k and k well-chosen centroids at each clustering iteration.

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Tagging augmented neural topic model for semantic sparse Web service discovery

Tian

Liu

Peng

et al. 2018

Concurrency and Computation

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Search engine based Web service discovery model suffers from the semantic sparsity problem due to the fact that Web services are described in short texts, which in turn leads to poor recall. To address this issue, external information that enriches the semantics of the Web service and improves discovery performance has been highly concerned. In light of this, we propose a novel Web service discovery approach that uses the neural topic model, which seamlessly integrates tagging information and word embedding for semantic sparsity problem. More specifically, instead of clustering Web services as done in most existing service discovery approaches, we use word embedding to map the words as continuous embeddings to embody external semantics of the service description. We also leverage the neural topic model in service discovery, which takes continuous word distribution as the input and interprets the Web service description as a hierarchical model. Based on the neural topic model and word embedding, we propose an efficient Web service query and ranking approach. Experiments conducted on a real-world Web service dataset demonstrate the effectiveness of the proposed approach. KEYWORDStagging augmented neural topic model, Web service discovery, word embeddings

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Leveraging Formal Concept Analysis with Topic Correlation for Service Clustering and Discovery

Cited by 32 publications

References 15 publications

Multilabel Learning for Automatic Web Services Tagging

Multilabel Learning for Automatic Web Services Tagging

Clustering Service Networks with Entity, Attribute, and Link Heterogeneity

Tagging augmented neural topic model for semantic sparse Web service discovery

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