Correlated Topic Model for Web Services Ranking

Aznag, Mustapha; Quafafou, Mohamed; Jarir, Zahi

doi:10.14569/ijacsa.2013.040637

Cited by 13 publications

(17 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Topic models are successfully used for a wide variety of applications including documents clustering and information retrieval [26], collaborative filtering [15], and visualization [16] as well as for modeling annotated data [8]. In our previous work [3], [4], we investigated the use of three probabilistic topic models PLSA, LDA and CTM to extract topics from semantically enriched service descriptions. These topics provide a model which represents any web service's description by a vector of terms.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 1 presents an overview of our proposed Web Service Tag Extraction mechanism. For each Web Service, we generate top-K tags using our previous approach based on Correlated Topic Model (CTM) described in [3]. We utilized CTM to extract latent factors z f ∈ Z = {z 1 , z 2 , ..., z k } from web service descriptions (i.e., STM).…”

Section: A Web Services Representation and Tags Extractionmentioning

confidence: 99%

“…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%

See 3 more Smart Citations

Multilabel Learning for Automatic Web Services Tagging

Aznag¹,

Quafafou²,

Jarir³

2014

ijacsa

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: A Web Services Representation and Tags Extractionmentioning

confidence: 99%

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

confidence: 99%

Section: B Service Discoverymentioning

confidence: 99%

See 2 more Smart Citations

Multilabel Learning for Automatic Web Services Tagging

Aznag¹,

Quafafou²,

Jarir³

2014

ijacsa

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our proposed approach is built upon our previous work on representing service descriptions in terms of topics [4], [5]. Topics or latent factors are a concept introduced by Probabilistic Topic Models [16].…”

Section: Introductionmentioning

confidence: 99%

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

Aznag

Quafafou

Jarir

2014

2014 IEEE International Conference on Web Services

Self Cite

View full text Add to dashboard Cite

With a growing number of web services, discovering services that can match with a user's query becomes a challenging task. It's very tedious for a service consumer to select the appropriate one according to her/his needs. In this paper, we propose a non-logic-based matchmaking approach that uses the Correlated Topic Model (CTM) to extract topic from semantic service descriptions and model the correlation between the extracted topics. Based on the topic correlation, service descriptions can be grouped into hierarchical clusters. In our approach, we use the Formal Concept Analysis (FCA) formalism to organize the constructed hierarchical clusters into concept lattices according to their topics. Thus, service discovery may be achieved more easily using the concept lattice. 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. In our experiment, we compared the accuracy of the our hierarchical clustering algorithm with that of a classical hierarchical agglomerative clustering. The comparisons of Precision@n and Normalised Discounted Cumulative Gain (NDCGn) values for our approach, Apache lucene and SAWSDL-MX2 Matchmaker indicate that the method based on CTM presented in this paper outperform all the others matchmakers in terms of ranking of the most relevant services.

show abstract

Semantic Pattern Mining Based Web Service Recommendation

Naim

Aznag

Durand

et al. 2016

Service-Oriented Computing

Self Cite

View full text Add to dashboard Cite

In semantic web-based framework, the build of philosophy is utilized to look comes about by relevant that implies of input inquiry or maybe than catchphrase coordinating. From the examination writing, there shows up to be a need for a apparatus which may provide a basic interface for complicate in quiries in dialect that may recover the domain-specific information from the transcendentalism. This examination paper proposes an IRSCSD framework (Data recovery framework for designing science space) as an reply. This method offers progressed questioning and browsing of organized data with look comes about mechanically total and rendered specifically in an exceedingly steady client-interface, in this manner diminishing the manual exertion of clients. So, the most objective of this examination is fashion and advancement of phonetics web-based framework for bunch activity transcendentalism towards domain-specific recovery back. Strategy taken after could be a piecemeal investigation that includes the ensuing stages. First Arrange includes the arranging of system for phonetics web-based framework. Moment organize builds the encapsulation for the system victimization Protégé instrument. Third Arrange bargains with the dialect address transformation into SPARQL look dialect victimization Python-fundamentally based address system.

show abstract

Correlated Topic Model for Web Services Ranking

Cited by 13 publications

References 20 publications

Multilabel Learning for Automatic Web Services Tagging

Multilabel Learning for Automatic Web Services Tagging

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

Semantic Pattern Mining Based Web Service Recommendation

Contact Info

Product

Resources

About