http://bcpw.bg.pw.edu.pl/Content/1968/Rudiments.pdf

Rudiments of rough sets

Rough sets and Boolean reasoning

Lectures on Cauchy’s Problem in Linear Partial Differential Equations. By J. Hadamard. Pp. viii+316. 15s.net. 1923. (Per Oxford University Press.)

Generalized fuzzy rough sets

Microsoft has promoted ASP.NET’s new web services more than almost any other part of the.NET Framework. But despite their efforts, confusion is still widespread about what a web service is and, more importantly, what it’s meant to accomplish. This chapter introduces web services and explains their role in Microsoft’s vision of the programmable web. Along the way, you’ll learn about the open standards plumbing that allows web services to work and removes some of the confusion surrounding technical terms like WSDL (Web Service Description Language), SOAP, and UDDI (universal description, discovery, and integration).

Web Services Architecture

a-RST: a generalization of rough set theory

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 [email protected] 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.

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

In Information Retrieval the Probabilistic Topic Models were originally developed and utilized for topic extraction and document modeling. 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. These extracted latent factors are then used to group the services into clusters. 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 web services using latent factors. In our experiment, we compared the accuracy of the three probabilistic clustering algorithms (PLSA, LDA and CTM) with that of a classical clustering algorithm. We evaluated also our service discovery approach by calculating the precision (P@n) and normalized discounted cumulative gain (NDCGn). The results show that both approaches based on CTM and LDA perform better than other search methods.

Probabilistic Topic Models for Web Services Clustering and Discovery

Multi-data source fusion

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).

/pdf/correlated-topic-model-for-web-services-ranking-5bxhthtir0.pdf

Mohamed Quafafou

Papers

a-RST: a generalization of rough set theory

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

Probabilistic Topic Models for Web Services Clustering and Discovery

Multi-data source fusion

Correlated Topic Model for Web Services Ranking