A Framework for Semi-automated Web Service Composition in Semantic Web

Number of web services available on Internet and its usage are increasing very fast. In many cases, one service is not enough to complete the business requirement; composition of web services is carried out. Autonomous composition of web services to achieve new functionality is generating considerable attention in semantic web domain. Development time and effort for new applications can be reduced with service composition. Various approaches to carry out automated composition of web services are discussed in literature. Web service composition using ontologies is one of the effective approaches. In this paper we demonstrate how the ontology based composition can be made faster for each customer. We propose a framework to provide precomposed web services to fulfil user requirements. We detail how ontology merging can be used for composition which expedites the whole process. We discuss how framework provides customer specific ontology merging and repository. We also elaborate on how merging of ontologies is carried out.Comment: 6 pages, 9 figures; CUBE 2013 International Conferenc

LO-MATCH: A semantic platform for matching migrants' competences with labour market's needs

Citizens' mobility and employability are receiving ever more attention by the European legislation. Various instruments have been defined to overcome lexical and semantic differences in the descriptions of qualifications, résumés and job profiles. However, the above differences still represent a significant constraint when abilities of non-European people have to be validated either for education and training or occupation purposes. In this work, a web platform that exploits semantic technologies to address such heterogeneity issues is presented. The platform allows migrants to annotate their knowledge, skills and competences in a shared format based on the European tools. The resulting knowledge base is then used to enable the automatic matchmaking of job seekers' abilities with companies' needs. The platform can additionally be used to support students and workers in the identification of their competence gap with respect to a given education or occupation opportunity, so that to personalize their further trainin

Video Data Visualization System: Semantic Classification And Personalization

We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the edges are the relation between documents and the classes of documents. Finally, we construct the user's profile, based on the interaction with the system, to render the system more adequate to its references.Comment: graphic

Improving the quality of the personalized electronic program guide

As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems—PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system

Comparison of group recommendation algorithms

In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops. Although most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not intended for personal usage but rather for consumption in groups. This paper investigates how effective group recommendations for movies can be generated by combining the group members' preferences (as expressed by ratings) or by combining the group members' recommendations. These two grouping strategies, which convert traditional recommendation algorithms into group recommendation algorithms, are combined with five commonly used recommendation algorithms to calculate group recommendations for different group compositions. The group recommendations are not only assessed in terms of accuracy, but also in terms of other qualitative aspects that are important for users such as diversity, coverage, and serendipity. In addition, the paper discusses the influence of the size and composition of the group on the quality of the recommendations. The results show that the grouping strategy which produces the most accurate results depends on the algorithm that is used for generating individual recommendations. Therefore, the paper proposes a combination of grouping strategies which outperforms each individual strategy in terms of accuracy. Besides, the results show that the accuracy of the group recommendations increases as the similarity between members of the group increases. Also the diversity, coverage, and serendipity of the group recommendations are to a large extent dependent on the used grouping strategy and recommendation algorithm. Consequently for (commercial) group recommender systems, the grouping strategy and algorithm have to be chosen carefully in order to optimize the desired quality metrics of the group recommendations. The conclusions of this paper can be used as guidelines for this selection process

A social network for supporting end-users in the composition of services: definition and proof of concept

[EN] Nowadays, end users are surrounded by plenty of services that are somehow supporting their daily routines and activities. Involving end users into the process of service creation can allow end users to benefit from a cheaper, faster, and better service provisioning. Even though we can already find tools that face this challenge, they consider end users as isolate individuals. In this paper, we investigate how social networks can be used to improve the composition of services by end users. To do so, we propose a graph-based definition of a social structure, and analyse how social connections can be exploited to both facilitate end users to discover services through browsing these connections, and recommend services to end users during the composition activity. As proof of concept, we implement and evaluate the proposed social network in the context of EUCalipTool, a mobile end-user environment for composing services.This work has been developed with the financial support of the Spanish State Research Agency under the Project TIN2017-84094-R and co-financed with ERDF.Valderas, P.; Torres Bosch, MV.; Pelechano Ferragud, V. (2020). A social network for supporting end-users in the composition of services: definition and proof of concept. Computing. 102(8):1909-1940. https://doi.org/10.1007/s00607-020-00796-8S190919401028Yu J, Sheng QZ, Han J, Wu Y, Liu C (2012) A semantically enhanced service repository for user-centric service discovery and management. Data Knowl Eng 72:202–218Daniel F, Casati F, Benatallah B, Shan M-C (2009) Hosted universal composition: models, languages and infrastructure in mashart. In: International conference on conceptual modeling. 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Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach

Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy