Shahab Mokarizadeh

As the utilization and development of Web services grows rapidly, the problem of analysing of existent Web services naturally arises. The analysis provided by the recent research work mostly extends to the statistical and explanatory examination of collections of Web services while deeper analysis of semantically annotated versions of services and the practical usage of the results of analysis are yet to receive substantial attention. The vast major- ity of existing web services lack any kind of formally expressed semantics, making semantic annotation a crucial preliminary step for analysis. In the absence of appropriate reference domain ontologies, annotation of existing web services is dependent on ontology development and ontology learning techniques. There is a great need for the development of automated ontology learning systems that allow semantic annotation of large collections of web services lacking any auxiliary textual materials. There is also a lack of effec- tive evaluation frameworks for assessing the quality of the provided semantic annotation on a large scale. The availability of a large quantity of semanti- cally annotated web services should allow the development of service selection and composition methods, taking advantage of domain diversity and the real- world characteristics of Web services. Moreover, user-generated contents such as tags and ratings in social networks are a rich source of information that can be exercised to perform more efficient service selection. Similarly, any method that targets the employment of user profiles in a social network needs to address a plausible solution for privacy issues. The main contribution of this dissertation is the development of techniques and frameworks supporting the construction, exploitation, and analysis of semantic web services. Specifically, we developed a semi-supervised method for ontology learning from Web service interface descriptions (WSDLs). The generated ontology is later used for semantic annotation of the examined web services. We also introduced a problem ontology as a specific case of task ontology, decomposing the given user query into a set of web services satisfying user requirements. The feasibility of problem ontology is evaluated as an action-planning component for a multi-robot system. Further, we developed an evaluation approach, suitable for the effective evaluation of large-scale, heterogeneous, real-world web service annotations. The approach consists of a set of procedures and metrics from network theory applied to the network structure of web services. The network is constructed by linking web services via matching input and output parameters of their operations. These web service networks are used to discover information exchange patterns among communities of services. The determined patterns can be employed for more effective service selection and composition strategies. Finally, in the context of a social network, we introduce a framework for privacy trust-aware user profile utilization. The efficiency of this framework is evaluated in the context of a user item-based recommendation system

ABSTRACT Within the course of this manuscript we present a privacy-preserving collaborative filtering recommender system which aims at alleviating the concern with privacy of user profiles within the context of sparse social trust data. While problem of sparsity in social trust is often addressed by taking similarity driven trust measures through a probabilistic matrix factorization technique, we address the issue of privacy by proposing a dynamic privacy inference model. The privacy inference model exploits the underlying inter-entity trust information in order to build a personalized privacy perspective for each individual within the social network. This is followed by our evaluation of the proposed solution by adopting an off-the-shelf collaborative filtering recommender library, in order to generate predictions using this personalized view.

ABSTRACT In the past years various methods have been developed which require semantic annotations of Web services as an input. Such methods typically leverage discovery, match-making, composition and execution of Web services in dynamic settings. At the same time a number of automated Web service annotation approaches have been proposed for enabling application of these methods in settings where it is not feasible to provide the annotations manually. However, lack of effective automated evaluation frameworks has seriously limited proper assessment of the constructed annotations in settings where the overall annotation quality of large quantities of Web services needs to be evaluated. This paper describes an evaluation framework for measuring the quality of semantic annotations for a large number of real-world Web services from heterogeneous application domains. The evaluation framework is generally based on analyzing properties of Web service networks constructed from semantic annotations of the Web services. More specifically, we measure scale-free, small-world and correlation degree properties of the networks to evaluate the overall quality of annotations. The evaluation is demonstrated using annotations constructed semi-automatically for a set of publicly available WSDL documents containing descriptions of about 200 000 Web service operations.

ABSTRACT The lack of formally expressed semantics in web services complemented with the increasing number of available web services is the main obstacle in analyzing and using the existing web services exposed in the Web. In the absence of appropriate reference domain ontology, annotation of existing web services is dependent on ontology development and ontology learning techniques. In this paper we present an unsupervised ontology learning approach tailored to learning from WSDL documents. The most specific feature of the suggested approach is that it constructs (semi-) automatically ontology fragments from a collection of WSDL documents, that lack any extra textual documentation, by just exploiting element names in the WSDL document. The suggested approach combines both linguistic and statistic analysis techniques such as lexico-syntactic patterns and term co-occurrence analysis. The preliminary results show that the generated ontology captures correctly more than half of the semantic classes and instances as well as taxonomic and non-taxonomic relations, hence, providing a reasonable basis for automatic web services annotation.

ABSTRACT The increasing presence and adoption of Web ser-vices on the Web has promoted the significance of management of new service development for service developing sectors. The major challenge is that how to find missing but potentially valuable Web services to be developed. This problem can be divided into two sub-problems: finding missing Web services and measuring the added-value of the introduced services. This paper addresses a plausible solution to the first sub problem. Given a collection of Web services, we propose a framework for suggesting a set of candidate Web services that can be introduced to the collection. These suggested services are novel and do not present in the given collection. Our solution relies on the network structure of Web services for finding and recommending new Web services and utilizes the already observed properties of Web services networks for collective evaluation of the suggested services. The proposed solution is evaluated using 753 semantically annotated Web services. The experimental results shows that the proposed framework provides web service community with new network driven methods for finding and evaluation of novel Web services.

Abstract Collaborative filtering (CF) recommender systems are among the most popular approaches to solving the information overload problem in social networks by generating accurate predictions based on the ratings of similar users. Traditional CF recommenders suffer from lack of scalability while decentralized CF recommenders (DHT-based, Gossip-based etc.) have promised to alleviate this problem. Thus, in this paper we propose a decentralized approach to CF recommender systems that uses the T-Man algorithm to ...

ABSTRACT Within the course of this manuscript we present a privacy-preserving collaborative filtering recommender system which aims at alleviating the concern with privacy of user profiles within the context of sparse social trust data. While problem of sparsity in social trust is often addressed by taking similarity driven trust measures through a probabilistic matrix factorization technique, we address the issue of privacy by proposing a dynamic privacy inference model. The privacy inference model exploits the underlying inter-entity trust information in order to build a personalized privacy perspective for each individual within the social network. This is followed by our evaluation of the proposed solution by adopting an off-the-shelf collaborative filtering recommender library, in order to generate predictions using this personalized view.

Abstract In this paper we introduce a privacy-aware collaborative filtering recommender framework which aims to address the privacy concern of profile owners in the context of social trust sparsity. While sparsity in social trust is mitigated by similarity driven trust using a probabilistic matrix factorization technique, the privacy issue is addressed by employing a dynamic privacy inference model.

Abstract Information diffusion has been studied between and within biosphere, microblogs, social networks, citation networks and other domains where the network structure is present. These studies have been useful for acquiring intrinsic knowledge for strategic decision making in related areas, such as planning online campaigns in case of microblogs and blogosphere. However, despite of the advances in services science, no study has been published on analyzing information diffusion in Web services networks.

As the utilization and development of Web services grows rapidly, the problem of analysing of existent Web services naturally arises. The analysis provided by the recent research work mostly extends to the statistical and explanatory examination of collections of Web services while deeper analysis of semantically annotated versions of services and the practical usage of the results of analysis are yet to receive substantial attention. The vast major- ity of existing web services lack any kind of formally expressed semantics, making semantic annotation a crucial preliminary step for analysis. In the absence of appropriate reference domain ontologies, annotation of existing web services is dependent on ontology development and ontology learning techniques. There is a great need for the development of automated ontology learning systems that allow semantic annotation of large collections of web services lacking any auxiliary textual materials. There is also a lack of effec- tive evaluation frameworks for assessing the quality of the provided semantic annotation on a large scale. The availability of a large quantity of semanti- cally annotated web services should allow the development of service selection and composition methods, taking advantage of domain diversity and the real- world characteristics of Web services. Moreover, user-generated contents such as tags and ratings in social networks are a rich source of information that can be exercised to perform more efficient service selection. Similarly, any method that targets the employment of user profiles in a social network needs to address a plausible solution for privacy issues.
The main contribution of this dissertation is the development of techniques and frameworks supporting the construction, exploitation, and analysis of semantic web services. Specifically, we developed a semi-supervised method for ontology learning from Web service interface descriptions (WSDLs). The generated ontology is later used for semantic annotation of the examined web services. We also introduced a problem ontology as a specific case of task ontology, decomposing the given user query into a set of web services satisfying user requirements. The feasibility of problem ontology is evaluated as an action-planning component for a multi-robot system. Further, we developed an evaluation approach, suitable for the effective evaluation of large-scale, heterogeneous, real-world web service annotations. The approach consists of a set of procedures and metrics from network theory applied to the network structure of web services. The network is constructed by linking web services via matching input and output parameters of their operations. These web service networks are used to discover information exchange patterns among communities of services. The determined patterns can be employed for more effective service selection and composition strategies. Finally, in the context of a social network, we introduce a framework for privacy trust-aware user profile utilization. The efficiency of this framework is evaluated in the context of a user item-based recommendation system