Mohamad Mehdi

The sum of all human knowledge: A systematic review of scholarly research on the content of Wikipedia

Wikipedia may be the best‐developed attempt thus far to gather all human knowledge in one place. Its accomplishments in this regard have made it a point of inquiry for researchers from different fields of knowledge. A decade of research has thrown light on many aspects of the Wikipedia community, its processes, and its content. However, due to the variety of fields inquiring about Wikipedia and the limited synthesis of the extensive research, there is little consensus on many aspects of Wikipedias content as an encyclopedic collection of human knowledge. This study addresses the issue by systematically reviewing 110 peer‐reviewed publications on Wikipedia content, summarizing the current findings, and highlighting the major research trends. Two major streams of research are identified: the quality of Wikipedia content (including comprehensiveness, currency, readability, and reliability) and the size of Wikipedia. Moreover, we present the key research trends in terms of the domains of inquiry, research design, data source, and data gathering methods. This review synthesizes scholarly understanding of Wikipedia content and paves the way for future studies.

Trustworthy Web Service Selection Using Probabilistic Models

Software architectures of large-scale systems are perceptibly shifting towards employing open and distributed computing. Service Oriented Computing (SOC) is a typical example of such environment in which the quality of interactions amongst software agents is a critical concern. Agent-based web services in open and distributed architectures need to interact with each other to achieve their goals and fulfill complex user requests. Two common tasks are influenced by the quality of interactions among web services: the selection and composition. Thus, to ensure the maximum gain in both tasks, it is essential for each agent-based web service to maintain a model of its environment. This model then provides a means for a web service to predict the quality of future interactions with its peers. In this paper, we formulate this model as a machine learning problem which we analyze by modeling the trustworthiness of web services using probabilistic models. We propose two approaches for trust learning of single and composed services; Bayesian Networks and Mixture of Multinomial Dirichlet Distributions (MMDD). The effectiveness of our approaches is empirically assessed using a simulation study. Our results show that representing the quality of a web service by Multinomial Dirichlet Distribution (MDD) provides high flexibility and accuracy in modeling trust. They also show that using our approaches to estimate trust enhances web services selection and composition.

Wikipedia in the eyes of its beholders: A systematic review of scholarly research on Wikipedia readers and readership

Hundreds of scholarly studies have investigated various aspects of Wikipedia. Although a number of literature reviews have provided overviews of this vast body of research, none has specifically focused on the readers of Wikipedia and issues concerning its readership. In this systematic literature review, we review 99 studies to synthesize current knowledge regarding the readership of Wikipedia and provide an analysis of research methods employed. The scholarly research has found that Wikipedia is popular not only for lighter topics such as entertainment but also for more serious topics such as health and legal information. Scholars, librarians, and students are common users, and Wikipedia provides a unique opportunity for educating students in digital literacy. We conclude with a summary of key findings, implications for researchers, and implications for the Wikipedia community.

Probabilistic approach for QoS-aware recommender system for trustworthy web service selection

We present a QoS-aware recommender approach based on probabilistic models to assist the selection of web services in open, distributed, and service-oriented environments. This approach allows consumers to maintain a trust model for each service provider they interact with, leading to the prediction of the most trustworthy service a consumer can interact with among a plethora of similar services. In this paper, we associate the trust in a service to its performance denoted by QoS ratings instigated by the amalgamation of various QoS metrics. Since the quality of a service is contingent, which renders its trustworthiness uncertain, we adopt a probabilistic approach for the prediction of the quality of a service based on the evaluation of past experiences (ratings) of each of its consumers. We represent the QoS ratings of services using different statistical distributions, namely multinomial Dirichlet, multinomial generalized Dirichlet, and multinomial Beta-Liouville. We leverage various machine learning techniques to compute the probabilities of each web service to belong to different quality classes. For instance, we use the Bayesian inference method to estimate the parameters of the aforementioned distributions, which presents a multidimensional probabilistic embodiment of the quality of the corresponding web services. We also employ a Bayesian network classifier with a Beta-Liouville prior to enable the classification of the QoS of composite services given the QoS of its constituents. We extend our approach to function in an online setting using the Voting EM algorithm that enables the estimation of the probabilities of the QoS after each interaction with a web service. Our experimental results demonstrate the effectiveness of the proposed approaches in modeling, classifying and incrementally learning the QoS ratings.

Trust and Reputation of Web Services Through QoS Correlation Lens

In modern distributed systems, service consumers are faced with pools of service providers that offer similar functionalities. This reality renders the selection of web services a challenging task. One popular solution is to base the selection decisions on the web services’ non-functional requirements depicted by a variety of QoS metrics. In this paper, we present a new approach for solving the web service selection problem; a QoS-aware trust model that leverages the correlation information among various QoS metrics. This model, based on the probability theory, estimates the trustworthiness of web services by exploiting two statistical distributions, namely, Dirichlet and generalized Dirichlet. These distributions represent the outcomes of multiple correlated QoS metrics. The former distribution is employed when the QoS metrics are positively correlated while the latter handles negatively correlated metrics. We also propose an algorithm to aggregate reputation feedback that propagate among the interacting web services. This algorithm deals with malicious feedback and various strategic behavior commonly performed by web services. Experimental results endorse the advantageous capability of our trust model and reputation algorithm compared to the state-of-the-art.

A QoS-Based Trust Approach for Service Selection and Composition via Bayesian Networks

Service oriented computing is being increasingly exploited in the architecture of current web applications. The interactions among the deployed web services are becoming vital to accomplish heterogeneous and compound business goals. Service selection and composition are two common tasks which are highly influenced by the quality of these interactions. Thus, assigning web services QoS-based trust scores that are incrementally updated, provides a means to assist both tasks. Then, services with higher trust scores are more likely to be selected than those with smaller ones. They are, additionally, more prone to be incorporated as part of composite services. In this paper, we propose a probabilistic approach based on Bayesian networks (BN) to learn the composition structure of composite services and compute QoS-based trust scores in an online setting. The learning of the BN structure and parameters is based on modeling the QoS, which is represented by the BNs variables, using a multinomial generalized Dirichlet distribution (MGDD). The effectiveness of our approaches is empirically assessed using real and synthetic data. Our experimental results show that MGDD provides a flexible and accurate representation of the QoS. They also prove the capability of the BN approach to learn the composition structure, and further the responsibility of the constituent services in the quality of the composite service even when their QoS is partially observed.

Excavating the mother lode of human-generated text

Wikipedia provides rich, natural semi-structured texts for information retrieval.It provides semantic information for keyword extraction from varied texts.It facilitates clustering, text classification and semantic relatedness analyses.It supplies a semantically structured knowledge base for studying ontologies. Although primarily an encyclopedia, Wikipedias expansive content provides a knowledge base that has been continuously exploited by researchers in a wide variety of domains. This article systematically reviews the scholarly studies that have used Wikipedia as a data source, and investigates the means by which Wikipedia has been employed in three main computer science research areas: information retrieval, natural language processing, and ontology building. We report and discuss the research trends of the identified and examined studies. We further identify and classify a list of tools that can be used to extract data from Wikipedia, and compile a list of currently available data sets extracted from Wikipedia.

QoS-Based Reputation Feedback Fusion under Unknown Correlation

Due to the flood of web services that offer similar functionalities, service consumers are left with a challenging selection decision. A popular approach to assist them with the service selection task is based on the reputation of web services. However, the propagation of reputation feedback in an open and distributed system of web services yield correlated reputation estimates. The existing web service reputation literature still lacks a system that handles the aggregation of reputation feedback under unknown correlation. To fill this gap, we employ two data fusion algorithms, the covariance intersection and ellipsoidal intersection, to aggregate QoS-based reputation feedback. Our experimental results endorse the advantageous capability and scalability of the proposed methods in aggregating reputation estimates, and show an enhanced performance when compared with the Kalman filter method.

Reputation in Communities of Agent-Based Web Services Through Data Mining

We present in this paper a reputation model for agent-based web services grouped into communities by their equivalent functionalities. The reputation of each web service is based on the non-functional properties of its interactions with other web services from the same community. We exploit various clustering and anomaly detection techniques to analyze and identify the quality patterns provided by each service. This enables the master of each community to allocate the requests it receives to the web service that best fulfill the quality requirements of the service consumers. Our experiments present realistic scenarios based on synthetic data that characterizes the reputation feedback of the quality provided by a web service at different times. The results showcase the capability of our reputation model in portraying the quality of web services that reside in a community and characterizing their fair and unfair feedback reports.

Modeling and Forecasting Time Series of Compositional Data: A Generalized Dirichlet Power Steady Model

This paper presents GDPSM a power steady model (PSM) based on generalized Dirichlet observations for modeling and predicting compositional time series. The model’s unobserved states evolve according to the generalized Dirichlet conjugate prior distributions. The observations’ distribution is transformed into a set of Beta distributions each of which is re-parametrized as a unidimensional Dirichlet in its exponential form. We demonstrate that dividing the modeling problem into multiple smaller problems leads to more accurate predictions. We evaluate this model with the web service selection application. Specifically, we analyze the proportions of the quality classes that are assigned to the web services interactions. Our model is compared with another PSM that assumes Dirichlet observations. The experiments show promising results in terms of precision errors and standardized residuals.