Person: ŞENSOY, Murat
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Murat
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ŞENSOY
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ArticlePublication Metadata only Agent-based semantic collaborative search(Executive Committee, Taiwan Academic Network, Ministry of Education, 2013) Şensoy, Murat; Computer Science; ŞENSOY, MuratNext generation of the Web builds upon technologies such as Semantic Web and Intelligent Software Agents. These technologies aim at knowledge representation that allows both humans and software agents to understand and reason about the content on the Web. In this paper, we propose an agent-based approach for collaborative distributed semantic search of the Web resources. Our approach enables a human user to semantically describe his search interest to an agent. Depending on the interests of their users, the agents evolve their ontologies and create search concepts. Based on these search concepts, the agents coordinate and compose virtual communities. Within these communities, agents with similar interests interact to locate and share URLs relevant to search interests of their users. Through these interactions, shared vocabularies are cooperatively emerged by agents to communicate properly within the communities. Our empirical evaluations and analysis of the proposed approach show that our approach combines Semantic Web technologies and multi-agent systems in a novel way to enable users to find and share the URLs relevant to their search interests.ArticlePublication Metadata only A generalized stereotype learning approach and its instantiation in trust modeling(Elsevier, 2018-08) Fang, H.; Zhang, J.; Şensoy, Murat; Computer Science; ŞENSOY, MuratOwing to the lack of historical data regarding an entity in online communities, a user may rely on stereotyping to estimate its behavior based on historical data about others. However, these stereotypes cannot accurately reflect the user's evaluation if they are based on limited historical data about other entities. In view of this issue, we propose a novel generalized stereotype learning approach: the fuzzy semantic framework. Specifically, we propose a fuzzy semantic process, incorporated with traditional machine-learning techniques to construct stereotypes. It consists of two sub-processes: a fuzzy process that generalizes over non-nominal attributes (e.g., price) by splitting their values in a fuzzy manner, and a semantic process that generalizes over nominal attributes (e.g., location) by replacing their specific values with more general terms according to a predefined ontology. We also implement the proposed framework on the traditional decision tree method to learn users' stereotypes and validate the effectiveness of our framework for computing trust in e-marketplaces. Experiments on real data confirm that our proposed model can accurately measure the trustworthiness of sellers with which buyers have limited experience.ArticlePublication Metadata only Handling epistemic and aleatory uncertainties in probabilistic circuits(Springer, 2022-04) Cerutti, F.; Kaplan, L. M.; Kimmig, A.; Şensoy, Murat; Computer Science; ŞENSOY, MuratWhen collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian approaches for probabilistic reasoning in order to determine the confidence (or epistemic uncertainty) in the probabilities in light of the training data. We propose an approach to Bayesian inference of posterior distributions that overcomes the independence assumption behind most of the approaches dealing with a large class of probabilistic reasoning that includes Bayesian networks as well as several instances of probabilistic logic. We provide an algorithm for Bayesian inference of posterior distributions from sparse, albeit complete, observations, and for deriving inferences and their confidences keeping track of the dependencies between variables when they are manipulated within the unifying computational formalism provided by probabilistic circuits. Each leaf of such circuits is labelled with a beta-distributed random variable that provides us with an elegant framework for representing uncertain probabilities. We achieve better estimation of epistemic uncertainty than state-of-the-art approaches, including highly engineered ones, while being able to handle general circuits and with just a modest increase in the computational effort compared to using point probabilities.ArticlePublication Metadata only Interest-based negotiation for policy-regulated asset sharing(Springer Nature, 2016) Parizas, C.; de Mel, G.; Preece, A. D.; Şensoy, Murat; Calo, S. B.; Pham, T.; Computer Science; ŞENSOY, MuratResources sharing is an important but complex problem to be solved. The problem is exacerbated in a coalition context due to policy constraints, that reflect concerns regarding security, privacy and performance to name a few, placed on the resources. Thus, to effectively share resources, members of a coalition need to negotiate on policies and at times refine them to meet the needs of the operating environment. Towards achieving this goal, in this work we propose and evaluate a novel policy negotiation mechanism based on the interest-based negotiation paradigm. Interest-based negotiation, promotes collaboration when compared with the traditional, position-based negotiation approaches.ArticlePublication Metadata only A hybrid reasoning mechanism for effective sensor selection for tasks(Elsevier, 2013-02) de Mel, G.; Şensoy, Murat; Vasconcelos, W.; Norman, T. J.; Computer Science; ŞENSOY, MuratIn this paper, we present Ontological Logic Programming (OLP), a novel approach that combines logic programming with ontological reasoning. OLP enables the use of ontological terms (i.e., individuals, classes and properties) directly within logic programmes. The interpretation of these terms is delegated to an ontology reasoner during the interpretation of the programme. Unlike similar approaches, OLP makes use of the full capacity of both ontological reasoning and logic programming. We evaluate the computational properties of OLP in different settings and show that its performance can be significantly improved using caching mechanisms. We then introduce a comprehensive sensor-task selection solution based on OLP and discuss the benefits one can obtain by using OLP. The solution is based on a set of interlinking ontologies that capture the crucial domain knowledge of sensor networks. We then make use of OLP to create and manage complex concepts in the domain as well as to implement effective resource-task assignment algorithms, which compute appropriate resources for tasks such that they sufficiently cover the tasks needs. We compare the advantages of OLP with a knowledge-based set-covering mechanism for resource-task selection.ArticlePublication Metadata only Location attestation and access control for mobile devices using GeoXACML(Elsevier, 2017-02) Arunkumar, S.; Soyluoglu, Berker; Şensoy, Murat; Srivatsa, M.; Rajarajan, M.; Computer Science; ŞENSOY, Murat; Soyluoglu, BerkerAccess control has been applied in various scenarios in the past for negotiating the best policy. Solutions with XACML for access control has been very well explored by research and have resulted in significant contributions to various sectors including healthcare. In controlling access to the sensitive data such as medical records, it is important to guarantee that the data is accessed by the right person for the right reason. Location of access requestor can be a good indication for his/her eligibility and reasons for accessing the data. To reason with geospatial information for access control, Geospatial XACML (eXtensible Access Control Markup Language) is proposed as a standard. However, there is no available implementation and architecture for reasoning with Geospatial XACML policies. This paper proposes to extend XACML with geohashing to implement geospatial policies. It also proposes an architecture for checking reliability of the geospatial information provided by clients. With a case study, we demonstrate how our framework can be used to control the privacy and data access of health service data in handheld devices.ArticlePublication Metadata only A 2020 perspective on “A generalized stereotype learning approach and its instantiation in trust modeling”(Elsevier, 2020-03) Fang, H.; Zhang, J.; Şensoy, Murat; Computer Science; ŞENSOY, MuratOwing to the rapid increase of user data and development of machine learning techniques, user modeling has been explored in depth and exploited by both academia and industry. It has prominent impacts in e-commercerelated applications by facilitating users' experience in online platforms and supporting business organizations' decision-making. Among all the techniques and applications, user profiling and recommender systems are two representative and effective ones, which have also obtained growing attention. In view of its wide applications, researchers and practitioners should improve user modeling from two perspectives: (1) more effort should be devoted to obtain more user data via techniques like sensing devices and develop more effective ways to manage complex data; and (2) improving the ability of learning from a limited number of data samples (e.g., few-shot learning) has become an increasingly hot topic for researchers.ArticlePublication Metadata only Agilely assigning sensing assets to mission tasks in a coalition context(IEEE, 2013) Preece, A.; Norman, T.; de Mel, G.; Pizzocaro, D.; Şensoy, Murat; Pham, T.; Computer Science; ŞENSOY, MuratWhen managing intelligence, surveillance, and reconnaissance (ISR) operations in a coalition context, assigning available sensing assets to mission tasks can be challenging. The authors' approach to ISR asset assignment uses ontologies, allocation algorithms, and a service-oriented architecture.Conference ObjectPublication Open Access Probabilistic logic programming with beta-distributed random variables(Association for the Advancement of Artificial Intelligence, 2019-07-17) Cerutti, F.; Kaplan, L.; Kimmig, A.; Şensoy, Murat; Computer Science; ŞENSOY, MuratWe enable aProbLog-a probabilistic logical programming approach-to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms for highly specified and engineered domains, while simultaneously we maintain the flexibility offered by aProbLog in handling complex relational domains. Our motivation is that faithfully capturing the distribution of probabilities is necessary to compute an expected utility for effective decision making under uncertainty: unfortunately, these probability distributions can be highly uncertain due to sparse data. To understand and accurately manipulate such probability distributions we need a well-defined theoretical framework that is provided by the Beta distribution, which specifies a distribution of probabilities representing all the possible values of a probability when the exact value is unknown.Conference ObjectPublication Metadata only Goal directed policy conflict detection and prioritisation: an empirical evaluation(Springer Science+Business Media, 2014) Aphale, M. S.; Norman, T. J.; Şensoy, Murat; Computer Science; ŞENSOY, MuratWe address the problem of developing effective automated reasoning support for the detection and resolution of conflicts between plans and policies (or norms). How automated reasoning mechanisms can effectively support human decision makers in this process is little understood. In this research, we have conducted experiments with human subjects to assess how effective these reasoning mechanisms are. We found that providing guidance to users regarding what problems to prioritise and highlighting related conflicts led to higher quality outcomes, and problems were successfully solved more rapidly.