Semantic Interpretation of User Queries for Question Answering on Interlinked Data

The Web of Data contains a wealth of knowledge belonging to a large number of domains. Retrieving data from such precious interlinked knowledge bases is an issue. By taking the structure of data into account, it is expected that upcoming generation of search engines is approaching to question answering systems, which directly answer user questions. But developing a question answering over these interlinked data sources is still challenging because of two inherent characteristics: First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between these datasets on both the schema and instance levels. In this respect, several challenges such as resource disambiguation, vocabulary mismatch, inference, link traversal are raised. In this dissertation, we address these challenges in order to build a question answering system for Linked Data. We present our question answering system Sina, which transforms user-supplied queries (i.e. either natural language queries or keyword queries) into conjunctive SPARQL queries over a set of interlinked data sources. The contributions of this work are as follows: 1. A novel approach for determining the most suitable resources for a user-supplied query from different datasets (disambiguation approach). We employed a Hidden Markov Model, whose parameters were bootstrapped with different distribution functions. 2. A novel method for constructing federated formal queries using the disambiguated resources and leveraging the linking structure of the underlying datasets. This approach essentially relies on a combination of domain and range inference as well as a link traversal method for constructing a connected graph, which ultimately renders a corresponding SPARQL query. 3. Regarding the problem of vocabulary mismatch, our contribution is divided into two parts, First, we introduce a number of new query expansion features based on semantic and linguistic inferencing over Linked Data. We evaluate the effectiveness of each feature individually as well as their combinations, employing Support Vector Machines and Decision Trees. Second, we propose a novel method for automatic query expansion, which employs a Hidden Markov Model to obtain the optimal tuples of derived words. 4. We provide two benchmarks for two different tasks to the community of question answering systems. The first one is used for the task of question answering on interlinked datasets (i.e. federated queries over Linked Data). The second one is used for the vocabulary mismatch task. We evaluate the accuracy of our approach using measures like mean reciprocal rank, precision, recall, and F-measure on three interlinked life-science datasets as well as DBpedia. The results of our accuracy evaluation demonstrate the effectiveness of our approach. Moreover, we study the runtime of our approach in its sequential as well as parallel implementations and draw conclusions on the scalability of our approach on Linked Data

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Automatic Extraction and Assessment of Entities from the Web

The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval

Trust Management for Context-Aware Composite Services

In the areas of cloud computing, big data and internet of things, composite services are designed to effectively address complex levels of user requirements. A major challenge for composite services management is the dynamic and continuously changing run-time environments that could raise several exceptional situations such as service execution time that may have greatly increased or a service that may become unavailable. Composite services in this environmental context have difficulty securing an acceptable quality of service (QoS). The need for dynamic adaptations to be triggered becomes then urgent for service-based systems. These systems also require trust management to ensure service level agreement (SLA) compliance. To face this dynamism and volatility, context-aware composite services (i.e., run-time self-adaptable services) are designed to continue offering their functionalities without compromising their operational efficiency to boost the added value of the composition. The literature on adaptation management for context-aware composite services mainly focuses on the closed world assumption that the boundary between the service and its run-time environment is known, which is impractical for dynamic services in the open world where environmental contexts are unexpected. Besides, the literature relies on centralized architectures that suffer from management overhead or distributed architectures that suffer from communication overhead to manage service adaptation. Moreover, the problem of encountering malicious constituent services at run-time still needs further investigation toward a more efficient solution. Such services take advantage of the environmental contexts for their benefit by providing unsatisfying QoS values or maliciously collaborate with other services. Furthermore, the literature overlooks the fact that composite services data is relational and relies on propositional data (i.e., flattened data containing the information without the structure). This contradicts with the fact that services are statistically dependent since QoS values of service are correlated with those of other services. This thesis aims to address these gaps by capitalizing on different methods from software engineering, computational intelligence and machine learning. To support context-aware composite services in the open world, dynamic adaptation mechanisms are carried out at design-time to guide the running services. To this end, this thesis proposes an adaptation solution based on a feature model that captures the variability of the composite service and deliberates the inter-dependency relations among QoS constraints. We apply the master-slaves adaptation pattern to enable coordination of the self-adaptation process based on the MAPE loop (Monitor-Analysis-Plan-Execute) at run time. We model the adaptation process as a multi-objective optimization problem and solve it using a meta-heuristic search technique constrained by SLA and feature model constraints. This enables the master to resolve conflicting QoS goals of the service adaptation. In the slave side, we propose an adaptation solution that immediately substitutes failed constituent services with no need for complex and costly global adaptation. To support the decision making at different levels of adaptation, we first propose an online SLA violation prediction model that requires small amounts of end-to-end QoS data. We then extend the model to comprehensively consider service dependency that exists in the real business world at run time by leveraging the relational dependency network, thus enhancing the prediction accuracy. In addition, we propose a trust management model for services based on the dependency network. Particularly, we predict the probability of delivering a satisfactory QoS under changing environmental contexts by leveraging the cyclic dependency relations among QoS metrics and environmental context variables. Moreover, we develop a service reputation evaluation technique based on the power of mass collaboration where we explicitly detect collusion attacks. As another contribution of this thesis, we introduce for the newcomer services a trust bootstrapping mechanism resilient to the white-washing attack using the concept of social adoption. The thesis reports simulation results using real datasets showing the efficiency of the proposed solutions

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice