Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors

In our thesis we explore the Automatic Question/Answer Generation (AQAG) and the application of Machine Learning (ML) in natural language queries. Initially we create a collection of question/answer tuples conceptually based on processing received data from (virtual) sensors placed in a smart city. Subsequently we train a Gated Recurrent Unit(GRU) model on the generated dataset and evaluate the accuracy we can achieve in answering those questions. This will help in turn to address the problem of automatic sensor composition based on natural language queries. To this end, the contribution of this thesis is two-fold: on one hand we are providing anautomatic procedure for dataset construction, based on natural language question templates, and on the other hand we apply a ML approach that establishes the correlation between the natural language queries and their virtual sensor representation, via their functional representation. We consider virtual sensors to be entities as described by Mihailescu et al, where they provide an interface constructed with certain properties in mind. We use those sensors for our application domain of a smart city environment, thus constructing our dataset around questions relevant to it

Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors

In this paper we address the problem of automatic sensor composition for servicing human-interpretable high-level tasks. To this end, we introduce multi-level distributed intelligent virtual sensors (multi-level DIVS) as an overlay framework for a given mesh of physical and/or virtual sensors already deployed in the environment. The goal for multi-level DIVS is two-fold: (i) to provide a convenient way for the user to specify high-level sensing tasks; (ii) to construct the computational graph that provides the correct output given a specific sensing task. For (i) we resort to a conversational user interface, which is an intuitive and user-friendly manner in which the user can express the sensing problem, i.e., natural language queries, while for (ii) we propose a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation. Finally, we evaluate and demonstrate the feasibility of our approach in the context of a smart city setup

Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors

In this paper we address the problem of automatic sensor composition for servicing human-interpretable high-level tasks. To this end, we introduce multi-level distributed intelligent virtual sensors (multi-level DIVS) as an overlay framework for a given mesh of physical and/or virtual sensors already deployed in the environment. The goal for multi-level DIVS is two-fold: (i) to provide a convenient way for the user to specify high-level sensing tasks; (ii) to construct the computational graph that provides the correct output given a specific sensing task. For (i) we resort to a conversational user interface, which is an intuitive and user-friendly manner in which the user can express the sensing problem, i.e., natural language queries, while for (ii) we propose a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation. Finally, we evaluate and demonstrate the feasibility of our approach in the context of a smart city setup