An Ontology-Based Framework for a Telehealthcare System to Foster Healthy Nutrition and Active Lifestyle in Older Adults

In recent years, telehealthcare systems (TSs) have become more and more widespread, as they can contribute to promoting the continuity of care and managing chronic conditions efficiently. Most TSs and nutrition recommendation systems require much information to return appropriate suggestions. This work proposes an ontology-based TS, namely HeNuALs, aimed at fostering a healthy diet and an active lifestyle in older adults with chronic pathologies. The system is built on the formalization of users' health conditions, which can be obtained by leveraging existing standards. This allows for modeling different pathologies via reusable knowledge, thus limiting the amount of information needed to retrieve nutritional indications from the system. HeNuALs is composed of (1) an ontological layer that stores patients and their data, food and its characteristics, and physical activity-related data, enabling the inference a series of suggestions based on the effects of foods and exercises on specific health conditions; (2) two applications that allow both the patient and the clinicians to access the data (with different permissions) stored in the ontological layer; and (3) a series of wearable sensors that can be used to monitor physical exercise (provided by the patient application) and to ensure patients' safety. HeNuALs inferences have been validated considering two different use cases. The system revealed the ability to determine suggestions for healthy, adequate, or unhealthy dishes for a patient with respiratory disease and for a patient with diabetes mellitus. Future work foresees the extension of the HeNuALs knowledge base by exploiting automatic knowledge retrieval approaches and validation of the whole system with target users

Combining open and closed world reasoning for the semantic web

Dissertação para obtenção do Grau de Doutor em InformáticaOne important problem in the ongoing standardization of knowledge representation languages for the Semantic Web is combining open world ontology languages, such as the OWL-based ones, and closed world rule-based languages. The main difficulty of such a combination is that both formalisms are quite orthogonal w.r.t. expressiveness and how decidability is achieved. Combining non-monotonic rules and ontologies is thus a challenging task that requires careful balancing between expressiveness of the knowledge representation language and the computational complexity of reasoning. In this thesis, we will argue in favor of a combination of ontologies and nonmonotonic rules that tightly integrates the two formalisms involved, that has a computational complexity that is as low as possible, and that allows us to query for information instead of calculating the whole model. As our starting point we choose the mature approach of hybrid MKNF knowledge bases, which is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. We extend the two-valued framework of MKNF logics to a three-valued logics, and we propose a well-founded semantics for non-disjunctive hybrid MKNF knowledge bases. This new semantics promises to provide better efficiency of reasoning,and it is faithful w.r.t. the original two-valued MKNF semantics and compatible with both the OWL-based semantics and the traditional Well- Founded Semantics for logic programs. We provide an algorithm based on operators to compute the unique model, and we extend SLG resolution with tabling to a general framework that allows us to query a combination of non-monotonic rules and any given ontology language. Finally, we investigate concrete instances of that procedure w.r.t. three tractable ontology languages, namely the three description logics underlying the OWL 2 pro les.Fundação para a Ciência e Tecnologia - grant contract SFRH/BD/28745/200

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

Interest-based segmentation of online video platforms' viewers using semantic technologies

To better connect supply and demand for various products, marketers needed novel ways to segment and target their customers with relevant adverts. Over the last decade, companies that collected a large amount of psychographic and behavioural data about their customers emerged as the pioneers of hyper-targeting. For example, Google can infer people’s interests based on their search queries, Facebook based on their thoughts, and Amazon by analysing their shopping cart history. In this context, the traditional channel used for advertising – the media market – saw its revenues plummeting as it failed to infer viewers’ interests based on the programmes they are watching, and target them with bespoke adverts. In order to propose a methodology for inferring viewers’ interests, this study adopted an interdisciplinary approach by analysing the problem from the viewpoint of three disciplines: Customer Segmentation, Media Market, and Large Knowledge Bases. Critically assessing and integrating the disciplinary insights was required for a deep understanding of: the reasons for which psychographic variables like interests and values are a better predictor for consumer behaviour as opposed to demographic variables; the various types of data collection and analysis methods used in the media industry; as well as the state of the art in terms of detecting concepts from text and linking them to various ontologies for inferring interests. Building on these insights, a methodology was proposed that can fully automate the process of inferring viewers interests by semantically analysing the description of the programmes they watch, and correlating it with data about their viewing history. While the methodology was deemed valid from a theoretical point of view, an extensive empirical validation was also undertaken for a better understanding of its applicability. Programme metadata for 320 programmes from a large broadcaster was analysed together with the viewing history of over 50,000 people during a three-year period. The findings from the validation were eventually used to further refine the methodology and show that is it possible not only to infer individual viewers interests based on the programmes watched, but also to cluster the audience based on their content consumption habits and track the performance of various topics in terms of attracting new viewers. Having an effective way to infer viewers’ interests has various applications for the media market, most notably in the areas of better segmenting and targeting audiences, developing content that matches viewers’ interests, or improving existing recommendation engine

Third International Conference on Technologies for Music Notation and Representation TENOR 2017

The third International Conference on Technologies for Music Notation and Representation seeks to focus on a set of specific research issues associated with Music Notation that were elaborated at the first two editions of TENOR in Paris and Cambridge. The theme of the conference is vocal music, whereas the pre-conference workshops focus on innovative technological approaches to music notation

A software development framework for context-aware systems

The beginning of the new century has been characterised by the miniaturisation and accessibility of electronics, which has enabled its widespread usage around the world. This technological background is progressively materialising the future of the remainder of the century, where industry-based societies have been moving towards information-based societies. Information from users and their environment is now pervasively available, and many new research areas have born in order to shape the potential of such advancements. Particularly, context-aware computing is at the core of many areas such as Intelligent Environments, Ambient Intelligence, Ambient Assisted Living or Pervasive Computing. Embedding contextual awareness into computers promises a fundamental enhancement in the interaction between computers and humans. While traditional computers require explicit commands in order to operate, contextually aware computers could also use information from the background and the users to provide services according to the situation. But embedding this contextual awareness has many unresolved challenges. The area of context-aware computing has attracted the interest of many researchers that have presented different approaches to solve particular aspects on the implementation of this technology. The great corpus of research in this direction indicates that context-aware systems have different requirements than those of traditional computing. Approaches for developing context-aware systems are typically scattered or do not present compatibility with other approaches. Existing techniques for creating context-aware systems also do not focus on covering all the different stages of a typical software development life-cycle. The contribution of this thesis is towards the foundation layers of a more holistic approach, that tries to facilitate further research on the best techniques for developing these kinds of systems. The approach presents a framework to support the development not only with methodologies, but with open-source tools that facilitate the implementation of context-aware systems in mobile and stationary platforms