Desing and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.

OWL Reasoners still useable in 2023

In a systematic literature and software review over 100 OWL reasoners/systems were analyzed to see if they would still be usable in 2023. This has never been done in this capacity. OWL reasoners still play an important role in knowledge organisation and management, but the last comprehensive surveys/studies are more than 8 years old. The result of this work is a comprehensive list of 95 standalone OWL reasoners and systems using an OWL reasoner. For each item, information on project pages, source code repositories and related documentation was gathered. The raw research data is provided in a Github repository for anyone to use

mini me swift the first mobile owl reasoner for ios

Mobile reasoners play a pivotal role in the so-called Semantic Web of Things. While several tools exist for the Android platform, iOS has been neglected so far. This is due to architectural differences and unavailability of OWL manipulation libraries, which make porting existing engines harder. This paper presents Mini-ME Swift, the first Description Logics reasoner for iOS. It implements standard (Subsumption, Satisfiability, Classification, Consistency) and non-standard (Abduction, Contraction, Covering, Difference) inferences in an OWL 2 fragment. Peculiarities are discussed and performance results are presented, comparing Mini-ME Swift with other state-of-the-art OWL reasoners

Street-level desires, Discovering the city on foot:

In the spring of 2004, Delft University of Technology was approached by Norwich City Council with a request to participate in their project Spatial Metro. Spatial Metro was developed within the framework of Interreg IIIB. Interreg is a community initiative which aims to stimulate interregional cooperation within the EU, financed by the European Regional Development Fund (ERDF). The programme aspires to strengthen economic and social cohesion throughout the EU by fostering the balanced development of the continent through cross-border, transnational and interregional cooperation. The B strain of Interreg deals with transnational cooperation. As an Interreg IIIB project in the North-west European region, Spatial Metro brings together partners from the United Kingdom, France, Germany, The Netherlands and even Switzerland. The original Spatial Metro project proposal is straightforward. It claims that cities are chaotic places. It states that tourists, visiting business people, shoppers and even residents rarely have a clear or coherently expressed view of what a city has to offer geographically or thematically. The proposal assumes that people’s stay is shortened by their lack of overview of or information on what a town can offer them. As lead partner of the project, Norwich explains in quantitative terms what this means to the economy of a city: Visitors who plan a day trip to a city will stay in town for an average four to four-and-half hours and spend about £ 100. If the welcome they receive is inhospitable, the destination is confusing, and demands are not met, the same visitor will tend to leave after only two hours and spend less than £ 50. If their arrival is welcoming, the destination is safe, clean, relaxed and intelligible and if visitors can navigate their way around and their initial expectations are fulfilled or surpassed, they will stay for six to seven hours and spend more than £ 150. At first glance, these statements may seem somewhat narrow in scope. Not every city is chaotic and surely there is more to life than just money. However, placed in their proper context, these words make perfect sense. Five cities are participating in Spatial Metro: Norwich and Bristol (UK), Rouen (F), Koblenz (D) and Biel/Bienne (CH). Each of these cities is characterised by a historic city centre. Norwich itself is proud to have the most intact mediaeval street pattern of the United Kingdom. Mediaeval street patterns are the product of spontaneous urban growth and lack the sometimes rigid clarity of modern planned developments. Mediaeval street patterns are indeed difficult to navigate and pose a true challenge. Norwich also developed a successful and long-standing policy to prevent out of town shopping by strengthening the vitality of its original historic district. Such a policy requires a city to take a serious look at its economic performance. From this perspective, it is a sound approach to optimise conditions allowing people to discover a city on foot. As such, the Spatial Metro project prompted the Delft University of Technology to tap into a greater European experience that integrates aspects such as urban renaissance, built heritage, public space, pedestrian mobility, leisure economy and even sustainability. The partnership also included knowledge organisations. Each of these partners has supported the project in their own unique way. The University of East Anglia deployed its automated modelling software to visualise the original historic centres. The University Koblenz/Landau delivered a so-called Blue Box that provides on the spot information by means of Bluetooth technology. The Swiss Pedestrian Association made various contributions as a strategic and competent expert organisation on pedestrian mobility. The Delft University of Technology examined the question as to how to assess the effectiveness of the investments made in Norwich, Rouen and Koblenz. How can aspects like the accessibility and navigability of public spaces be measured? Much of the effectiveness hereof naturally depends on the way people use the public space. We used novel tools to analyse in detail the movement patterns of people visiting these three city centres. Finally, Delft decided to capture the essence of the Spatial Metro experience in a document reflecting the versatility of the transnational response to pedestrian mobility and the regeneration of the historic European city centre. The document became this book, ‘Street Level Desires’. The book aims to disseminate our experience and knowledge to further strengthen social and economic cohesion throughout Europe. &nbsp

Semantic Management of Location-Based Services in Wireless Environments

En los últimos años el interés por la computación móvil ha crecido debido al incesante uso de dispositivos móviles (por ejemplo, smartphones y tablets) y su ubicuidad. El bajo coste de dichos dispositivos unido al gran número de sensores y mecanismos de comunicación que equipan, hace posible el desarrollo de sistemas de información útiles para sus usuarios. Utilizando un cierto tipo especial de sensores, los mecanismos de posicionamiento, es posible desarrollar Servicios Basados en la Localización (Location-Based Services o LBS en inglés) que ofrecen un valor añadido al considerar la localización de los usuarios de dispositivos móviles para ofrecerles información personalizada. Por ejemplo, se han presentado numerosos LBS entre los que se encuentran servicios para encontrar taxis, detectar amigos en las cercanías, ayudar a la extinción de incendios, obtener fotos e información de los alrededores, etc. Sin embargo, los LBS actuales están diseñados para escenarios y objetivos específicos y, por lo tanto, están basados en esquemas predefinidos para el modelado de los elementos involucrados en estos escenarios. Además, el conocimiento del contexto que manejan es implícito; razón por la cual solamente funcionan para un objetivo específico. Por ejemplo, en la actualidad un usuario que llega a una ciudad tiene que conocer (y comprender) qué LBS podrían darle información acerca de medios de transporte específicos en dicha ciudad y estos servicios no son generalmente reutilizables en otras ciudades. Se han propuesto en la literatura algunas soluciones ad hoc para ofrecer LBS a usuarios pero no existe una solución general y flexible que pueda ser aplicada a muchos escenarios diferentes. Desarrollar tal sistema general simplemente uniendo LBS existentes no es sencillo ya que es un desafío diseñar un framework común que permita manejar conocimiento obtenido de datos enviados por objetos heterogéneos (incluyendo datos textuales, multimedia, sensoriales, etc.) y considerar situaciones en las que el sistema tiene que adaptarse a contextos donde el conocimiento cambia dinámicamente y en los que los dispositivos pueden usar diferentes tecnologías de comunicación (red fija, inalámbrica, etc.). Nuestra propuesta en la presente tesis es el sistema SHERLOCK (System for Heterogeneous mobilE Requests by Leveraging Ontological and Contextual Knowledge) que presenta una arquitectura general y flexible para ofrecer a los usuarios LBS que puedan serles interesantes. SHERLOCK se basa en tecnologías semánticas y de agentes: 1) utiliza ontologías para modelar la información de usuarios, dispositivos, servicios, y el entorno, y un razonador para manejar estas ontologías e inferir conocimiento que no ha sido explicitado; 2) utiliza una arquitectura basada en agentes (tanto estáticos como móviles) que permite a los distintos dispositivos SHERLOCK intercambiar conocimiento y así mantener sus ontologías locales actualizadas, y procesar peticiones de información de sus usuarios encontrando lo que necesitan, allá donde esté. El uso de estas dos tecnologías permite a SHERLOCK ser flexible en términos de los servicios que ofrece al usuario (que son aprendidos mediante la interacción entre los dispositivos), y de los mecanismos para encontrar la información que el usuario quiere (que se adaptan a la infraestructura de comunicación subyacente)