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  • 1.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Bridging the Semantic Gap between Sensor Data and Ontological Knowledge2015Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The rapid growth of sensor data can potentially enable a better awareness of the environment for humans. In this regard, interpretation of data needs to be human-understandable. For this, data interpretation may include semantic annotations that hold the meaning of numeric data. This thesis is about bridging the gap between quantitative data and qualitative knowledge to enrich the interpretation of data. There are a number of challenges which make the automation of the interpretation process non-trivial. Challenges include the complexity of sensor data, the amount of available structured knowledge and the inherent uncertainty in data. Under the premise that high level knowledge is contained in ontologies, this thesis investigates the use of current techniques in ontological knowledge representation and reasoning to confront these challenges. Our research is divided into three phases, where the focus of the first phase is on the interpretation of data for domains which are semantically poor in terms of available structured knowledge. During the second phase, we studied publicly available ontological knowledge for the task of annotating multivariate data. Our contribution in this phase is about applying a diagnostic reasoning algorithm to available ontologies. Our studies during the last phase have been focused on the design and development of a domain-independent ontological representation model equipped with a non-monotonic reasoning approach with the purpose of annotating time-series data. Our last contribution is related to coupling the OWL-DL ontology with a non-monotonic reasoner. The experimental platforms used for validation consist of a network of sensors which include gas sensors whose generated data is complex. A secondary data set includes time series medical signals representing physiological data, as well as a number of publicly available ontologies such as NCBO Bioportal repository.

  • 2.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Hammar, Karl
    SICS - East Swedish ICT, Linköping, Sweden; Jönköping University, Jönköping, Sweden.
    Blomqvist, Eva
    SICS - East Swedish ICT, Linköping, Sweden.
    SmartEnv as a Network of Ontology Patterns2018In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968Article in journal (Refereed)
    Abstract [en]

    In this article we outline the details of an ontology, called SmartEnv, proposed as a representational model to assist the development process of smart (i.e., sensorized) environments. The SmartEnv ontology is described in terms of its modules representing different aspects including physical and conceptual aspects of a smart environment. We propose the use of the Ontology Design Pattern (ODP) paradigm in order to modularize our proposed solution, while at the same time avoiding strong dependencies between the modules in order to manage the representational complexity of the ontology. The ODP paradigm and related methodologies enable incremental construction of ontologies by first creating and then linking small modules. Most modules (patterns) of the SmartEnv ontology are inspired by, and aligned with, the Semantic Sensor Network (SSN) ontology, however with extra interlinks to provide further precision and cover more representational aspects. The result is a network of 8 ontology patterns together forming a generic representation for a smart environment. The patterns have been submitted to the ODP portal and are available on-line at stable URIs.

  • 3.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology. Örebro University, School of Law, Psychology and Social Work.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Exploiting Context and Semantics for UAV Path-finding in an Urban Setting2017In: Proceedings of the 1st International Workshop on Application of Semantic Web technologies in Robotics (AnSWeR 2017), Portoroz, Slovenia, May 29th, 2017 / [ed] Emanuele Bastianelli, Mathieu d'Aquin, Daniele Nardi, Technical University Aachen , 2017, p. 11-20Conference paper (Refereed)
    Abstract [en]

    In this paper we propose an ontology pattern that represents paths in a geo-representation model to be used in an aerial path planning processes. This pattern provides semantics related to constraints (i.e., ight forbidden zones) in a path planning problem in order to generate collision free paths. Our proposed approach has been applied on an ontology containing geo-regions extracted from satellite imagery data from a large urban city as an illustrative example.

  • 4.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 11, article id 2545Article in journal (Refereed)
    Abstract [en]

    This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

  • 5.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Knowing without telling: integrating sensing and mapping for creating an artificial companion2016In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, New York, NY, USA: Association for Computing Machinery (ACM), 2016, p. 11:1-11:4Conference paper (Refereed)
    Abstract [en]

    This paper depicts a sensor-based map navigation approach which targets users, who due to disabilities or lack of technical knowledge are currently not in the focus of map system developments for personalized information. What differentiates our approach from the state-of-art mostly integrating localized social media data, is that our vision is to integrate real time sensor generated data that indicates the situation of dfferent phenomena (such as the physiological functions of the body) related to the user. The challenge hereby is mainly related to knowledge representation and integration. The tentative impact of our vision for future navigation systems is re ected within a scenario.

  • 6.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Automated reasoning using abduction for interpretation of medical signals2014In: Journal of Biomedical Semantics, ISSN 2041-1480, E-ISSN 2041-1480, Vol. 5, article id 35Article in journal (Refereed)
    Abstract [en]

    This paper proposes an approach to leverage upon existing ontologies in order to automate the annotation of time series medical data. The annotation is achieved by an abductive reasoner using parsimonious covering theorem in order to determine the best explanation or annotation for specific user defined events in the data. The novelty of this approach resides in part by the system’s flexibility in how events are defined by users and later detected by the system. This is achieved via the use of different ontologies which find relations between medical, lexical and numerical concepts. A second contribution resides in the application of an abductive reasoner which uses the online and existing ontologies to provide annotations. The proposed method is evaluated on datasets collected from ICU patients and the generated annotations are compared against those given by medical experts.

  • 7.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Automatic annotation of sensor data streams using abductive reasoning2013In: Automatic Annotation of Sensor Data Streams using AbductiveReasoning, SCITEPRESS, 2013, p. 345-354Conference paper (Refereed)
  • 8.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Ontology alignment for classification of low level sensor data2012Conference paper (Refereed)
  • 9.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Reasoning for Improved Sensor Data Interpretation in a Smart Home2014Conference paper (Refereed)
    Abstract [en]

    In this paper an ontological representation and reasoning paradigm has been proposed for interpretation of time-series signals. The signals come from sensors observing a smart environment. The signal chosen for the annotation process is a set of unintuitive and complexgas sensor data. The ontology of this paradigm is inspired form the SSNontology (Semantic Sensor Network) and used for representation of both the sensor data and the contextual information. The interpretation process is mainly done by an incremental ASP solver which as input receivesa logic program that is generated from the contents of the ontology. The contextual information together with high level domain knowledge given in the ontology are used to infer explanations (answer sets) for changes in the ambient air detected by the gas sensors.

  • 10.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Reasoning for sensor data interpretation: an application to air quality monitoring2015In: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 7, no 4, p. 579-597Article in journal (Refereed)
    Abstract [en]

    In this paper we introduce a representation and reasoning model for the interpretation of time-series signals of a gas sensor situated in a sensor network. The interpretation process includes inferring high level explanations for changes detected over the gas signals. Inspired from the Semantic Sensor Network (SSN), the ontology used in this work provides an adaptive way of modelling the domain-related knowledge. Furthermore, exploiting (Incremental) Answer Set Programming (ASP) enables a declarative and automatic way of rule definition. Converting the ontology concepts and relations into ASP logic programs, the interpretation process defines a logic program whose answer sets are considered as eventual explanations for the detected changes in the gas sensor signals. The proposed approach is tested in a kitchen environment which contains several objects monitored by different sensors. The contextual information provided by the sensor network together with high level domain knowledge are used to infer explanations for changes in the ambient air detected by the gas sensors.

  • 11.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Towards Automatic Ontology Alignment for Enriching Sensor Data Analysis2013In: Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937, Vol. 415, p. 179-193Article in journal (Refereed)
    Abstract [en]

    In this work ontology alignment is used to align an ontology comprising high level knowledge to a structure representing the results of low-level sensor data classification. To resolve inherent uncertainties from the data driven classifier, an ontology about application domain is aligned to the classifier output and the result is recommendation system able to suggest a course of action that will resolve the uncertainty. This work is instantiated in a medical application domain where signals from an electronic nose are classified into different bacteria types. In case of misclassifications resulting from the data driven classifier, the alignment to an ontology representing traditional microbiology tests suggests a subset of tests most relevant to use. The result is a hybrid classification system (electronic nose and traditional testing) that automatically exploits domain knowledge in the identification process.

  • 12.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images2016In: SDW 2016: Spatial Data on the Web, Proceedings / [ed] Krzysztof Janowicz et al., CEUR Workshop Proceedings , 2016, p. 5-8Conference paper (Refereed)
  • 13.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Sioutis, Michael
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    A Symbolic Approach for Explaining Errors in Image Classification Tasks2018Conference paper (Refereed)
    Abstract [en]

    Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has failed is the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. This paper presents an approach where the process of reasoning about errors emerging from a machine learning framework is automated using symbolic techniques. By utilizing spatial and geometrical reasoning between objects in a scene, the system is able to describe misclassified regions in relation to its context. The system is demonstrated in the remote sensing domain where objects and entities are detected in satellite images.

  • 14.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Context Recognition: Towards Automatic Query Generation2015In: Ambient Intelligence: 12th European Conference, AmI 2015, Athens, Greece, November 11-13, 2015, Proceedings, Springer, 2015, p. 205-218Conference paper (Refereed)
    Abstract [en]

    In this paper, we present an ontology-based approach in designing knowledge model for context recognition (CR) systems. The main focus in this paper is on the use of an ontology to facilitate the generation of user-based queries to the CR system. By leveraging from the ontology, users need not know about sensor details and the structure of the ontology in expressing queries related to events of interest. To validate the approach and demonstrate the flexibility of the ontology for query generation, the ontology has been integrated in two separate application domains. The first domain considers a health care system implemented for the GiraffPlus project where the query generation process is automated to request information about activities of daily living. The second application uses the same ontology for an air quality monitoring application in the home. Since these two systems are independently developed for different purposes, the ease of applying the ontology upon them can be considered as a credit for its generality.

  • 15.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Renoux, Jennifer
    Örebro University, School of Science and Technology.
    Köckemann, Uwe
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Blomqvist, Eva
    RISE SICS East, Linköping, Sweden.
    Tsiftes, Nicolas
    RISE SICS, Stockholm, Sweden.
    Voigt, Thiemo
    RISE SICS, Stockholm, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Ontology-based Context-aware System for Smart Homes: E-care@home2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 7, article id 1586Article in journal (Refereed)
    Abstract [en]

    Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

  • 16.
    Köckemann, Uwe
    et al.
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Integrating Ontologies for Context-based Constraint-based Planning2018In: MRC 2018: Modelling and Reasoning in Context, 2018, p. 22-29Conference paper (Refereed)
    Abstract [en]

    We describe an approach for integrating ontologies with a constraint-based planner to compile configuration planning domains based on the current context. We consider two alternative approaches: The first one integrates SPARQL queries directly with the planner while the second one generates SPARQL queries dynamically from provided triples. The first approach offers the full freedom of the SPARQL query language, while the second offers a more dynamic way for the planner to influence queries based on what is currently relevant for the planner. We evaluate the approach based on how much redundancy is removed by “outsourcing” knowledge into the ontology compared to modeling it directly into the domain of the planner.

  • 17.
    Längkvist, Martin
    et al.
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Interactive Learning with Convolutional Neural Networks for Image Labeling2016In: International Joint Conference on Artificial Intelligence (IJCAI), 2016Conference paper (Refereed)
    Abstract [en]

    Recently, deep learning models, such as Convolutional Neural Networks, have shown to give good performance for various computer vision tasks. A pre-requisite for such models is to have access to lots of labeled data since the most successful ones are trained with supervised learning. The process of labeling data is expensive, time-consuming, tedious, and sometimes subjective, which can result in falsely labeled data, which has a negative effect on both the training and the validation. In this work, we propose a human-in-the-loop intelligent system that allows the agent and the human to collabo- rate to simultaneously solve the problem of labeling data and at the same time perform scene labeling of an unlabeled image data set with minimal guidance by a human teacher. We evaluate the proposed in- teractive learning system by comparing the labeled data set from the system to the human-provided labels. The results show that the learning system is capable of almost completely label an entire image data set starting from a few labeled examples provided by the human teacher.

  • 18.
    Längkvist, Martin
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks2016In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 4, article id 329Article in journal (Refereed)
    Abstract [en]

    The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

  • 19.
    Persson, Andreas
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Rajasekaran, Balasubramanian
    Dept Sci & Technol, Ctr Appl Autonomous Sensor Syst AASS, Univ Örebro, Örebro, Sweden.
    Krishna, Vamsi
    Dept Science & Technology, Center for Applied Autonomous Sensor Syst (AASS), Örebro University, Örebro, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    I would like some food: anchoring objects to semantic web informationin human-robot dialogue interactions2013In: Social Robotics: Proceedings of 5th International Conference, ICSR 2013, Bristol, UK, October 27-29, 2013. / [ed] Guido Herrmann, Martin J. Pearson, Alexander Lenz, Paul Bremner, Adam Spiers, Ute Leonards, Springer, 2013, p. 361-370Conference paper (Refereed)
    Abstract [en]

    Ubiquitous robotic systems present a number of interesting application areas for socially assistive robots that aim to improve quality of life. In particular the combination of smart home environments and relatively inexpensive robots can be a viable technological solutions for assisting elderly and persons with disability in their own home. Such services require an easy interface like spoken dialogue and the ability to refer to physical objects using semantic terms. This paper presents an implemented system combining a robot and a sensor network deployed in a test apartment in an elderly residence area. The paper focuses on the creation and maintenance (anchoring) of the connection between the semantic information present in the dialogue with perceived physical objects in the home. Semantic knowledge about concepts and their correlations are retrieved from on-line resources and ontologies, e.g. WordNet, and sensor information is provided by cameras distributed in the apartment.

  • 20.
    Renoux, Jennifer
    et al.
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Köckemann, Uwe
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Context Recognition in Multiple Occupants Situations: Detecting the Number of Agents in a Smart Home Environment with Simple Sensors2017In: Knowledge-based techniques for problem solving and reasoning(KnowProS 2017): A workshop at AAAI 2017, February 5, 2017, San Francisco, U.S.A., Palo Alto: AAAI Press, 2017, Vol. ws17, p. 758-764, article id WS-17-12Conference paper (Refereed)
    Abstract [en]

    Context-recognition and activity recognition systems in multi-user environments such as smart homes, usually assume to know the number of occupants in the environment. However, being able to count the number of users in the environment is important in order to accurately recognize the activities of (groups of) agents. For smart environments without cameras, the problem of counting the number of agents is non-trivial. This is in part due to the difficulty of using a single non-vision based sensors to discriminate between one or several persons, and thus information from several sensors must be combined in order to reason about the presence of several agents. In this paper we address the problem of counting the number of agents in a topologically known environment using simple sensors that can indicate anonymous human presence. To do so, we connect an ontology to a probabilistic model (a Hidden Markov Model) in order to estimate the number of agents in each section of the environment. We evaluate our methods on a smart home setup where a number of motion and pressure sensors are distributed in various rooms of the home.

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