Relevant bibliographies by topics / Sensor data semantic annotation

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  1. Journal articles
  2. Dissertations / Theses
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  4. Book chapters
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Academic literature on the topic 'Sensor data semantic annotation'

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Published: 10 March 2023

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Journal articles on the topic "Sensor data semantic annotation"

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Sejdiu, Besmir, Florije Ismaili, and Lule Ahmedi. "Integration of Semantics Into Sensor Data for the IoT." International Journal on Semantic Web and Information Systems 16, no. 4 (October 2020): 1–25. http://dx.doi.org/10.4018/ijswis.2020100101.

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The internet of things (IoT) as an evolving technology represents an active scientific research field in recognizing research challenges associated with its application in various domains, ranging from consumer convenience, smart energy, and resource saving to IoT enterprises. Sensors are crucial components of IoT that relay the collected data in the form of the data stream for further processing. Interoperability of various connected digital resources is a key challenge in IoT environments. The enrichment of raw sensor data with semantic annotations using concept definitions from ontologies enables more expressive data representation that supports knowledge discovery. In this paper, a systematic review of integration of semantics into sensor data for the IoT is provided. The conducted review is focused on analyzing the main solutions of adding semantic annotations to the sensor data, standards that enable all types of sensor data via the web, existing models of stream data annotation, and the IoT trend domains that use semantics.
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Elsaleh, Tarek, Shirin Enshaeifar, Roonak Rezvani, Sahr Thomas Acton, Valentinas Janeiko, and Maria Bermudez-Edo. "IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services." Sensors 20, no. 4 (February 11, 2020): 953. http://dx.doi.org/10.3390/s20040953.

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With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so much for annotating stream data. Semantic models for stream annotation are scarce, as generally, semantics are heavy to process and not ideal for Internet of Things (IoT) environments, where the data are frequently updated. We present a light model to semantically annotate streams, IoT-Stream. It takes advantage of common knowledge sharing of the semantics, but keeping the inferences and queries simple. Furthermore, we present a system architecture to demonstrate the adoption the semantic model, and provide examples of instantiation of the system for different use cases. The system architecture is based on commonly used architectures in the field of IoT, such as web services, microservices and middleware. Our system approach includes the semantic annotations that take place in the pipeline of IoT services and sensory data analytics. It includes modules needed to annotate, consume, and query data annotated with IoT-Stream. In addition to this, we present tools that could be used in conjunction to the IoT-Stream model and facilitate the use of semantics in IoT.
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Llaves, Alejandro, Oscar Corcho, Peter Taylor, and Kerry Taylor. "Enabling RDF Stream Processing for Sensor Data Management in the Environmental Domain." International Journal on Semantic Web and Information Systems 12, no. 4 (October 2016): 1–21. http://dx.doi.org/10.4018/ijswis.2016100101.

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This paper presents a generic approach to integrate environmental sensor data efficiently, allowing the detection of relevant situations and events in near real-time through continuous querying. Data variety is addressed with the use of the Semantic Sensor Network ontology for observation data modelling, and semantic annotations for environmental phenomena. Data velocity is handled by distributing sensor data messaging and serving observations as RDF graphs on query demand. The stream processing engine presented in the paper, morph-streams++, provides adapters for different data formats and distributed processing of streams in a cluster. An evaluation of different configurations for parallelization and semantic annotation parameters proves that the described approach reduces the average latency of message processing in some cases.
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Xu, Hongsheng, and Huijuan Sun. "Application of Rough Concept Lattice Model in Construction of Ontology and Semantic Annotation in Semantic Web of Things." Scientific Programming 2022 (April 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/7207372.

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In order to solve the problem of interoperability in Internet of Things, the Semantic Web technology is introduced into the Internet of Things to form Semantic Web of Things. Ontology construction is the core of Semantic Web of Things. Firstly, this paper analyzes the shortcomings of ontology construction methods in the Semantic Web of Things. Then, this paper proposes construction of semantic ontology based on improved rough concept lattice, which provides theoretical basis for semantic annotation of the sensing data attributes. In addition, this paper describes the semantic annotation system for the Internet of Things based on semantic similarity of ontology. The system consists of three steps: ontology mapping integration module, information extraction module, and semantic annotation of sensing data. Finally, the experimental results show that this semantic annotation method effectively improves the flexibility of sensor information and data attributes and effectively enhances the expression ability of sensor information and the use value of data.
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Abdel Hakim, Alaa E., and Wael Deabes. "Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data." Algorithms 12, no. 10 (October 17, 2019): 217. http://dx.doi.org/10.3390/a12100217.

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In supervised Activities of Daily Living (ADL) recognition systems, annotating collected sensor readings is an essential, yet exhaustive, task. Readings are collected from activity-monitoring sensors in a 24/7 manner. The size of the produced dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. This results in annotation gaps in the input data to the adopting learning system. The performance of the recognition system is negatively affected by these gaps. In this work, we propose and investigate three different paradigms to handle these gaps. In the first paradigm, the gaps are taken out by dropping all unlabeled readings. A single “Unknown” or “Do-Nothing” label is given to the unlabeled readings within the operation of the second paradigm. The last paradigm handles these gaps by giving every set of them a unique label identifying the encapsulating certain labels. Also, we propose a semantic preprocessing method of annotation gaps by constructing a hybrid combination of some of these paradigms for further performance improvement. The performance of the proposed three paradigms and their hybrid combination is evaluated using an ADL benchmark dataset containing more than 2.5 × 10 6 sensor readings that had been collected over more than nine months. The evaluation results emphasize the performance contrast under the operation of each paradigm and support a specific gap handling approach for better performance.
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Sejdiu, Besmir, Florije Ismaili, and Lule Ahmedi. "IoTSAS: An Integrated System for Real-Time Semantic Annotation and Interpretation of IoT Sensor Stream Data." Computers 10, no. 10 (October 11, 2021): 127. http://dx.doi.org/10.3390/computers10100127.

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Sensors and other Internet of Things (IoT) technologies are increasingly finding application in various fields, such as air quality monitoring, weather alerts monitoring, water quality monitoring, healthcare monitoring, etc. IoT sensors continuously generate large volumes of observed stream data; therefore, processing requires a special approach. Extracting the contextual information essential for situational knowledge from sensor stream data is very difficult, especially when processing and interpretation of these data are required in real time. This paper focuses on processing and interpreting sensor stream data in real time by integrating different semantic annotations. In this context, a system named IoT Semantic Annotations System (IoTSAS) is developed. Furthermore, the performance of the IoTSAS System is presented by testing air quality and weather alerts monitoring IoT domains by extending the Open Geospatial Consortium (OGC) standards and the Sensor Observations Service (SOS) standards, respectively. The developed system provides information in real time to citizens about the health implications from air pollution and weather conditions, e.g., blizzard, flurry, etc.
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Desimoni, Federico, Sergio Ilarri, Laura Po, Federica Rollo, and Raquel Trillo-Lado. "Semantic Traffic Sensor Data: The TRAFAIR Experience." Applied Sciences 10, no. 17 (August 25, 2020): 5882. http://dx.doi.org/10.3390/app10175882.

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Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.
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Pacha, Shobharani, Suresh Ramalingam Murugan, and R. Sethukarasi. "Semantic annotation of summarized sensor data stream for effective query processing." Journal of Supercomputing 76, no. 6 (November 25, 2017): 4017–39. http://dx.doi.org/10.1007/s11227-017-2183-7.

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Vedurmudi, Anupam Prasad, Julia Neumann, Maximilian Gruber, and Sascha Eichstädt. "Semantic Description of Quality of Data in Sensor Networks." Sensors 21, no. 19 (September 28, 2021): 6462. http://dx.doi.org/10.3390/s21196462.

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The annotation of sensor data with semantic metadata is essential to the goals of automation and interoperability in the context of Industry 4.0. In this contribution, we outline a semantic description of quality of data in sensor networks in terms of indicators, metrics and interpretations. The concepts thus defined are consolidated into an ontology that describes quality of data metainformation in heterogeneous sensor networks and methods for the determination of corresponding quality of data dimensions are outlined. By incorporating support for sensor calibration models and measurement uncertainty via a previously derived ontology, a conformity with metrological requirements for sensor data is ensured. A quality description for a calibrated sensor generated using the resulting ontology is presented in the JSON-LD format using the battery level and calibration data as quality indicators. Finally, the general applicability of the model is demonstrated using a series of competency questions.
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Nadim, Ismail, Yassine El Ghayam, and Abdelalim Sadiq. "Semantic Annotation of Web of Things Using Entity Linking." International Journal of Business Analytics 7, no. 4 (October 2020): 1–13. http://dx.doi.org/10.4018/ijban.2020100101.

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The web of things (WoT) improves syntactic interoperability between internet of things (IoT) devices by leveraging web standards. However, the lack of a unified WoT data model remains a challenge for the semantic interoperability. Fortunately, semantic web technologies are taking this challenge over by offering numerous semantic vocabularies like the semantic sensor networks (SSN) ontology. Although it enables the semantic interoperability between heterogeneous devices, the manual annotation hinders the scalability of the WoT. As a result, the automation of the semantic annotation of WoT devices becomes a prior issue for researchers. This paper proposes a method to improve the semi-automatic semantic annotation of web of things (WoT) using the entity linking task and the well-known ontologies, mainly the SSN.
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Dissertations / Theses on the topic "Sensor data semantic annotation"

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Amir, Mohammad. "Semantically-enriched and semi-Autonomous collaboration framework for the Web of Things. Design, implementation and evaluation of a multi-party collaboration framework with semantic annotation and representation of sensors in the Web of Things and a case study on disaster management." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/14363.

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This thesis proposes a collaboration framework for the Web of Things based on the concepts of Service-oriented Architecture and integrated with semantic web technologies to offer new possibilities in terms of efficient asset management during operations requiring multi-actor collaboration. The motivation for the project comes from the rise in disasters where effective cross-organisation collaboration can increase the efficiency of critical information dissemination. Organisational boundaries of participants as well as their IT capability and trust issues hinders the deployment of a multi-party collaboration framework, thereby preventing timely dissemination of critical data. In order to tackle some of these issues, this thesis proposes a new collaboration framework consisting of a resource-based data model, resource-oriented access control mechanism and semantic technologies utilising the Semantic Sensor Network Ontology that can be used simultaneously by multiple actors without impacting each other’s networks and thus increase the efficiency of disaster management and relief operations. The generic design of the framework enables future extensions, thus enabling its exploitation across many application domains. The performance of the framework is evaluated in two areas: the capability of the access control mechanism to scale with increasing number of devices, and the capability of the semantic annotation process to increase in efficiency as more information is provided. The results demonstrate that the proposed framework is fit for purpose.
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Furno, Domenico. "Hybrid approaches based on computational intelligence and semantic web for distributed situation and context awareness." Doctoral thesis, Universita degli studi di Salerno, 2013. http://hdl.handle.net/10556/927.

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2011 - 2012
The research work focuses on Situation Awareness and Context Awareness topics. Specifically, Situation Awareness involves being aware of what is happening in the vicinity to understand how information, events, and one’s own actions will impact goals and objectives, both immediately and in the near future. Thus, Situation Awareness is especially important in application domains where the information flow can be quite high and poor decisions making may lead to serious consequences. On the other hand Context Awareness is considered a process to support user applications to adapt interfaces, tailor the set of application-relevant data, increase the precision of information retrieval, discover services, make the user interaction implicit, or build smart environments. Despite being slightly different, Situation and Context Awareness involve common problems such as: the lack of a support for the acquisition and aggregation of dynamic environmental information from the field (i.e. sensors, cameras, etc.); the lack of formal approaches to knowledge representation (i.e. contexts, concepts, relations, situations, etc.) and processing (reasoning, classification, retrieval, discovery, etc.); the lack of automated and distributed systems, with considerable computing power, to support the reasoning on a huge quantity of knowledge, extracted by sensor data. So, the thesis researches new approaches for distributed Context and Situation Awareness and proposes to apply them in order to achieve some related research objectives such as knowledge representation, semantic reasoning, pattern recognition and information retrieval. The research work starts from the study and analysis of state of art in terms of techniques, technologies, tools and systems to support Context/Situation Awareness. The main aim is to develop a new contribution in this field by integrating techniques deriving from the fields of Semantic Web, Soft Computing and Computational Intelligence. From an architectural point of view, several frameworks are going to be defined according to the multi-agent paradigm. Furthermore, some preliminary experimental results have been obtained in some application domains such as Airport Security, Traffic Management, Smart Grids and Healthcare. Finally, future challenges is going to the following directions: Semantic Modeling of Fuzzy Control, Temporal Issues, Automatically Ontology Elicitation, Extension to other Application Domains and More Experiments. [edited by author]
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Khan, Imran. "Cloud-based cost-efficient application and service provisioning in virtualized wireless sensor networks." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0019/document.

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Des Réseaux de Capteurs Sans Fil (RdCSF) deviennent omniprésents et sont utilisés dans diverses applications domaines. Ils sont les pierres angulaires de l'émergence de l'Internet des Objets (IdO) paradigme. Déploiements traditionnels de réseaux de capteurs sont spécifiques à un domaine, avec des applications généralement incrustés dans le RdCSF, excluant la ré-utilisation de l'infrastructure par d'autres applications. Maintenant, avec l'avènement de l'IdO, cette approche est de moins en moins viable. Une solution possible réside dans le partage d'une même RdCSF par de plusieurs applications et services, y compris même les applications et services qui ne sont pas envisagées lors du déploiement de RdCSF. Deux principaux développements majeurs ont conduit à cette solution potentielle. Premièrement, comme les nœuds de RdCSF sont de plus en plus puissants, il devient de plus en plus pertinent de rechercher comment pourrait plusieurs applications partager les mêmes déploiements WSN. La deuxième évolution est le Cloud Computing paradigme qui promeut des ressources et de la rentabilité en appliquant le concept de virtualisation les ressources physiques disponibles. Grâce à ces développements de cette thèse fait les contributions suivantes. Tout d'abord, un vaste état de la revue d'art est présenté qui présente les principes de base de RdCSF la virtualisation et sa pertinence avec précaution motive les scénarios sélectionnés. Les travaux existants sont présentés en détail et évaluées de manière critique en utilisant un ensemble d'exigences provenant du scénario. Cette contribution améliore sensiblement les critiques actuelles sur l'état de l'art en termes de portée, de la motivation, de détails, et les questions de recherche futures. La deuxième contribution se compose de deux parties: la première partie est une nouvelle architecture de virtualization RdCSF multicouche permet l'approvisionnement de plusieurs applications et services au cours du même déploiement de RdCSF. Il est mis en œuvre et évaluée en utilisant un prototype basé sur un scénario de preuve de concept en utilisant le kit Java SunSpot. La deuxième partie de cette contribution est l'architecture étendue qui permet à l’infrastructure virtualisée RdCSF d'interagir avec un RdCSF Platform-as-a-Service (PaaS) à un niveau d'abstraction plus élevé. Grâce à ces améliorations RdCSF PaaS peut provisionner des applications et des services RdCSF aux utilisateurs finaux que Software-as-a-Service (SaaS). Les premiers résultats sont présentés sur la base de l'implantation de l'architecture améliorée en utilisant le kit Java SunSpot. La troisième contribution est une nouvelle architecture d'annotation de données pour les applications sémantiques dans les environnements virtualisés les RdCSF. Il permet en réseau annotation de données et utilise des superpositions étant la pierre angulaire. Nous utilisons la base ontologie de domaine indépendant d'annoter les données du capteur. Un prototype de preuve de concept, basé sur un scénario, est développé et mis en œuvre en utilisant Java SunSpot, Kits AdvanticSys et Google App Engine. La quatrième et dernière contribution est l'amélioration à l'annotation de données proposée l'architecture sur deux fronts. L'un est l'extension à l'architecture proposée pour soutenir la création d'ontologie, de la distribution et la gestion. Le deuxième front est une heuristique génétique basée algorithme utilisé pour la sélection de noeuds capables de stocker l'ontologie de base. L'extension de la gestion d'ontologie est mise en oeuvre et évaluée à l'aide d'un prototype de validation de principe à l'aide de Java kit SunSpot, tandis que les résultats de la simulation de l'algorithme sont présentés
Wireless Sensor Networks (WSNs) are becoming ubiquitous and are used in diverse applications domains. Traditional deployments of WSNs are domain-specific, with applications usually embedded in the WSN, precluding the re-use of the infrastructure by other applications. This can lead to redundant deployments. Now with the advent of IoT, this approach is less and less viable. A potential solution lies in the sharing of a same WSN by multiple applications and services, to allow resource- and cost-efficiency. In this dissertation, three architectural solutions are proposed for this purpose. The first solution consists of two parts: the first part is a novel multilayer WSN virtualization architecture that allows the provisioning of multiple applications and services over the same WSN deployment. The second part of this contribution is the extended architecture that allows virtualized WSN infrastructure to interact with a WSN Platform-as-a-Service (PaaS) at a higher level of abstraction. Both these solutions are implemented and evaluated using two scenario-based proof-of-concept prototypes using Java SunSpot kit. The second architectural solution is a novel data annotation architecture for the provisioning of semantic applications in virtualized WSNs. It is capable of providing in-network, distributed, real-time annotation of raw sensor data and uses overlays as the cornerstone. This architecture is implemented and evaluated using Java SunSpot, AdvanticSys kits and Google App Engine. The third architectural solution is the enhancement to the data annotation architecture on two fronts. One is a heuristic-based genetic algorithm used for the selection of capable nodes for storing the base ontology. The second front is the extension to the proposed architecture to support ontology creation, distribution and management. The simulation results of the algorithm are presented and the ontology management extension is implemented and evaluated using a proof-of-concept prototype using Java SunSpot kit. As another contribution, an extensive state-of-the-art review is presented that introduces the basics of WSN virtualization and motivates its pertinence with carefully selected scenarios. This contribution substantially improves current state-of-the-art reviews in terms of the scope, motivation, details, and future research issues
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CUTRONA, VINCENZO. "Semantic Table Annotation for Large-Scale Data Enrichment." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/317044.

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I dati rappresentano uno dei principali asset che creano valore. L'analisi dei dati è diventata una componente cruciale negli studi scientifici e nelle decisioni aziendali negli ultimi anni e ha portato i ricercatori a definire nuove metodologie per rappresentare, gestire e analizzare i dati. Contemporaneamente, la crescita della potenza di calcolo ha permesso l'analisi di enormi quantità di dati, permettendo alle persone di estrarre informazioni utili dai dati raccolti. L'analisi predittiva gioca un ruolo cruciale in molte applicazioni poiché fornisce più conoscenza per supportare le decisioni aziendali. Tra le tecniche statistiche disponibili per supportare l'analitica predittiva, l'apprendimento automatico è una tecnica capace di risolvere molte classi diverse di problemi, e che ha beneficiato maggiormente della crescita della potenza di calcolo. Infatti, negli ultimi anni, sono stati proposti modelli di apprendimento automatico più complessi e accurati, che richiedono una quantità crescente di dati attuali e storici per funzionare al meglio. La richiesta di una quantità così massiccia di dati per addestrare i modelli di apprendimento automatico rappresenta un ostacolo iniziale per i data scientist, perché le informazioni necessarie sono di solito sparse in diversi set di dati che devono essere integrati manualmente. Di conseguenza, l'arricchimento dei dati è diventato un compito critico nel processo di preparazione dei dati, e al giorno d'oggi, la maggior parte dei progetti prevedere un processo di preparazione dei dati costoso in termini di tempo, volto ad arricchire un set di dati principali con informazioni aggiuntive da varie fonti esterne per migliorare la solidità dei modelli addestrati risultanti. Come facilitare la progettazione del processo di arricchimento per gli scienziati dei dati è una sfida, così come sostenere il processo di arricchimento su larga scala. Nonostante la crescente importanza dell'attività di arricchimento, essa è ancora supportata solo in misura limitata dalle soluzioni esistenti, delegando la maggior parte dello sforzo al data scientist, che è incaricato sia di rilevare i set di dati che contengono le informazioni necessarie, sia di integrarli. In questa tesi, introduciamo una metodologia per supportare l'attività di arricchimento dei dati, che si concentra sullo sfruttamento della semantica come fattore chiave, fornendo agli utenti uno strumento semantico per progettare il processo di arricchimento, insieme a una piattaforma per eseguire il processo su larga scala. Illustriamo come l'arricchimento dei dati può essere affrontato tramite trasformazioni di dati tabellari, sfruttando metodi di interpretazione semantica delle tabelle, e discutiamo le tecniche di implementazione per supportare l'esecuzione del processo risultante su grandi set di dati. Dimostriamo sperimentalmente la scalabilità e l'efficienza della soluzione proposta impiegandola in uno scenario del mondo reale. Infine, introduciamo un nuovo set di dati di riferimento per valutare le prestazioni e la scalabilità degli algoritmi di annotazione semantica delle tabelle, e proponiamo un nuovo approccio efficiente per migliorare le prestazioni di tali algoritmi.
Data are the new oil, and they represent one of the main value-creating assets. Data analytics has become a crucial component in scientific studies and business decisions in the last years and has brought researchers to define novel methodologies to represent, manage, and analyze data. Simultaneously, the growth of computing power enabled the analysis of huge amounts of data, allowing people to extract useful information from collected data. Predictive analytics plays a crucial role in many applications since it provides more knowledge to support business decisions. Among the statistical techniques available to support predictive analytics, machine learning is the technique that features capabilities to solve many different classes of problems, and that has benefited the most from computing power growth. In the last years, more complex and accurate machine learning models have been proposed, requiring an increasing amount of current and historical data to perform the best. The demand for such a massive amount of data to train machine learning models represents an initial hurdle for data scientists because the information needed is usually scattered in different data sets that have to be manually integrated. As a consequence, data enrichment has become a critical task in the data preparation process, and nowadays, most of all the data science projects involve a time-costly data preparation process aimed at enriching a core data set with additional information from various external sources to improve the sturdiness of resulting trained models. How to ease the design of the enrichment process for data scientists is defying and supporting the enrichment process at a large scale. Despite the growing importance of the enrichment task, it is still supported only to a limited extent by existing solutions, delegating most of the effort to the data scientist, who is in charge of both detecting the data sets that contain the needed information, and integrate them. In this thesis, we introduce a methodology to support the data enrichment task, which focuses on harnessing the semantics as the key factor by providing users with a semantics-aided tool to design the enrichment process, along with a platform to execute the process at a business scale. We illustrate how the data enrichment can be addressed via tabular data transformations exploiting semantic table interpretation methods, discussing implementation techniques to support the enactment of the resulting process on large data sets. We experimentally demonstrate the scalability and run-time efficiency of the proposed solution by employing it in a real-world scenario. Finally, we introduce a new benchmark dataset to evaluate the performance and the scalability of existing semantic table annotation algorithms, and we propose an efficient novel approach to improve the performance of such algorithms.
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Anderson, Neil David Alan. "Data extraction & semantic annotation from web query result pages." Thesis, Queen's University Belfast, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.705642.

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Our unquenchable thirst for knowledge is one of the few things that really defines our humanity. Yet the Information Age, which we have created, has left us floating aimlessly in a vast ocean of unintelligible data. Hidden Web databases are one massive source of structured data. The contents of these databases are, however, often only accessible through a query proposed by a user. The data returned in these Query Result Pages is intended for human consumption and, as such, has nothing more than an implicit semantic structure which can be understood visually by a human reader, but not by a computer. This thesis presents an investigation into the processes of extraction and semantic understanding of data from Query Result Pages. The work is multi-faceted and includes at the outset, the development of a vision-based data extraction tool. This work is followed by the development of a number of algorithms which make use of machine learning-based techniques first to align the data extracted into semantically similar groups and then to assign a meaningful label to each group. Part of the work undertaken in fulfilment of this thesis has also addressed the lack of large, modern datasets containing a wide range of result pages representing of those typically found online today. In particular, a new innovative crowdsourced dataset is presented. Finally, the work concludes by examining techniques from the complementary research field of Information Extraction. An initial, critical assessment of how these mature techniques could be applied to this research area is provided.
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Patni, Harshal Kamlesh. "Real Time Semantic Analysis of Streaming Sensor Data." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1324181415.

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Wong, Ping-wai, and 黃炳蔚. "Semantic annotation of Chinese texts with message structures based on HowNet." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38212389.

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Alirezaie, Marjan. "Bridging the Semantic Gap between Sensor Data and Ontological Knowledge." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-45908.

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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.
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Hatem, Muna Salman. "A framework for semantic web implementation based on context-oriented controlled automatic annotation." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/3207.

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The Semantic Web is the vision of the future Web. Its aim is to enable machines to process Web documents in a way that makes it possible for the computer software to "understand" the meaning of the document contents. Each document on the Semantic Web is to be enriched with meta-data that express the semantics of its contents. Many infrastructures, technologies and standards have been developed and have proven their theoretical use for the Semantic Web, yet very few applications have been created. Most of the current Semantic Web applications were developed for research purposes. This project investigates the major factors restricting the wide spread of Semantic Web applications. We identify the two most important requirements for a successful implementation as the automatic production of the semantically annotated document, and the creation and maintenance of semantic based knowledge base. This research proposes a framework for Semantic Web implementation based on context-oriented controlled automatic Annotation; for short, we called the framework the Semantic Web Implementation Framework (SWIF) and the system that implements this framework the Semantic Web Implementation System (SWIS). The proposed architecture provides for a Semantic Web implementation of stand-alone websites that automatically annotates Web pages before being uploaded to the Intranet or Internet, and maintains persistent storage of Resource Description Framework (RDF) data for both the domain memory, denoted by Control Knowledge, and the meta-data of the Web site's pages. We believe that the presented implementation of the major parts of SWIS introduce a competitive system with current state of art Annotation tools and knowledge management systems; this is because it handles input documents in the ii context in which they are created in addition to the automatic learning and verification of knowledge using only the available computerized corporate databases. In this work, we introduce the concept of Control Knowledge (CK) that represents the application's domain memory and use it to verify the extracted knowledge. Learning is based on the number of occurrences of the same piece of information in different documents. We introduce the concept of Verifiability in the context of Annotation by comparing the extracted text's meaning with the information in the CK and the use of the proposed database table Verifiability_Tab. We use the linguistic concept Thematic Role in investigating and identifying the correct meaning of words in text documents, this helps correct relation extraction. The verb lexicon used contains the argument structure of each verb together with the thematic structure of the arguments. We also introduce a new method to chunk conjoined statements and identify the missing subject of the produced clauses. We use the semantic class of verbs that relates a list of verbs to a single property in the ontology, which helps in disambiguating the verb in the input text to enable better information extraction and Annotation. Consequently we propose the following definition for the annotated document or what is sometimes called the 'Intelligent Document' 'The Intelligent Document is the document that clearly expresses its syntax and semantics for human use and software automation'. This work introduces a promising improvement to the quality of the automatically generated annotated document and the quality of the automatically extracted information in the knowledge base. Our approach in the area of using Semantic Web iii technology opens new opportunities for diverse areas of applications. E-Learning applications can be greatly improved and become more effective.
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Lindberg, Hampus. "Semantic Segmentation of Iron Ore Pellets in the Cloud." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-86896.

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This master's thesis evaluates data annotation, semantic segmentation and Docker for use in AWS. The data provided has to be annotated and is to be used as a dataset for the creation of a neural network. Different neural network models are then to be compared based on performance. AWS has the option to use Docker containers and thus that option is to be examined, and lastly the different tools available in AWS SageMaker will be analyzed for bringing a neural network to the cloud. Images were annotated in Ilastik and the dataset size is 276 images, then a neural network was created in PyTorch by using the library Segmentation Models PyTorch which gave the option of trying different models. This neural network was created in a notebook in Google Colab for a quick setup and easy testing. The dataset was then uploaded to AWS S3 and the notebook was brought from Colab to an AWS instance where the dataset then could be loaded from S3. A Docker container was created and packaged with the necessary packages and libraries as well as the training and inference code, to then be pushed to the ECR (Elastic Container Registry). This container could then be used to perform training jobs in SageMaker which resulted in a trained model stored in S3, and the hyperparameter tuning tool was also examined to get a better performing model. The two different deployment methods in SageMaker was then investigated to understand the entire machine learning solution. The images annotated in Ilastik were deemed sufficient as the neural network results were satisfactory. The neural network created was able to use all of the models accessible from Segmentation Models PyTorch which enabled a lot of options. By using a Docker container all of the tools available in SageMaker could be used with the created neural network packaged in the container and pushed to the ECR. Training jobs were run in SageMaker by using the container to get a trained model which could be saved to AWS S3. Hyperparameter tuning was used and got better results than the manually tested parameters which resulted in the best neural network produced. The model that was deemed the best was Unet++ in combination with the Dpn98 encoder. The two different deployment methods in SageMaker was explored and is believed to be beneficial in different ways and thus has to be reconsidered for each project. By analysis the cloud solution was deemed to be the better alternative compared to an in-house solution, in all three aspects measured, which was price, performance and scalability.
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Books on the topic "Sensor data semantic annotation"

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Padó, Sebastian. Cross-lingual annotation projection models for role-semantic information. Saarbrücken: Saarland University, 2007.

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Semantics Empowered Web 30 Managing Enterprise Social Sensor And Cloudbased Data And Services For Advanced Applications. MORGAN & CLAYPOOL PUBLISHERS, 2012.

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Semantic Multimedia 4th International Conference On Semantic And Digital Media Technologies Samt 2009 Graz Austria December 24 2009 Proceedings. Springer, 2010.

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Dowd, Cate. Digital Journalism, Drones, and Automation. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190655860.001.0001.

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Advances in online technology and news systems, such as automated reasoning across digital resources and connectivity to cloud servers for storage and software, have changed digital journalism production and publishing methods. Integrated media systems used by editors are also conduits to search systems and social media, but the lure of big data and rise in fake news have fragmented some layers of journalism, alongside investments in analytics and a shift in the loci for verification. Data has generated new roles to exploit data insights and machine learning methods, but access to big data and data lakes is so significant it has spawned newsworthy partnerships between media moguls and social media entrepreneurs. However, digital journalism does not even have its own semantic systems that could protect the values of journalism, but relies on the affordances of other systems. Amidst indexing and classification systems for well-defined vocabulary and concepts in news, data leaks and metadata present challenges for journalism. By contrast data visualisations and real-time field reporting with short-form mobile media and civilian drones set new standards during the European asylum seeker crisis. Aerial filming with drones also adds to the ontological base of journalism. An ontology for journalism and intersecting ontologies can inform the design of new semantic learning systems. The Semantic CAT Method, which draws on participatory design and game design, also assists the conceptual design of synthetic players with emotion attributes, towards a meta-model for learning. The design of context-aware sensor systems to protect journalists in conflict zones is also discussed.
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Book chapters on the topic "Sensor data semantic annotation"

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Wei, Wang, and Payam Barnaghi. "Semantic Annotation and Reasoning for Sensor Data." In Lecture Notes in Computer Science, 66–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04471-7_6.

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Vijayaprabakaran, K., and K. Sathiyamurthy. "A Framework for Semantic Annotation and Mapping of Sensor Data Streams Based on Multiple Linear Regression." In Advances in Intelligent Systems and Computing, 211–22. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3600-3_20.

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Windmann, Stefan, and Christian Kühnert. "Information modeling and knowledge extraction for machine learning applications in industrial production systems." In Machine Learning for Cyber Physical Systems, 73–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_8.

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AbstractIn this paper, a new information model for machine learning applications is introduced, which allows for a consistent acquisition and semantic annotation of process data, structural information and domain knowledge from industrial productions systems. The proposed information model is based on Industry 4.0 components and IEC 61360 component descriptions. To model sensor data, components of the OGC SensorThings model such as data streams and observations have been incorporated in this approach. Machine learning models can be integrated into the information model in terms of existing model serving frameworks like PMML or Tensorflowgraph. Based on the proposed information model, a tool chain for automatic knowledge extraction is introduced and the automatic classification of unstructured text is investigated as a particular application case for the proposed tool chain.
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Sejdiu, Besmir, Florije Ismaili, and Lule Ahmedi. "A Real-Time Integration of Semantic Annotations into Air Quality Monitoring Sensor Data." In Communications in Computer and Information Science, 98–113. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83007-6_5.

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Manonmani, M., and Sarojini Balakrishanan. "Semantic Annotation of Healthcare Data." In Handbook of Artificial Intelligence in Biomedical Engineering, 217–32. Series statement: Biomedical engineering: techniques and applications: Apple Academic Press, 2020. http://dx.doi.org/10.1201/9781003045564-10.

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Gil, Yolanda, Varun Ratnakar, and Ewa Deelman. "Metadata Catalogs with Semantic Representations." In Provenance and Annotation of Data, 90–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850_11.

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Pacifico, Stefano, Janez Starc, Janez Brank, Luka Bradesko, and Marko Grobelnik. "Semantic Annotation of Text Using Open Semantic Resources." In Encyclopedia of Machine Learning and Data Mining, 1–6. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_903-1.

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Pacifico, Stefano, Janez Starc, Janez Brank, Luka Bradesko, and Marko Grobelnik. "Semantic Annotation of Text Using Open Semantic Resources." In Encyclopedia of Machine Learning and Data Mining, 1132–37. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_903.

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Mozos, Óscar Martínez. "Semantic Information in Sensor Data." In Semantic Labeling of Places with Mobile Robots, 99–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11210-2_8.

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Chen, Liming, and Chris D. Nugent. "Semantic-Based Sensor Data Segmentation." In Human Activity Recognition and Behaviour Analysis, 127–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19408-6_6.

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Conference papers on the topic "Sensor data semantic annotation"

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Yu, Ching-Tzu, Yu-Hui Zou, Hao-Yu Li, and Szu-Yin Lin. "Automatic Clustering and Semantic Annotation for Dynamic IoT Sensor Data." In 2018 1st International Cognitive Cities Conference (IC3). IEEE, 2018. http://dx.doi.org/10.1109/ic3.2018.00-30.

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Khan, Imran, Rifat Jafrin, Fatima Zahra Errounda, Roch Glitho, Noel Crespi, Monique Morrow, and Paul Polakos. "A data annotation architecture for semantic applications in virtualized wireless sensor networks." In 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM). IEEE, 2015. http://dx.doi.org/10.1109/inm.2015.7140273.

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Karthik, N., and VS Ananthanarayana. "A Trust Model for Lightweight Semantic Annotation of Sensor Data in Pervasive Environment." In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS). IEEE, 2018. http://dx.doi.org/10.1109/icis.2018.8466471.

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Sejdiu, Besmir, Florije Ismaili, and Lule Ahmedi. "A Management Model of Real-time Integrated Semantic Annotations to the Sensor Stream Data for the IoT." In 16th International Conference on Web Information Systems and Technologies. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010111500590066.

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Oliveira, Pedro, and Joao Rocha. "Semantic annotation tools survey." In 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2013. http://dx.doi.org/10.1109/cidm.2013.6597251.

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Bader, Sebastian, and Jan Oevermann. "Semantic Annotation of Heterogeneous Data Sources." In Semantics2017: Semantics 2017 - 13th International Conference on Semantic Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132218.3132221.

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Amaral, Pedro, Pedro Oliveira, Márcio Moutinho, Daniel Matado, Ruben Costa, and João Sarraipa. "Semantic Annotation of Aquaculture Production Data." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67316.

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Aquaculture is probably the fastest growing food-producing sector in the world producing nearly 50 percent of the fish that is used for food, according to the Food and Agriculture Organization of the United Nations (FAO). With the growing of the Aquaculture sector, problems of global knowledge access, seamless data exchanges and lack of data reuse between aquaculture companies and its related stakeholders become more evident. From an IT perspective, aquaculture is characterized by high volumes of heterogeneous data, and lack of interoperability intra and inter-organizations. Each organization uses different data representations, using its native languages and legacy classification systems to manage and organize information, leading to a problem of integrating information from different sources due to lack of semantic interoperability that exists among knowledge organization tools used in different information systems. The lack of semantic interoperability that exists can be minimized, if innovative semantic techniques for representing, indexing and searching sources of non-structured information are applied. To address these issues, authors are developing a platform specifically designed for the aquaculture sector, which will allow even small companies to explore their data and extract knowledge, to improve in terms of use of feed, environmental impact, growth of the fish, cost, etc.
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Khurana, Udayan, and Sainyam Galhotra. "Semantic Concept Annotation for Tabular Data." In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459637.3482295.

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An, Hyoung-keun, and Jae-jin Koh. "Annotation of Multimedia data using Semantic Metadata." In 2006 International Forum on Strategic Technology. IEEE, 2006. http://dx.doi.org/10.1109/ifost.2006.312304.

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Little, Suzanne, Ovidio Salvetti, and Petra Perner. "Semi-Automatic Semantic Annotation of Images." In 2007 Seventh IEEE International Conference on Data Mining - Workshops (ICDM Workshops). IEEE, 2007. http://dx.doi.org/10.1109/icdmw.2007.22.

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