62 research outputs found

    The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information

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    Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.publishedVersio

    Semantic technologies for supporting KDD processes

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    209 p.Achieving a comfortable thermal situation within buildings with an efficient use of energy remains still an open challenge for most buildings. In this regard, IoT (Internet of Things) and KDD (Knowledge Discovery in Databases) processes may be combined to solve these problems, even though data analysts may feel overwhelmed by heterogeneity and volume of the data to be considered. Data analysts could benefit from an application assistant that supports them throughout the KDD process. This research work aims at supporting data analysts through the different KDD phases towards the achievement of energy efficiency and thermal comfort in tertiary buildings. To do so, the EEPSA (Energy Efficiency Prediction Semantic Assistant) is proposed, which aids data analysts discovering the most relevant variables for the matter at hand, and informs them about relationships among relevant data. This assistant leverages Semantic Technologies such as ontologies, ontology-driven rules and ontology-driven data access. More specifically, the EEPSA ontology is the cornerstone of the assistant. This ontology is developed on top of three ODPs (Ontology Design Patterns) and it is designed so that its customization to address similar problems in different types of buildings can be approached methodically

    Behaviour on Linked Data - Specification, Monitoring, and Execution

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    People, organisations, and machines around the globe make use of web technologies to communicate. For instance, 4.16 bn people with access to the internet made 4.6 bn pages on the web accessible using the transfer protocol HTTP, organisations such as Amazon built ecosystems around the HTTP-based access to their businesses under the headline RESTful APIs, and the Linking Open Data movement has put billions of facts on the web available in the data model RDF via HTTP. Moreover, under the headline Web of Things, people use RDF and HTTP to access sensors and actuators on the Internet of Things. The necessary communication requires interoperable systems at a truly global scale, for which web technologies provide the necessary standards regarding the transfer and the representation of data: the HTTP protocol specifies how to transfer messages, besides defining the semantics of sending/receiving different types of messages, and the RDF family of languages specifies how to represent the data in the messages, besides providing means to elaborate the semantics of the data in the messages. The combination of HTTP and RDF -together with the shared assumption of HTTP and RDF to use URIs as identifiers- is called Linked Data. While the representation of static data in the context of Linked Data has been formally grounded in mathematical logic, a formal treatment of dynamics and behaviour on Linked Data is largely missing. We regard behaviour in this context as the way in which a system (e.g. a user agent or server) works, and this behaviour manifests itself in dynamic data. Using a formal treatment of behaviour on Linked Data, we could specify applications that use or provide Linked Data in a way that allows for formal analysis (e.g. expressivity, validation, verification). Using an experimental treatment of behaviour, or a treatment of the behaviour\u27s manifestation in dynamic data, we could better design the handling of Linked Data in applications. Hence, in this thesis, we investigate the notion of behaviour in the context of Linked Data. Specifically, we investigate the research question of how to capture the dynamics of Linked Data to inform the design of applications. The first contribution is a corpus that we built and analysed to monitor dynamic Linked Data on the web to study the update behaviour. We provide an extensive analysis to set up a long-term study of the dynamics of Linked Data on the web. We analyse data from the long-term study for dynamics on the level of accessing changing documents and on the level of changes within the documents. The second contribution is a model of computation for Linked Data that allows for expressing executable specifications of application behaviour. We provide a mapping from the conceptual foundations of the standards around Linked Data to Abstract State Machines, a Turing-complete model of computation rooted in mathematical logic. The third contribution is a workflow ontology and corresponding operational semantics to specify applications that execute and monitor behaviour in the context of Linked Data. Our approach allows for monitoring and executing behaviour specified in workflow models and respects the assumptions of the standards and practices around Linked Data. We evaluate our findings using the experimental corpus of dynamic Linked Data on the web and a synthetic benchmark from the Internet of Things, specifically the domain of building automation

    Current and Future Challenges in Knowledge Representation and Reasoning

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    Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

    A Knowledge Graph Based Integration Approach for Industry 4.0

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    The fourth industrial revolution, Industry 4.0 (I40) aims at creating smart factories employing among others Cyber-Physical Systems (CPS), Internet of Things (IoT) and Artificial Intelligence (AI). Realizing smart factories according to the I40 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this communication, CPS along with their data need to be described and interoperability conflicts arising from various representations need to be resolved. For establishing interoperability, industry communities have created standards and standardization frameworks. Standards describe main properties of entities, systems, and processes, as well as interactions among them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Despite being published by official international organizations, different standards may contain divergent definitions for similar entities. Further, when utilizing the same standard for the design of a CPS, different views can generate interoperability conflicts. Albeit expressive, standardization frameworks may represent divergent categorizations of the same standard to some extent, interoperability conflicts need to be resolved to support effective and efficient communication in smart factories. To achieve interoperability, data need to be semantically integrated and existing conflicts conciliated. This problem has been extensively studied in the literature. Obtained results can be applied to general integration problems. However, current approaches fail to consider specific interoperability conflicts that occur between entities in I40 scenarios. In this thesis, we tackle the problem of semantic data integration in I40 scenarios. A knowledge graphbased approach allowing for the integration of entities in I40 while considering their semantics is presented. To achieve this integration, there are challenges to be addressed on different conceptual levels. Firstly, defining mappings between standards and standardization frameworks; secondly, representing knowledge of entities in I40 scenarios described by standards; thirdly, integrating perspectives of CPS design while solving semantic heterogeneity issues; and finally, determining real industry applications for the presented approach. We first devise a knowledge-driven approach allowing for the integration of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The standards ontology is used for representing the main properties of standards and standardization frameworks, as well as relationships among them. The I40KG permits to integrate standards and standardization frameworks while solving specific semantic heterogeneity conflicts in the domain. Further, we semantically describe standards in knowledge graphs. To this end, standards of core importance for I40 scenarios are considered, i.e., the Reference Architectural Model for I40 (RAMI4.0), AutomationML, and the Supply Chain Operation Reference Model (SCOR). In addition, different perspectives of entities describing CPS are integrated into the knowledge graphs. To evaluate the proposed methods, we rely on empirical evaluations as well as on the development of concrete use cases. The attained results provide evidence that a knowledge graph approach enables the effective data integration of entities in I40 scenarios while solving semantic interoperability conflicts, thus empowering the communication in smart factories

    Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

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    Currently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life and to promote an improvement in the treatments of patients athome that suffer from different pathologies. That is why there has emerged a line of work of greatinterest, focused on the study and analysis of daily life activities, on the use of different data analysistechniques to identify and to help manage this type of patient. This article shows the result of thesystematic review of the literature on the use of the Clustering method, which is one of the mostused techniques in the analysis of unsupervised data applied to activities of daily living, as well asthe description of variables of high importance as a year of publication, type of article, most usedalgorithms, types of dataset used, and metrics implemented. These data will allow the reader tolocate the recent results of the application of this technique to a particular area of knowledg

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

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    A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC

    Enhanced Living Environments

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    This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
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