29 research outputs found

    Computational and human-based methods for knowledge discovery over knowledge graphs

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    The modern world has evolved, accompanied by the huge exploitation of data and information. Daily, increasing volumes of data from various sources and formats are stored, resulting in a challenging strategy to manage and integrate them to discover new knowledge. The appropriate use of data in various sectors of society, such as education, healthcare, e-commerce, and industry, provides advantages for decision support in these areas. However, knowledge discovery becomes challenging since data may come from heterogeneous sources with important information hidden. Thus, new approaches that adapt to the new challenges of knowledge discovery in such heterogeneous data environments are required. The semantic web and knowledge graphs (KGs) are becoming increasingly relevant on the road to knowledge discovery. This thesis tackles the problem of knowledge discovery over KGs built from heterogeneous data sources. We provide a neuro-symbolic artificial intelligence system that integrates symbolic and sub-symbolic frameworks to exploit the semantics encoded in a KG and its structure. The symbolic system relies on existing approaches of deductive databases to make explicit, implicit knowledge encoded in a KG. The proposed deductive database DSDS can derive new statements to ego networks given an abstract target prediction. Thus, DSDS minimizes data sparsity in KGs. In addition, a sub-symbolic system relies on knowledge graph embedding (KGE) models. KGE models are commonly applied in the KG completion task to represent entities in a KG in a low-dimensional vector space. However, KGE models are known to suffer from data sparsity, and a symbolic system assists in overcoming this fact. The proposed approach discovers knowledge given a target prediction in a KG and extracts unknown implicit information related to the target prediction. As a proof of concept, we have implemented the neuro-symbolic system on top of a KG for lung cancer to predict polypharmacy treatment effectiveness. The symbolic system implements a deductive system to deduce pharmacokinetic drug-drug interactions encoded in a set of rules through the Datalog program. Additionally, the sub-symbolic system predicts treatment effectiveness using a KGE model, which preserves the KG structure. An ablation study on the components of our approach is conducted, considering state-of-the-art KGE methods. The observed results provide evidence for the benefits of the neuro-symbolic integration of our approach, where the neuro-symbolic system for an abstract target prediction exhibits improved results. The enhancement of the results occurs because the symbolic system increases the prediction capacity of the sub-symbolic system. Moreover, the proposed neuro-symbolic artificial intelligence system in Industry 4.0 (I4.0) is evaluated, demonstrating its effectiveness in determining relatedness among standards and analyzing their properties to detect unknown relations in the I4.0KG. The results achieved allow us to conclude that the proposed neuro-symbolic approach for an abstract target prediction improves the prediction capability of KGE models by minimizing data sparsity in KGs

    ВСхнология комплСксной ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠΈ ΠΆΠΈΠ·Π½Π΅Π½Π½ΠΎΠ³ΠΎ Ρ†ΠΈΠΊΠ»Π° сСмантичСски совмСстимых ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Ρ‹Ρ… систСм Π½ΠΎΠ²ΠΎΠ³ΠΎ поколСния

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    Π’ ΠΈΠ·Π΄Π°Π½ΠΈΠΈ прСдставлСно описаниС Ρ‚Π΅ΠΊΡƒΡ‰Π΅ΠΉ вСрсии ΠΎΡ‚ΠΊΡ€Ρ‹Ρ‚ΠΎΠΉ Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ онтологичСского проСктирования, производства ΠΈ эксплуатации сСмантичСски совмСстимых Π³ΠΈΠ±Ρ€ΠΈΠ΄Π½Ρ‹Ρ… ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Ρ‹Ρ… систСм (Π’Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ OSTIS). ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π° стандартизация ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Ρ‹Ρ… систСм, Π° Ρ‚Π°ΠΊΠΆΠ΅ стандартизация ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² ΠΈ срСдств ΠΈΡ… проСктирования, Ρ‡Ρ‚ΠΎ являСтся ваТнСйшим Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠΌ, ΠΎΠ±Π΅ΡΠΏΠ΅Ρ‡ΠΈΠ²Π°ΡŽΡ‰ΠΈΠΌ ΡΠ΅ΠΌΠ°Π½Ρ‚ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ ΡΠΎΠ²ΠΌΠ΅ΡΡ‚ΠΈΠΌΠΎΡΡ‚ΡŒ ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Ρ‹Ρ… систСм ΠΈ ΠΈΡ… ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚ΠΎΠ², Ρ‡Ρ‚ΠΎ сущСствСнноС сниТСниС трудоСмкости Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Ρ‚Π°ΠΊΠΈΡ… систСм. Книга ΠΏΡ€Π΅Π΄Π½Π°Π·Π½Π°Ρ‡Π΅Π½Π° всСм, ΠΊΡ‚ΠΎ интСрСсуСтся ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΠ°ΠΌΠΈ искусствСнного ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚Π°, Π° Ρ‚Π°ΠΊΠΆΠ΅ спСциалистам Π² области ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Ρ‹Ρ… систСм ΠΈ ΠΈΠ½ΠΆΠ΅Π½Π΅Ρ€ΠΈΠΈ Π·Π½Π°Π½ΠΈΠΉ. ΠœΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ использована студСнтами, магистрантами ΠΈ аспирантами ΡΠΏΠ΅Ρ†ΠΈΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ Β«Π˜ΡΠΊΡƒΡΡΡ‚Π²Π΅Π½Π½Ρ‹ΠΉ ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚Β». Π’Π°Π±Π». 8. Ил. 223. Π‘ΠΈΠ±Π»ΠΈΠΎΠ³Ρ€.: 665 Π½Π°Π·Π²

    Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences

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    Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin

    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 β€œTechnologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part β€œProcesses 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

    Big Data in Bioeconomy

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    This edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen. With their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future. With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources

    A Web GIS-based Integration of 3D Digital Models with Linked Open Data for Cultural Heritage Exploration

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    This PhD project explores how geospatial semantic web concepts, 3D web-based visualisation, digital interactive map, and cloud computing concepts could be integrated to enhance digital cultural heritage exploration; to offer long-term archiving and dissemination of 3D digital cultural heritage models; to better interlink heterogeneous and sparse cultural heritage data. The research findings were disseminated via four peer-reviewed journal articles and a conference article presented at GISTAM 2020 conference (which received the β€˜Best Student Paper Award’)

    The Nexus Between Security Sector Governance/Reform and Sustainable Development Goal-16

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    This Security Sector Reform (SSR) Paper offers a universal and analytical perspective on the linkages between Security Sector Governance (SSG)/SSR (SSG/R) and Sustainable Development Goal-16 (SDG-16), focusing on conflict and post-conflict settings as well as transitional and consolidated democracies. Against the background of development and security literatures traditionally maintaining separate and compartmentalized presence in both academic and policymaking circles, it maintains that the contemporary security- and development-related challenges are inextricably linked, requiring effective measures with an accurate understanding of the nature of these challenges. In that sense, SDG-16 is surely a good step in the right direction. After comparing and contrasting SSG/R and SDG-16, this SSR Paper argues that human security lies at the heart of the nexus between the 2030 Agenda of the United Nations (UN) and SSG/R. To do so, it first provides a brief overview of the scholarly and policymaking literature on the development-security nexus to set the background for the adoption of The Agenda 2030. Next, it reviews the literature on SSG/R and SDGs, and how each concept evolved over time. It then identifies the puzzle this study seeks to address by comparing and contrasting SSG/R with SDG-16. After making a case that human security lies at the heart of the nexus between the UN’s 2030 Agenda and SSG/R, this book analyses the strengths and weaknesses of human security as a bridge between SSG/R and SDG-16 and makes policy recommendations on how SSG/R, bolstered by human security, may help achieve better results on the SDG-16 targets. It specifically emphasizes the importance of transparency, oversight, and accountability on the one hand, and participative approach and local ownership on the other. It concludes by arguing that a simultaneous emphasis on security and development is sorely needed for addressing the issues under the purview of SDG-16

    Knowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams

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    The rise of the Internet of Things leads to an unprecedented number of continuous sensor observations that are available as IoT data streams. Harmonization of such observations is a labor-intensive task due to heterogeneity in format, syntax, and semantics. We aim to reduce the effort for such harmonization tasks by employing a knowledge-driven approach. To this end, we pursue the idea of exploiting the large body of formalized public knowledge represented as statements in Linked Open Data

    Towards Interoperable Research Infrastructures for Environmental and Earth Sciences

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    This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a β€˜reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions
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