Reasoning Web. Explainable Artificial Intelligence - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Reasoning Web. Explainable Artificial Intelligence write by Markus Krötzsch. This book was released on 2019-09-17. Reasoning Web. Explainable Artificial Intelligence available in PDF, EPUB and Kindle. This volume contains lecture notes of the 15th Reasoning Web Summer School (RW 2019), held in Bolzano, Italy, in September 2019. The research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.
Reasoning Web. Declarative Artificial Intelligence
Reasoning Web. Declarative Artificial Intelligence - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Reasoning Web. Declarative Artificial Intelligence write by Marco Manna. This book was released on 2020-10-17. Reasoning Web. Declarative Artificial Intelligence available in PDF, EPUB and Kindle. This volume contains 8 lecture notes of the 16th Reasoning Web Summer School (RW 2020), held in Oslo, Norway, in June 2020. The Reasoning Web series of annual summer schools has become the prime educational event in the field of reasoning techniques on the Web, attracting both young and established researchers. The broad theme of this year's summer school was “Declarative Artificial Intelligence” and it covered various aspects of ontological reasoning and related issues that are of particular interest to Semantic Web and Linked Data applications. The following eight lectures have been presented during the school: Introduction to Probabilistic Ontologies, On the Complexity of Learning Description Logic Ontologies, Explanation via Machine Arguing, Stream Reasoning: From Theory to Practice, First-Order Rewritability of Temporal Ontology-Mediated Queries, An Introduction to Answer Set Programming and Some of Its Extensions, Declarative Data Analysis using Limit Datalog Programs, and Knowledge Graphs: Research Directions.
Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges
Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges write by I. Tiddi. This book was released on 2020-05-06. Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges available in PDF, EPUB and Kindle. The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.
Role of Explainable Artificial Intelligence in E-Commerce
Role of Explainable Artificial Intelligence in E-Commerce - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Role of Explainable Artificial Intelligence in E-Commerce write by Loveleen Gaur. This book was released on . Role of Explainable Artificial Intelligence in E-Commerce available in PDF, EPUB and Kindle.
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Intelligence in Medicine write by David Riaño. This book was released on 2019-06-19. Artificial Intelligence in Medicine available in PDF, EPUB and Kindle. This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
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