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Biological Ontologies and Semantic Biology

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889192779 Year: Pages: 106 DOI: 10.3389/978-2-88919-277-9 Language: English
Publisher: Frontiers Media SA
Subject: Botany --- Biotechnology --- General and Civil Engineering --- Genetics --- Science (General)
Added to DOAB on : 2015-12-03 13:02:24
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As the amount of biological information and its diversity accumulates massively there is a critical need to facilitate the integration of this data to allow new and unexpected conclusions to be drawn from it. The Semantic Web is a new wave of web- based technologies that allows the linking of data between diverse data sets via standardised data formats (“big data”). Semantic Biology is the application of semantic web technology in the biological domain (including medical and health informatics). The Special Topic encompasses papers in this very broad area, including not only ontologies (development and applications), but also text mining, data integration and data analysis making use of the technologies of the Semantic Web. Ontologies are a critical requirement for such integration as they allow conclusions drawn about biological experiments, or descriptions of biological entities, to be understandable and integratable despite being contained in different databases and analysed by different software systems. Ontologies are the standard structures used in biology, and more broadly in computer science, to hold standardized terminologies for particular domains of knowledge. Ontologies consist of sets of standard terms, which are defined and may have synonyms for ease of searching and to accommodate different usages by different communities. These terms are linked by standard relationships, such as “is_a” (an eye “is_a” sense organ) or “part_of” (an eye is “part_of” a head). By linking terms in this way, more detailed, or granular, terms can be linked to broader terms, allowing computation to be carried out that takes these relationships into account.

Semantic technologies for distributed information systems

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ISBN: 9783866441002 Year: Pages: XIV, 226 p. DOI: 10.5445/KSP/1000005707 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:02:01
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In this book we show how semantic technologies and in particular the use of ontologies can be employed to address a number of typical challenges in distributed information systems: First, we illustrate how ontologies enable the integration of information across heterogeneous nodes. Second, we show how ontology evolution allows dealing with the dynamics in the information in a consistent manner. Third, we present a model for ontologybased coordination using semantic overlay networks.

Knowledge Graphs and Big Data Processing

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Book Series: Lecture Notes in Computer Science; Information Systems and Applications, incl. Internet/Web, and HCI ISBN: 9783030531997 Year: Pages: 209 DOI: 10.1007/978-3-030-53199-7 Language: English
Publisher: Springer Nature
Subject: Computer Science --- Political Science --- Business and Management
Added to DOAB on : 2020-09-01 00:02:02
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This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.

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