Search (5 results, page 1 of 1)

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Abstract

This book describes proven methodologies for developing semantic applications: software applications which explicitly or implicitly uses the semantics (i.e., the meaning) of a domain terminology in order to improve usability, correctness, and completeness. An example is semantic search, where synonyms and related terms are used for enriching the results of a simple text-based search. Ontologies, thesauri or controlled vocabularies are the centerpiece of semantic applications. The book includes technological and architectural best practices for corporate use.

Content

Introduction.- Ontology Development.- Compliance using Metadata.- Variety Management for Big Data.- Text Mining in Economics.- Generation of Natural Language Texts.- Sentiment Analysis.- Building Concise Text Corpora from Web Contents.- Ontology-Based Modelling of Web Content.- Personalized Clinical Decision Support for Cancer Care.- Applications of Temporal Conceptual Semantic Systems.- Context-Aware Documentation in the Smart Factory.- Knowledge-Based Production Planning for Industry 4.0.- Information Exchange in Jurisdiction.- Supporting Automated License Clearing.- Managing cultural assets: Implementing typical cultural heritage archive's usage scenarios via Semantic Web technologies.- Semantic Applications for Process Management.- Domain-Specific Semantic Search Applications.

LCSH

Information storage and retrieval
Information Storage and Retrieval

RSWK

Information Retrieval
Semantic Web

Subject

Information Retrieval
Semantic Web
Information storage and retrieval
Information Storage and Retrieval

Theme

Semantic Web

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Abstract

Beim Corporate Semantic Web betrachtet man Semantic Web-Anwendungen, die innerhalb eines Unternehmens oder einer Organisation - kommerziell und nicht kommerziell - eingesetzt werden, von Mitarbeitern, von Kunden oder Partnern. Die Autoren erläutern prägende Erfahrungen in der Entwicklung von Semantic Web-Anwendungen. Sie berichten über Software-Architektur, Methodik, Technologieauswahl, Linked Open Data Sets, Lizenzfragen etc. Anwendungen aus den Branchen Banken, Versicherungen, Telekommunikation, Medien, Energie, Maschinenbau, Logistik, Touristik, Spielwaren, Bibliothekswesen und Kultur werden vorgestellt. Der Leser erhält so einen umfassenden Überblick über die Semantic Web-Einsatzbereiche sowie konkrete Umsetzungshinweise für eigene Vorhaben.

Content

Kapitel 1; Corporate Semantic Web; 1.1 Das Semantic Web; 1.2 Semantische Anwendungen im Unternehmenseinsatz; 1.3 Bereitstellen von Linked Data reicht nicht; 1.4 Eine global vernetzte Wissensbasis -- Fiktion oder Realität?; 1.5 Semantik)=)RDF?; 1.6 Richtig vorgehen; 1.7 Modellieren ist einfach (?!); 1.8 Juristische Fragen; 1.9 Semantische Anwendungen stiften Nutzen in Unternehmen -- nachweislich!; 1.10 Fazit; Literatur; Kapitel 2; Einordnung und Abgrenzung des Corporate Semantic Webs; 2.1 Grundlegende Begriffe; 2.2 Corporate Semantic Web 2.3 Public Semantic Web2.4 Social Semantic Web 3.0; 2.5 Pragmatic Web; 2.6 Zusammenfassung und Ausblick "Ubiquitous Pragmatic Web 4.0"; Literatur; Kapitel 3; Marktstudie: Welche Standards und Tools werden in Unternehmen eingesetzt?; 3.1 Einleitung; 3.2 Semantische Suche in Webarchiven (Quantinum AG); 3.2.1 Kundenanforderungen; 3.2.2 Technische Umsetzung; 3.2.3 Erfahrungswerte; 3.3 Semantische Analyse und Suche in Kundenspezifikationen (Ontos AG); 3.3.1 Kundenanforderungen; 3.3.2 Technische Umsetzung; 3.3.3 Erfahrungswerte 3.4 Sicherheit für Banken im Risikomanagement (VICO Research & Consulting GmbH)3.4.1 Kundenanforderungen; 3.4.2 Technische Umsetzung; 3.4.3 Erfahrungswerte; 3.5 Interaktive Fahrzeugdiagnose (semafora GmbH); 3.5.1 Kundenanforderungen; 3.5.2 Technische Umsetzung; 3.5.3 Erfahrungswerte; 3.6 Quo Vadis?; 3.7 Umfrage-Ergebnisse; 3.8 Semantic Web Standards & Tools; 3.9 Ausblick; Literatur; Kapitel 4; Modellierung des Sprachraums von Unternehmen; 4.1 Hintergrund; 4.2 Eine Frage der Bedeutung; 4.3 Bedeutung von Begriffen im Unternehmenskontext; 4.3.1 Website-Suche bei einem Industrieunternehmen 4.3.2 Extranet-Suche bei einem Marktforschungsunternehmen4.3.3 Intranet-Suche bei einem Fernsehsender; 4.4 Variabilität unserer Sprache und unseres Sprachgebrauchs; 4.4.1 Konsequenzen des Sprachgebrauchs; 4.5 Terminologiemanagement und Unternehmensthesaurus; 4.5.1 Unternehmensthesaurus; 4.5.2 Mut zur Lücke: Arbeiten mit unvollständigen Terminologien; 4.6 Pragmatischer Aufbau von Unternehmensthesauri; 4.6.1 Begriffsanalyse des Anwendungsbereichs; 4.6.2 Informationsquellen; 4.6.3 Häufigkeitsverteilung; 4.6.4 Aufwand und Nutzen; Literatur; Kapitel 5 Schlendern durch digitale Museen und Bibliotheken5.1 Einleitung; 5.2 Anwendungsfall 1: Schlendern durch das Digitale Museum; 5.3 Anwendungsfall 2: Literatur in Bibliotheken finden; 5.4 Herausforderungen; 5.5 Die Anforderungen treiben die Architektur; 5.5.1 Semantic ETL; 5.5.2 Semantic Logic; 5.5.3 Client; 5.6 Diskussion; 5.7 Empfehlungen und Fazit; Literatur; Kapitel 6; Semantische Suche im Bereich der Energieforschungsförderung; 6.1 Das Projekt EnArgus®; 6.2 Die Fachontologie; 6.2.1 Semantische Suche; 6.2.2 Repräsentation der semantischen Relationen in der Fachontologie

LCSH

Information storage and retrieval system

RSWK

Unternehmen / Semantic Web / Aufsatzsammlung

Subject

Unternehmen / Semantic Web / Aufsatzsammlung
Information storage and retrieval system

Theme

Semantic Web

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LCSH

Information storage and retrieval systems

Subject

Information storage and retrieval systems

Theme

Semantic Web

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LCSH

Information storage and retrieval systems

Subject

Information storage and retrieval systems

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Abstract

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.