Search (9 results, page 1 of 1)

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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.

Content

Inhalt: An Introduction to Text Mining.- Information Extraction from Text.- A Survey of Text Summarization Techniques.- A Survey of Text Clustering Algorithms.- Dimensionality Reduction and Topic Modeling.- A Survey of Text Classification Algorithms.- Transfer Learning for Text Mining.- Probabilistic Models for Text Mining.- Mining Text Streams.- Translingual Mining from Text Data.- Text Mining in Multimedia.- Text Analytics in Social Media.- A Survey of Opinion Mining and Sentiment Analysis.- Biomedical Text Mining: A Survey of Recent Progress.- Index.

LCSH

Data mining

RSWK

Text Mining / Aufsatzsammlung

Subject

Text Mining / Aufsatzsammlung
Data mining

Theme

Data Mining

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LCSH

Data mining

RSWK

Data Mining / Kongress / Cochin <Kerala, 2010>

Subject

Data Mining / Kongress / Cochin <Kerala, 2010>
Data mining

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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

Data mining
Data Mining and Knowledge Discovery

RSWK

Data Mining

Subject

Data Mining
Data mining
Data Mining and Knowledge Discovery

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Abstract

This book constitutes the thoroughly refereed post-conference proceedings of the First COST Action IC1302 International KEYSTONE Conference on semantic Keyword-based Search on Structured Data Sources, IKC 2015, held in Coimbra, Portugal, in September 2015. The 13 revised full papers, 3 revised short papers, and 2 invited papers were carefully reviewed and selected from 22 initial submissions. The paper topics cover techniques for keyword search, semantic data management, social Web and social media, information retrieval, benchmarking for search on big data.

Content

Inhalt: Professional Collaborative Information Seeking: On Traceability and Creative Sensemaking / Nürnberger, Andreas (et al.) - Recommending Web Pages Using Item-Based Collaborative Filtering Approaches / Cadegnani, Sara (et al.) - Processing Keyword Queries Under Access Limitations / Calì, Andrea (et al.) - Balanced Large Scale Knowledge Matching Using LSH Forest / Cochez, Michael (et al.) - Improving css-KNN Classification Performance by Shifts in Training Data / Draszawka, Karol (et al.) - Classification Using Various Machine Learning Methods and Combinations of Key-Phrases and Visual Features / HaCohen-Kerner, Yaakov (et al.) - Mining Workflow Repositories for Improving Fragments Reuse / Harmassi, Mariem (et al.) - AgileDBLP: A Search-Based Mobile Application for Structured Digital Libraries / Ifrim, Claudia (et al.) - Support of Part-Whole Relations in Query Answering / Kozikowski, Piotr (et al.) - Key-Phrases as Means to Estimate Birth and Death Years of Jewish Text Authors / Mughaz, Dror (et al.) - Visualization of Uncertainty in Tag Clouds / Platis, Nikos (et al.) - Multimodal Image Retrieval Based on Keywords and Low-Level Image Features / Pobar, Miran (et al.) - Toward Optimized Multimodal Concept Indexing / Rekabsaz, Navid (et al.) - Semantic URL Analytics to Support Efficient Annotation of Large Scale Web Archives / Souza, Tarcisio (et al.) - Indexing of Textual Databases Based on Lexical Resources: A Case Study for Serbian / Stankovic, Ranka (et al.) - Domain-Specific Modeling: Towards a Food and Drink Gazetteer / Tagarev, Andrey (et al.) - Analysing Entity Context in Multilingual Wikipedia to Support Entity-Centric Retrieval Applications / Zhou, Yiwei (et al.)

Date

1. 2.2016 18:25:22

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LCSH

Data mining

Subject

Data mining

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LCSH

Data mining

Subject

Data mining

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Content

Neumaier, Sebastian (et al.): Data Integration for Open Data on the Web - Stamou, Giorgos (et al.): Ontological Query Answering over Semantic Data - Calì, Andrea: Ontology Querying: Datalog Strikes Back - Sequeda, Juan F.: Integrating Relational Databases with the Semantic Web: A Reflection - Rousset, Marie-Christine (et al.): Datalog Revisited for Reasoning in Linked Data - Kaminski, Roland (et al.): A Tutorial on Hybrid Answer Set Solving with clingo - Eiter, Thomas (et al.): Answer Set Programming with External Source Access - Lukasiewicz, Thomas: Uncertainty Reasoning for the Semantic Web - Calvanese, Diego (et al.): OBDA for Log Extraction in Process Mining

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Abstract

Dieses Buch präsentiert ein breites Spektrum aktueller Methoden zur Repräsentation und Verarbeitung (un)sicheren Wissens in maschinellen Systemen in didaktisch aufbereiteter Form. Neben symbolischen Ansätzen des nichtmonotonen Schließens (Default-Logik, hier konstruktiv und leicht verständlich mittels sog. Default-Bäume realisiert) werden auch ausführlich quantitative Methoden wie z.B. probabilistische Markov- und Bayes-Netze vorgestellt. Weitere Abschnitte beschäftigen sich mit Wissensdynamik (Truth Maintenance-Systeme), Aktionen und Planen, maschinellem Lernen, Data Mining und fallbasiertem Schließen.In einem vertieften Querschnitt werden zentrale alternative Ansätze einer logikbasierten Wissensmodellierung ausführlich behandelt. Detailliert beschriebene Algorithmen geben dem Praktiker nützliche Hinweise zur Anwendung der vorgestellten Ansätze an die Hand, während fundiertes Hintergrundwissen ein tieferes Verständnis für die Besonderheiten der einzelnen Methoden vermittelt . Mit einer weitgehend vollständigen Darstellung des Stoffes und zahlreichen, in den Text integrierten Aufgaben ist das Buch für ein Selbststudium konzipiert, eignet sich aber gleichermaßen für eine entsprechende Vorlesung. Im Online-Service zu diesem Buch werden u.a. ausführliche Lösungshinweise zu allen Aufgaben des Buches angeboten.Zahlreiche Beispiele mit medizinischem, biologischem, wirtschaftlichem und technischem Hintergrund illustrieren konkrete Anwendungsszenarien. Von namhaften Professoren empfohlen: State-of-the-Art bietet das Buch zu diesem klassischen Bereich der Informatik. Die wesentlichen Methoden wissensbasierter Systeme werden verständlich und anschaulich dargestellt. Repräsentation und Verarbeitung sicheren und unsicheren Wissens in maschinellen Systemen stehen dabei im Mittelpunkt. In der vierten, verbesserten Auflage wurde die Anzahl der motivierenden Selbsttestaufgaben mit aktuellem Praxisbezug nochmals erweitert. Ein Online-Service mit ausführlichen Musterlösungen erleichtert das Lernen.

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Date

1. 2.2016 18:25:22