Modern Approaches for Intelligent Information and Database Systems

This book offers a unique blend of reports on both theoretical models and their applications in the area of Intelligent Information and Database Systems. The reports cover a broad range of research topics, including advanced learning techniques, knowledge engineering, Natural Language Processing (NL...

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Bibliographic Details
Other Authors: Sieminski, Andrzej (Editor), Kozierkiewicz, Adrianna (Editor), Nunez, Manuel (Editor), Ha, Quang Thuy (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2018, 2018
Edition:1st ed. 2018
Series:Studies in Computational Intelligence
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • Part I: Knowledge Engineering and Semantic Web
  • A Three-stage Consensus-based Method for Collective Knowledge Determination
  • Fuzzy Ontology Modeling by Utilizing Fuzzy Set and Fuzzy Description Logic
  • An Approach for Recommending Group Experts on Question and Answering Sites
  • A method for uncertainty elicitation of experts using belief function
  • Storing Hypergraph-based Data Models in Non-hypergraph Data Storage
  • Part II: Natural Language Processing and Text Mining
  • A Fuzzy Logic Approach to Predict the Popularity of a Presidential Candidate
  • DNA Sequences Representation Derived from Discrete Wavelet Transformation for Text Similarity Recognition
  • Tweet integration by finding the shortest paths on a word graph
  • Event detection in Twitter: Methodological Evaluation and Structural Analysis of the Bibliometric Data
  • Combination of inner approach and context-based approach for extracting feature of medical record data
  • A Novel Method to Predict Type for DBpedia Entity
  • Context-Based Personalized Predictors of the Length of Written Responses to Open-ended Questions of Elementary School Students
  • Part III: Machine Learning and Data Mining
  • Robust Scale-Invariant Normalization and Similarity Measurement for Time Series Data