Computational collective intelligence : 12th international conference, ICCCI 2020 Da Nang, Vietnam, November 30-December 3, 2020 proceedings

Computational collective intelligence : 12th international conference, ICCCI 2020 Da Nang, Vietnam, November 30-December 3, 2020 proceedings / Ngoc Thanh Nguyen, Bao Hung Hoang, Cong Phap Huynh, Dosam Hwang, Bogdan Trawiński, Gottfried Vossen (eds.).

This volume constitutes the refereed proceedings of the 12th International Conference on Computational Collective Intelligence, ICCCI 2020, held in Da Nang, Vietnam, in November 2020.* The 70 full papers presented were carefully reviewed and selected from 314 submissions. The papers are grouped in t...

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Bibliographic Details
Corporate Author: ICCCI (Conference) Online)
Other Authors: Nguyen, Ngoc Thanh (Computer scientist), Hoang, Bao Hung, Huynh, Cong Phap, Hwang, Dosam, Trawiński, Bogdan, Vossen, Gottfried
Format: eBook
Language:English
Published: Cham, Switzerland : Springer, 2020.
Series:Lecture notes in computer science ; 12496.
Lecture notes in computer science. Lecture notes in artificial intelligence.
LNCS sublibrary. Artificial intelligence.
Subjects:
Expert systems (Computer science) > Congresses.
Intelligent agents (Computer software) > Congresses.
Artificial intelligence > Congresses.
Semantic Web > Congresses.
Human-computer interaction > Congresses.
Semantic Web
Intelligent agents (Computer software)
Human-computer interaction
Expert systems (Computer science)
Application software
Artificial intelligence
Computer networks
Computers
Optical data processing
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Online Access:Click for online access
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • Knowledge Engineering and Semantic Web
  • Towards a Holistic Schema Matching Approach Designed for Large-Scale Schemas
  • 1 Introduction
  • 2 Related Work
  • 3 Problem Statement
  • 4 The HMO Approach
  • 4.1 Overlaps and Distinctions Generator
  • 4.2 Schema Overlaps Matcher
  • 4.3 Distinct Elements Matcher
  • 5 Experimental Results
  • 5.1 Experimental Settings
  • 5.2 Results and Discussion
  • 6 Conclusion and Future Work
  • References
  • Overcoming Local Optima for Determining 2-Optimality Consensus for Collectives
  • 1 Introduction
  • 2 Related Work
  • 3 Basic Notions
  • 4 Proposed Approach
  • 5 Experiments and Evaluation
  • 5.1 Consensus Quality
  • 5.2 Running Time
  • 6 Conclusions
  • References
  • Fundamentals of Generalized and Extended Graph-Based Structural Modeling
  • 1 Introduction
  • 2 Formal Definitions
  • 3 Extended Graph Generalization Abstract Syntax
  • 4 Extended Graph Generalization Features
  • 5 Extended Graph Generalization Semantics
  • 6 Extended Graph Generalization Concrete Syntax
  • 6.1 Extended Graph Generalization Diagram Example
  • 7 A Social Network Model Example Expressed in the EGG Categories
  • 8 Conclusions
  • References
  • Social Networks and Recommender Systems
  • Topic Diffusion Prediction on Bibliographic Network: New Approach with Combination Between External and Intrinsic Factors
  • 1 Introduction
  • 2 Preliminaries
  • 2.1 Meta-Path
  • 3 Proposed Approach
  • 3.1 Supervised Learning for Topic Diffusion on Bibliographic Network
  • 3.2 Activation Probability Feature
  • 3.3 Author's Interest Feature
  • 4 Experiments and Results
  • 4.1 Experiments
  • 4.2 Results
  • 5 Conclusion and Future Work
  • References
  • Detecting the Degree of Risk in Online Market Based on Satisfaction of Twitter Users
  • 1 Introduction
  • 2 Related Works
  • 3 Research Problem
  • 3.1 Definition of Tweet and Types of Tweet Sentiments
  • 3.2 Definition of Different Types of User Satisfaction
  • 3.3 Definition of Different Degrees of Risk
  • 3.4 Research Question
  • 4 Proposed Method
  • 4.1 Feature Extraction
  • 4.2 Tweet Sentiment Analysis
  • 4.3 Determining the User Satisfaction, Dissatisfaction, and Hesitation
  • 4.4 Calculating the Degree of Risk
  • 5 Experiment
  • 5.1 Data Acquisition
  • 5.2 Evaluation Results
  • 5.3 Results and Discussion
  • 6 Conclusion and Future Work
  • References
  • Method of Detecting Bots on Social Media. A Literature Review
  • 1 Introduction and Background
  • 2 Type of Bots
  • 3 Method of Detecting Social Bots
  • 3.1 Social Media Giant Model
  • 3.2 Experts-Approach
  • 3.3 Collective Intelligence-Approach
  • 3.4 Machine Learning Model
  • 3.5 Hybrid Method
  • 3.6 Graphical Modeling to Detect Bots
  • 3.7 Behavioral Based-Model
  • 4 Discussion and Open Challenges
  • 5 Conclusions and Future Directions
  • References

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