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Graph-Based Representation and Reasoning

Overview of attention for book
Cover of 'Graph-Based Representation and Reasoning'

Table of Contents

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    Book Overview
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    Chapter 1 Visualization, Reasoning, and Rationality
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    Chapter 2 Introducing Contextual Reasoning to the Semantic Web with OWL $$^{C}$$
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    Chapter 3 Graph-Based Variability Modelling: Towards a Classification of Existing Formalisms
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    Chapter 4 Publishing Uncertainty on the Semantic Web: Blurring the LOD Bubbles
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    Chapter 5 Formal Context Generation Using Dirichlet Distributions
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    Chapter 6 Lifted Temporal Most Probable Explanation
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    Chapter 7 Temporal Relations Between Imprecise Time Intervals: Representation and Reasoning
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    Chapter 8 Relevant Attributes in Formal Contexts
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    Chapter 9 Adaptive Collaborative Filtering for Recommender System
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    Chapter 10 Exploring and Conceptualising Attestation
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    Chapter 11 Enhancing Layered Enterprise Architecture Development Through Conceptual Structures
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    Chapter 12 Ontology-Informed Lattice Reduction Using the Discrimination Power Index
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    Chapter 13 Redescription Mining for Learning Definitions and Disjointness Axioms in Linked Open Data
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    Chapter 14 Covering Concept Lattices with Concept Chains
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    Chapter 15 The Compositional Rule of Inference Under the Composition Max-Product
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    Chapter 16 Mining Social Networks from Linked Open Data
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    Chapter 17 Information Retrieval Chatbots Based on Conceptual Models
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    Chapter 18 Semiotic-Conceptual Analysis of a Lexical Field
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    Chapter 19 Applying Semiotic-Conceptual Analysis to Mathematical Language
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    Chapter 20 Mathematical Similarity Models: Do We Need Incomparability to Be Precise?
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    Chapter 21 Conceptual Graphs Based Modeling of MongoDB Data Structure and Query
Overall attention for this book and its chapters
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6 tweeters

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Title
Graph-Based Representation and Reasoning
Published by
Springer International Publishing, January 2019
DOI 10.1007/978-3-030-23182-8
ISBNs
978-3-03-023181-1, 978-3-03-023182-8
Editors

Dominik Endres, Mehwish Alam, Diana Şotropa

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 67%
Student > Bachelor 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Engineering 1 33%