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Conceptual Structures: Knowledge Architectures for Smart Applications

Overview of attention for book
Cover of 'Conceptual Structures: Knowledge Architectures for Smart Applications'

Table of Contents

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    Book Overview
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    Chapter 1 An Introduction to Conceptual Graphs
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    Chapter 2 Trikonic Inter-Enterprise Architectonic
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    Chapter 3 Hypermedia Discourse: Contesting Networks of Ideas and Arguments
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    Chapter 4 Dynamic Epistemic Logic and Knowledge Puzzles
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    Chapter 5 Peirce on Icons and Cognition
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    Chapter 6 Using Cognitive Archetypes and Conceptual Graphs to Model Dynamic Phenomena in Spatial Environments
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    Chapter 7 A Datatype Extension for Simple Conceptual Graphs and Conceptual Graphs Rules
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    Chapter 8 A Knowledge Management Optimization Problem Using Marginal Utility in a Metric Space with Conceptual Graphs
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    Chapter 9 Conceptual Graphs as Cooperative Formalism to Build and Validate a Domain Expertise
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    Chapter 10 An Inferential Approach to the Generation of Referring Expressions
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    Chapter 11 A Conceptual Graph Description of Medical Data for Brain Tumour Classification
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    Chapter 12 A Conceptual Graph Based Approach to Ontology Similarity Measure
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    Chapter 13 A Comparison of Different Conceptual Structures Projection Algorithms
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    Chapter 14 A Conceptual Graph Approach to Feature Modeling
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    Chapter 15 From Conceptual Structures to Semantic Interoperability of Content
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    Chapter 16 Faster Concept Analysis
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    Chapter 17 The Design Space of Information Presentation: Formal Design Space Analysis with FCA and Semiotics
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    Chapter 18 Reducing the Representation Complexity of Lattice-Based Taxonomies
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    Chapter 19 An FCA Perspective on n -Distributivity
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    Chapter 20 Towards a Semantology of Music
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    Chapter 21 Analysis of the Publication Sharing Behaviour in BibSonomy
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    Chapter 22 The MILL – Method for Informal Learning Logistics
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    Chapter 23 Bilingual Word Association Networks
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    Chapter 24 Using FCA for Encoding Closure Operators into Neural Networks
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    Chapter 25 Arc Consistency Projection: A New Generalization Relation for Graphs
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    Chapter 26 Mining Frequent Closed Unordered Trees Through Natural Representations
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    Chapter 27 Conceptual Structures: Knowledge Architectures for Smart Applications
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    Chapter 28 Historical and Conceptual Foundation of Diagrammatical Ontology
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    Chapter 29 Learning Common Outcomes of Communicative Actions Represented by Labeled Graphs
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    Chapter 30 Belief Flow in Assertion Networks
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    Chapter 31 Conceptual Fingerprints: Lexical Decomposition by Means of Frames – a Neuro-cognitive Model
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    Chapter 32 Constants and Functions in Peirce’s Existential Graphs
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    Chapter 33 Revelator Game of Inquiry: A Peircean Challenge for Conceptual Structures in Application and Evolution
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    Chapter 34 Helping System Users to Be Smarter by Representing Logic in Transaction Frame Diagrams
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    Chapter 35 Quo Vadis, CS? – On the (non)-Impact of Conceptual Structures on the Semantic Web
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    Chapter 36 A Framework for Analyzing and Testing Overlapping Requirements with Actors in Conceptual Graphs
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    Chapter 37 Implementation of SPARQL Query Language Based on Graph Homomorphism
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    Chapter 38 Cooperative CG-Wrappers for Web Content Extraction
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    Chapter 39 Conceptual Graphs and Ontologies for Information Retrieval
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    Chapter 40 Representation Levels Within Knowledge Representation
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    Chapter 41 Supporting Lexical Ontology Learning by Relational Exploration
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    Chapter 42 Characterizing Implications of Injective Partial Orders
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    Chapter 43 DVDSleuth: A Case Study in Applied Formal Concept Analysis for Navigating Web Catalogs
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    Chapter 44 Navigation in Knowledge-Based System for Helpdesk Based on FCA
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    Chapter 45 Functorial Properties of Formal Concept Analysis
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    Chapter 46 Towards an Ontology to Conceptualize Solution Analysis Tasks in CSCL Environments
Overall attention for this book and its chapters
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About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
25 Mendeley
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Title
Conceptual Structures: Knowledge Architectures for Smart Applications
Published by
Lecture notes in computer science, January 2007
DOI 10.1007/978-3-540-73681-3
ISBNs
978-3-54-073680-6, 978-3-54-073681-3
Authors

Uta Priss, Simon Polovina, Richard Hill

Editors

Priss, Uta, Polovina, Simon, Hill, Richard

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 8%
Portugal 1 4%
Switzerland 1 4%
India 1 4%
United Kingdom 1 4%
Spain 1 4%
Unknown 18 72%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 20%
Researcher 5 20%
Student > Ph. D. Student 5 20%
Student > Doctoral Student 3 12%
Lecturer 2 8%
Other 4 16%
Unknown 1 4%
Readers by discipline Count As %
Computer Science 18 72%
Mathematics 2 8%
Business, Management and Accounting 1 4%
Psychology 1 4%
Social Sciences 1 4%
Other 1 4%
Unknown 1 4%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 January 2015.
All research outputs
#7,486,475
of 22,884,315 outputs
Outputs from Lecture notes in computer science
#2,487
of 8,127 outputs
Outputs of similar age
#42,271
of 157,086 outputs
Outputs of similar age from Lecture notes in computer science
#31
of 59 outputs
Altmetric has tracked 22,884,315 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,127 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 54% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 157,086 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.