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Conceptual Structures for Discovering Knowledge

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Cover of 'Conceptual Structures for Discovering Knowledge'

Table of Contents

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    Book Overview
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    Chapter 1 Semantic Technologies for Enterprises
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    Chapter 2 Utility and Feasibility of Reasoning beyond Decidability in Semantic Technologies
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    Chapter 3 Cognitive Architectures for Conceptual Structures
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    Chapter 4 In-Close2, a High Performance Formal Concept Miner
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    Chapter 5 A Mapping from Conceptual Graphs to Formal Concept Analysis
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    Chapter 6 Partial Orders and Logical Concept Analysis to Explore Patterns Extracted by Data Mining
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    Chapter 7 A Buzz and E-Reputation Monitoring Tool for Twitter Based on Galois Lattices
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    Chapter 8 Using Generalization of Syntactic Parse Trees for Taxonomy Capture on the Web
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    Chapter 9 A.N. Prior’s Ideas on Tensed Ontology
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    Chapter 10 Crowdsourced Knowledge: Peril and Promise for Conceptual Structures Research
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    Chapter 11 Evaluating the Transaction Graph through a Financial Trading Case Study
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    Chapter 12 Integration of the Controlled Language ACE to the Amine Platform
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    Chapter 13 Identifying Relations between Medical Concepts by Parsing UMLS ®  Definitions
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    Chapter 14 Topicality in Logic-Based Ontologies
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    Chapter 15 A Concept Discovery Approach for Fighting Human Trafficking and Forced Prostitution
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    Chapter 16 A Modeling Method and Declarative Language for Temporal Reasoning Based on Fluid Qualities
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    Chapter 17 Expressing Conceptual Graph Queries from Patterns: How to Take into Account the Relations
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    Chapter 18 Unix Systems Monitoring with FCA
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    Chapter 19 Supporting Ontology Design through Large-Scale FCA-Based Ontology Restructuring
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    Chapter 20 Towards a Formalization of Individual Work Execution at Computer Workplaces
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    Chapter 21 Semi-supervised Learning for Mixed-Type Data via Formal Concept Analysis
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    Chapter 22 Towards Structuring Episodes in Patient History
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    Chapter 23 Rigorous, and Informal?
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    Chapter 24 OpenSEA – Using Common Logic to Provide a Semantic Enterprise Architecture Framework
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    Chapter 25 An Android Based Medication Reminder System: A Concept Analysis Approach
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    Chapter 26 System Decomposition for Temporal Concept Analysis
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    Chapter 27 Modeling UAS Swarm System Using Conceptual and Dynamic Architectural Modeling Concepts
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    Chapter 28 Name Extraction and Formal Concept Analysis
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    Chapter 29 Towards the Development of an Integrated Framework for Enhancing Enterprise Search Using Latent Semantic Indexing
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    Chapter 30 Trace of Objects to Retrieve Prediction Patterns of Activities in Smart Homes
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    Chapter 31 Distributed Context Aware Collaborative Filtering Approach for Service Selection in Wireless Mesh Networks
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    Chapter 32 A Framework for the Evaluation of Adaptive IR Systems through Implicit Recommendation
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    Chapter 33 MedMatch – Towards Domain Specific Semantic Matching
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    Chapter 34 Application Identification of Semantic Web Techniques in KM Systems
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    Chapter 35 Aligning the Teaching of FCA with Existing Module Learning Outcomes
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    Chapter 36 A Proposal for Developing a Primer for Constructing and Analyzing Conceptual Structures
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    Chapter 37 Internationalising the Computing Curricula: A Peircian Approach
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    Chapter 38 Broadening the Ontological Perspectives in Science Learning: Implications for Research and Practice in Science Teaching
Overall attention for this book and its chapters
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2 X users
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6 patents
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2 Wikipedia pages

Citations

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Title
Conceptual Structures for Discovering Knowledge
Published by
Springer, Berlin, Heidelberg, January 2011
DOI 10.1007/978-3-642-22688-5
ISBNs
978-3-64-222687-8, 978-3-64-222688-5
Editors

Simon Andrews, Simon Polovina, Richard Hill, Babak Akhgar

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 233 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 <1%
Russia 1 <1%
Unknown 231 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 2%
Researcher 3 1%
Student > Bachelor 2 <1%
Student > Ph. D. Student 2 <1%
Student > Doctoral Student 2 <1%
Other 0 0%
Unknown 219 94%
Readers by discipline Count As %
Computer Science 9 4%
Mathematics 1 <1%
Business, Management and Accounting 1 <1%
Economics, Econometrics and Finance 1 <1%
Psychology 1 <1%
Other 1 <1%
Unknown 219 94%