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Knowledge Engineering and Management by the Masses

Overview of attention for book
Cover of 'Knowledge Engineering and Management by the Masses'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Pattern-Based Mapping Refinement
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    Chapter 2 Practical Considerations on Identity for Instance Management in Ontological Investigation
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    Chapter 3 Involving Business Users in Formal Modeling Using Natural Language Pattern Sentences
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    Chapter 4 Knowledge Acquisition from Sources of Law in Public Administration
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    Chapter 5 Knowledge Engineering and Management by the Masses
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    Chapter 6 Ontology Development for the Masses: Creating ICD-11 in WebProtégé
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    Chapter 7 RDFauthor: Employing RDFa for Collaborative Knowledge Engineering
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    Chapter 8 Pattern-Based Ontology Transformation Service Exploiting OPPL and OWL-API
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    Chapter 9 Experimenting with eXtreme Design
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    Chapter 10 Weaving a Social Data Web with Semantic Pingback
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    Chapter 11 Social People-Tagging vs. Social Bookmark-Tagging
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    Chapter 12 FOLCOM or the Costs of Tagging
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    Chapter 13 Epiphany: Adaptable RDFa Generation Linking the Web of Documents to the Web of Data
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    Chapter 14 Knowledge Engineering and Management by the Masses
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    Chapter 15 Using Semantic Web Resources for Data Quality Management
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    Chapter 16 Using Ontological Contexts to Assess the Relevance of Statements in Ontology Evolution
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    Chapter 17 What Is Concept Drift and How to Measure It?
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    Chapter 18 Mobile Cultural Heritage Guide: Location-Aware Semantic Search
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    Chapter 19 Semantic Scout: Making Sense of Organizational Knowledge
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    Chapter 20 Authoring Technical Documents for Effective Retrieval
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    Chapter 21 Knowledge Engineering and Management by the Masses
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    Chapter 22 Towards Better Ontological Support for Recognizing Textual Entailment
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    Chapter 23 Making Sense of Design Patterns
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    Chapter 24 Acquiring and Modelling Legal Knowledge Using Patterns: An Application for the Dutch Immigration and Naturalisation Service
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    Chapter 25 A Model-Driven Approach for Using Templates in OWL Ontologies
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    Chapter 26 Specialization and Validation of Statecharts in OWL
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    Chapter 27 Temporal Knowledge Acquisition and Modeling
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    Chapter 28 Using Machine Learning to Support Continuous Ontology Development
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    Chapter 29 Handling Markup Overlaps Using OWL
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    Chapter 30 Ontology Learning for Cost-Effective Large-Scale Semantic Annotation of Web Service Interfaces
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    Chapter 31 Towards Hybrid Reasoning for Verifying and Validating Multilevel Models
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    Chapter 32 Representing, Proving and Sharing Trustworthiness of Web Resources Using Veracity
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    Chapter 33 Enhancing Content-Based Recommendation with the Task Model of Classification
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    Chapter 34 Extending Open Rating Systems for Ontology Ranking and Reuse
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    Chapter 35 HyperTwitter: Collaborative Knowledge Engineering via Twitter Messages
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    Chapter 36 TagSorting: A Tagging Environment for Collaboratively Building Ontologies
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    Chapter 37 QuiKey – An Efficient Semantic Command Line
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    Chapter 38 Kali-ma: A Semantic Guide to Browsing and Accessing Functionalities in Plugin-Based Tools
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    Chapter 39 Constructing Understandable Explanations for Semantic Search Results
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    Chapter 40 Ontology Engineering with Rough Concepts and Instances
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    Chapter 41 Building Large Lexicalized Ontologies from Text: A Use Case in Automatic Indexing of Biotechnology Patents
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    Chapter 42 ReBEC: A Method for Capturing Experience during Software Development Projects
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    Chapter 43 Knowledge Engineering and Management by the Masses
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    Chapter 44 Evaluations of User-Driven Ontology Summarization
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    Chapter 45 A Visualization Service for the Semantic Web
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    Chapter 46 How Much Semantic Data on Small Devices?
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    Chapter 47 A Semantic Approach for Learning Objects Repositories with Knowledge Reuse
Overall attention for this book and its chapters
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
37 Mendeley
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Title
Knowledge Engineering and Management by the Masses
Published by
Lecture notes in computer science, January 2010
DOI 10.1007/978-3-642-16438-5
ISBNs
978-3-64-216437-8, 978-3-64-216438-5
Authors

Philipp Cimiano, H. Sofia Pinto

Editors

Cimiano, Philipp, Pinto, H. Sofia

Twitter Demographics

The data shown below were collected from the profiles of 8 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 3%
Student > Master 1 3%
Unknown 35 95%
Readers by discipline Count As %
Computer Science 3 8%
Linguistics 1 3%
Social Sciences 1 3%
Unknown 32 86%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 05 October 2020.
All research outputs
#9,757,007
of 17,362,547 outputs
Outputs from Lecture notes in computer science
#3,607
of 7,687 outputs
Outputs of similar age
#147,578
of 350,575 outputs
Outputs of similar age from Lecture notes in computer science
#517
of 1,140 outputs
Altmetric has tracked 17,362,547 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,687 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 51% 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 350,575 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 1,140 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.