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Semantic Web Challenges

Overview of attention for book
Cover of 'Semantic Web Challenges'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 The Second Open Knowledge Extraction Challenge
  3. Altmetric Badge
    Chapter 2 Enhancing Entity Linking by Combining NER Models
  4. Altmetric Badge
    Chapter 3 Collective Disambiguation and Semantic Annotation for Entity Linking and Typing
  5. Altmetric Badge
    Chapter 4 DWS at the 2016 Open Knowledge Extraction Challenge: A Hearst-Like Pattern-Based Approach to Hypernym Extraction and Class Induction
  6. Altmetric Badge
    Chapter 5 Entity Typing and Linking Using SPARQL Patterns and DBpedia
  7. Altmetric Badge
    Chapter 6 Challenge on Fine-Grained Sentiment Analysis Within ESWC2016
  8. Altmetric Badge
    Chapter 7 App2Check Extension for Sentiment Analysis of Amazon Products Reviews
  9. Altmetric Badge
    Chapter 8 Sentiment Polarity Detection from Amazon Reviews: An Experimental Study
  10. Altmetric Badge
    Chapter 9 Exploiting Propositions for Opinion Mining
  11. Altmetric Badge
    Chapter 10 The IRMUDOSA System at ESWC-2016 Challenge on Semantic Sentiment Analysis
  12. Altmetric Badge
    Chapter 11 A Knowledge-Based Approach for Aspect-Based Opinion Mining
  13. Altmetric Badge
    Chapter 12 Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
  14. Altmetric Badge
    Chapter 13 6th Open Challenge on Question Answering over Linked Data (QALD-6)
  15. Altmetric Badge
    Chapter 14 SPARKLIS on QALD-6 Statistical Questions
  16. Altmetric Badge
    Chapter 15 Top-K Shortest Paths in Large Typed RDF Datasets Challenge
  17. Altmetric Badge
    Chapter 16 Top-k Shortest Paths in Directed Labeled Multigraphs
  18. Altmetric Badge
    Chapter 17 Modified MinG Algorithm to Find Top-K Shortest Paths from large RDF Graphs
  19. Altmetric Badge
    Chapter 18 Using Triple Pattern Fragments to Enable Streaming of Top-k Shortest Paths via the Web
  20. Altmetric Badge
    Chapter 19 Semantic Publishing Challenge – Assessing the Quality of Scientific Output in Its Ecosystem
  21. Altmetric Badge
    Chapter 20 Reconstructing the Logical Structure of a Scientific Publication Using Machine Learning
  22. Altmetric Badge
    Chapter 21 Automatically Identify and Label Sections in Scientific Journals Using Conditional Random Fields
  23. Altmetric Badge
    Chapter 22 ACM: Article Content Miner for Assessing the Quality of Scientific Output
  24. Altmetric Badge
    Chapter 23 Information Extraction from PDF Sources Based on Rule-Based System Using Integrated Formats
  25. Altmetric Badge
    Chapter 24 An Automatic Workflow for the Formalization of Scholarly Articles’ Structural and Semantic Elements
Attention for Chapter 12: Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
7 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
51 Mendeley
Summary Twitter Dimensions citations
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Chapter title
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
Chapter number 12
Book title
Semantic Web Challenges
Published in
arXiv, May 2016
DOI 10.1007/978-3-319-46565-4_12
Book ISBNs
978-3-31-946564-7, 978-3-31-946565-4
Authors

Soufian Jebbara, Philipp Cimiano

View on publisher site

Twitter Demographics

The data shown below were collected from the profiles of 7 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Unknown 50 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 29%
Student > Ph. D. Student 8 16%
Student > Doctoral Student 4 8%
Student > Bachelor 4 8%
Researcher 4 8%
Other 6 12%
Unknown 10 20%
Readers by discipline Count As %
Computer Science 29 57%
Engineering 6 12%
Linguistics 2 4%
Social Sciences 2 4%
Economics, Econometrics and Finance 1 2%
Other 0 0%
Unknown 11 22%

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 20 September 2017.
All research outputs
#13,332,723
of 22,999,744 outputs
Outputs from arXiv
#211,908
of 944,084 outputs
Outputs of similar age
#172,378
of 339,114 outputs
Outputs of similar age from arXiv
#2,919
of 15,585 outputs
Altmetric has tracked 22,999,744 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 944,084 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 75% 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 339,114 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15,585 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.

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