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A Physical Embedding Model for Knowledge Graphs

Overview of attention for book
Cover of 'A Physical Embedding Model for Knowledge Graphs'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Building a Large-Scale Knowledge Graph for Elementary Education in China
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    Chapter 2 A Temporal Semantic Search System for Traditional Chinese Medicine Based on Temporal Knowledge Graphs
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    Chapter 3 Testing of Various Approaches for Semiautomatic Parish Records Word Standardization
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    Chapter 4 Concept Similarity Under the Agent’s Preferences for the Description Logic $$\mathcal {A}\!\mathcal {L}\!\mathcal {E}\!\mathcal {H}$$ A L E H
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    Chapter 5 Data Quality for Deep Learning of Judgment Documents: An Empirical Study
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    Chapter 6 Aligning Sentences Between Comparable Texts of Different Styles
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    Chapter 7 An In-depth Analysis of Graph Neural Networks for Semi-supervised Learning
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    Chapter 8 XTransE: Explainable Knowledge Graph Embedding for Link Prediction with Lifestyles in e-Commerce
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    Chapter 9 Feasibility Study: Rule Generation for Ontology-Based Decision-Making Systems
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    Chapter 10 Attention-Based Direct Interaction Model for Knowledge Graph Embedding
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    Chapter 11 Discovering Hypernymy Relationships in Chinese Traffic Legal Texts
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    Chapter 12 Multi-task Learning for Attribute Extraction from Unstructured Electronic Medical Records
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    Chapter 13 Uncertain Ontology-Aware Knowledge Graph Embeddings
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    Chapter 14 Investigating Schema Definitions Using RDFS and OWL 2 for RDF Databases in Life Sciences
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    Chapter 15 RQE: Rule-Driven Query Expansion to Solve Empty Answers in SPARQL
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    Chapter 16 Aspect-Level Sentiment Analysis of Online Product Reviews Based on Multi-features
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    Chapter 17 A Seq2seq-Based Approach to Question Answering over Knowledge Bases
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    Chapter 18 Building Knowledge Graph Across Different Subdomains Using Interlinking Ontology for Biomedical Concepts
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    Chapter 19 WPQA: A Gaming Support System Based on Machine Learning and Knowledge Graph
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    Chapter 20 Combining Concept Graph with Improved Neural Networks for Chinese Short Text Classification
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    Chapter 21 Construction of Chinese Pediatric Medical Knowledge Graph
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    Chapter 22 EasyKG: An End-to-End Knowledge Graph Construction System
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

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Article details
Title
A Physical Embedding Model for Knowledge Graphs
Published by
arXiv, January 2020
DOI 10.1007/978-981-15-3412-6
ISBNs
978-9-81-153411-9, 978-9-81-153412-6
Authors

Caglar Demir, Axel-Cyrille Ngonga Ngomo

Editors

Wang, Xin, Lisi, Francesca A., Xiao, Guohui, Botoeva, Elena

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 09 November 2022.
All research outputs
#5,650,230
of 33,834,589 outputs
Outputs from arXiv
#109,857
of 1,482,665 outputs
Outputs of similar age
#96,210
of 524,765 outputs
Outputs of similar age from arXiv
#2,054
of 19,628 outputs
Altmetric has tracked 33,834,589 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,482,665 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 92% 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 524,765 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 19,628 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.