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  1. Proceedings of the International Workshop on Semantic Big Data (SBD '16)
  2. Scalable algorithms for scholarly figure mining and semantics
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Scalable algorithms for scholarly figure mining and semantics
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Scalable algorithms for scholarly figure mining and semantics

Content Provider ACM Digital Library
Author Wang, Shuting Choudhury, Sagnik Ray Giles, C. Lee.
Abstract Most scholarly papers contain one or multiple figures. Often these figures show experimental results, e.g, line graphs are used to compare various methods. Compared to the text of the paper, figures and their semantics have received relatively less attention. This has significantly limited semantic search capabilities in scholarly search engines. Here, we report scalable algorithms for generating semantic metadata for figures. Our system has four sequential modules: 1. Extraction of figure, caption and mention; 2. Binary classification of figures as compound (contains sub-figures) or not; 3. Three class classification of non compound figures as line graph, bar graph or others; and 4. Automatic processing of line graphs to generate a textual summary. In each step a metadata file is generated, each having richer information than the previous one. The algorithms are scalable yet each individual step has an accuracy greater than 80%.
Starting Page 1
Ending Page 6
Page Count 6
File Format PDF
ISBN 9781450342995
DOI 10.1145/2928294.2928305
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-06-26
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Vector graphics Figure semantics
Content Type Text
Resource Type Article
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