Improving Visual Search Results

Authors

  • Reddy S Computer Science, Indian Institute of Information Technology, Bhagalpur, India

DOI:

https://doi.org/10.26438/ijcse/v6i12.169171

Keywords:

Python Script using Beautiful Soup, a python library for pulling data out of HTML and XML files

Abstract

This paper introduces a new method to improve visual search results and understand structured data. While many online resources teach basics of web development, few of them are designed to help novices learn the web development concepts and design patterns used by experts to implement complex visual features. Professional web pages embed these design patterns and could serve as rich learning materials, but their metadata are complex and difficult for novices to understand. This paper presents Metadata.py*, a Metadata inspection tool that helps novices use their visual intuition to make sense of professional web pages/sites. We introduce a new visual relevance testing technique to identify properties that have visual search results, which Metadata.py uses to hide visually irrelevant code and surface unintuitive relationships between properties. In user studies, Metadata.py helped novice developers replicate complex web features 75% faster than those using Chrome Developer Tool and allowed novices to recognize and explain unfamiliar concepts. These results show that visual inspection tools can support learning from complex professional web pages, even for novice developers. Metadata,py: Python Script using Beautiful Soup, a python library for pulling data out of HTML and XML files.

References

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[7] Heery, Rachel and Manjula Patel, Application Profiles: Mixing and Matching Metadata Schemas, Ariadne, Issue 25 (September 2000)

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Published

2018-12-31
CITATION
DOI: 10.26438/ijcse/v6i12.169171
Published: 2018-12-31

How to Cite

[1]
S. Reddy, “Improving Visual Search Results”, Int. J. Comp. Sci. Eng., vol. 6, no. 12, pp. 169–171, Dec. 2018.

Issue

Section

Research Article