CSCI8380 (Spring 2012):
Paper Review Form
Raga Sowmya Tummalapenta
Enrichment and Ranking of the YouTube Tag Space
and Integration with the Linked Data Cloud
Section I. Overview
A. Reader Interest
1. Which category
describes this manuscript?
Practice/Application/Case Study/Experience Report
1. Please explain how this manuscript advances this field of research and/or contributes
new to the literature.
This paper describes a framework for semantic enrichment, ranking and integration of
web video tags using Semantic Web technologies. This semantic enrichment bridges the gap
between the uncontrolled and flat structures typically f
ound in user
generated content and
structures provided by the Semantic Web.
Further the Multi
dimensional context filtering for tag expansion makes the tag ranking
much easier providing less ambiguous tags to concept matching.
Does the introduction state the objectives of the manuscript in terms that encourage
the reader to read on?
__Could be improved
2. How would you rate the organization of the manuscript? Is it focused? Is the length
___Could be improved
3. Please rate and comment on the readability of this manuscript.
___Easy to read
but requires some effort to understand
___Difficult to read and understand
Section II. Evaluation
Please rate the manuscript. Explain your choice.
Section III. Detailed Comments
(provide your thoughts/criticism about the ideas in the
only summarize the paper but have a critical look here
Current techniques to retrieve, integrate and present this media to users
deficient and could do with improvement
This paper describes a framework
for semantic enrichment, ranking
integration of web video tags
using Semantic Web technologies
enrichment is done
various contextual a
nalyses of the video.
Further the paper proposed a
performed over a local
by means of spreading act
This paper also
described the tag
resource mapping (with DBpedia as our
semantic repository) and outlined how
Linked Data principles can aid with linking to
generated video content.
one aspect that you liked
the most in this paper.
The aspect I liked the most is t
he idea of using semantic web technologies
for semantic enrichment, ranking and integration of web video tags. Further the
paper is neatly and clearly arranged making it easier to read.
aspect that you disliked the most in this paper.
An overview of the ideology is easier to understand but the paper lacks the
could have been explained
in more detail.
V. Discussion Points
(provide at least 3
ideas/techniques described in the paper;
these will be used for discussions in the class)
How scalable is the given approach?
Other than DBpedia, can the given approach be used with other
Does the multi dimensional context filtering for tag expansion really make the tag
ranking much easier?