Review Comment:
Overall evaluation
Select your choice from the options below and write its number below.
== 3 strong accept
== 2 accept
== 1 weak accept
== 0 borderline paper
== -1 weak reject
* -2 reject
== -3 strong reject
Reviewer's confidence
Select your choice from the options below and write its number below.
== 5 (expert)
== 4 (high)
* 3 (medium)
== 2 (low)
== 1 (none)
Interest to the Knowledge Engineering and Knowledge Management Community
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== 5 excellent
== 4 good
== 3 fair
* 2 poor
== 1 very poor
Novelty
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== 5 excellent
== 4 good
== 3 fair
* 2 poor
== 1 very poor
Technical quality
Select your choice from the options below and write its number below.
== 5 excellent
== 4 good
== 3 fair
* 2 poor
== 1 very poor
Evaluation
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== 5 excellent
== 4 good
== 3 fair
* 2 poor
== 1 not present
Clarity and presentation
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== 5 excellent
== 4 good
== 3 fair
* 2 poor
== 1 very poor
Review
This paper proposes an approach to identify the salient and discriminating aspects of a music style compard to other styles, e.g. as a basis to improve retrieval and recommendation.
The approach extracts Wikipedia pages related to the music styles under consideration (Baroque, Carnatic, Flamenco, Hinudstani and Jazz) and constructs a graph consisting of edges indicating how often one entity is references from a given entity (node).
A measure of salience is proposed to compute a salience signature for each music style, relying on PageRank and IDF-style category weighting. The approach is evaluated on a recommendation task with respect to a baseline relying on a Linked Data based Semantic Distance measure.
Overall, the approach is rather straightforward and quite adhoc. The main design choices are not well justified, in particular why the measure has been defined as it has been defined. Further, the novelty with respect to existing methods is not well justified. The discussion of related work is rather superficial, glossing over many related approaches at best.
It is also not clear what is specific about this method that makes it suitable for the music domain. The method seems generally applicable to other domains. In this sense it is odd that the authors emphasize the application to the music domain rather than the method per se. In fact, the application to discover salient and discriminating properties of music styles is in my view only one of many possible application domains on which the authors happened to choose to evaluate their method. Unless the methods proposed is really specific for the music domain - which in my view it is not - the authors should focus on the description of the method per se and the general type of problems that it has been designed for, presenting the application to the music domain only as one application and to provide a proof-of-concept and evaluation of their approach.
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