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
-1
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)
3
Interest to the Knowledge Engineering and Knowledge Management Community
Select your choice from the options below and write its number below.
== 5 excellent
== 4 good
== 3 fair
== 2 poor
== 1 very poor
5
Novelty
Select your choice from the options below and write its number below.
== 5 excellent
== 4 good
== 3 fair
== 2 poor
== 1 very poor
3
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
2
Evaluation
Select your choice from the options below and write its number below.
== 5 excellent
== 4 good
== 3 fair
== 2 poor
== 1 not present
Not applicable
Clarity and presentation
Select your choice from the options below and write its number below.
== 5 excellent
== 4 good
== 3 fair
== 2 poor
== 1 very poor
5
Review
Overall:
* The authors have tackled the laudable task of surveying ontology use. However: though survey seems to be easy to do, they are not. Constructing a survey is a difficult task and it seems to me that the authors have not done it successfully. Rather than just asking some question, the questions of a survey should be derived from an (or several) hypothesis, the questions should be developed in a way to ensure that inconsistent responses can be discovered, etc. etc. In the details I give some examples of what went wrong (though I am myself not an expert on constructing surveys I have to confess!).
* At the end of the paper I wondered what I had learned from the survey. The conclusion are ones that many people have without the survey (e.g. “one where ontologies have very few individuals, the other in which the number of individuals is more commensurate with the number of classes” is already given in Christoph Tempich, Raphael Volz: Towards a benchmark for Semantic Web reasoners - an analysis of the DAML ontology library. EON 2003 ). A scientific use of surveys is to confirm the wisdom commonly held and support it with numbers. Such a kind of number-foundation however needs to come with stronger hypotheses at the beginning.
* I am puzzled by the title. I had expected from the title that the paper was not about how large ontologies are and whether their engineering was helped by patterns, but rather how *users* experience them in a particular piece of software. This paper is rather about survey ontology engineering practice (though not quite). Note that again that seems to be due to a lack of theory and hypotheses that might have constituted a basis for the survey.
* I would suggest to have this as a poster at EKAW. It is definitely interesting for the community.
Details:
• I am puzzled by table 1, because what was the methodology to arrive at these category of uses? Why is there nothing about visualization? Why is there nothing about explanation/understanding (e.g. MagPie)? Maybe there is a rationale for this list, but it is not explained.
• I am puzzled by table 1, because some options seem to overlap and even contain each other, especially the questions concerning DI, LD and HD. This might actually be a possibility in order to determine inconsistent answers. E.g. DI should imply HD, should it not? If “no” what is the meaning of these explanations? If “yes” how does it come that DI is so much more prominent than HD? Furthermore, correlations are not sufficient to tackle these answers!
• A survey of 13 respondents, such as done in section 7.2 is not meaningful
• I do not understand Table 8. Does it mean that there are patterns with hundreds of classes? This does not seem to make sense. (maybe a pattern that is instantiated hundred of times? This would make more sense). Anyway the text does not tell
* Further References to be considered:
Birte Glimm, Aidan Hogan, Markus Krötzsch, Axel Polleres: OWL: Yet to arrive on the Web of Data? LDOW 2012, workshop at WWW-2012, CEUR-WS.org 2012
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