Review Comment:
This paper is about measuring the evolution quality of a dataset. The evolution quality is defined as "predictability of evolution". In other words, if the evolution of a dataset can be "predicted" using data from previous evolutions, then the quality of evolution is "good". I have several concerns about this paper, the most important of which are related to the approach itself.
First of all, I am not sure whether "quality of evolution" should *only* be determined by its "predictability". I understand that predictability gives some kind of comfort that the ontology will not change in arbitrary ways, but why is this "quality"? I find it hard to think of a way to prove/evaluate this hypothesis and the paper only provides intuitive arguments. Nevertheless, I am willing to accept (in a rather "axiomatic" way) that evolution quality is, by definition, equal to predictability.
Predictability is not such a useful concept either. Being able to predict the evolution of a dataset/ontology O1 is surely a good thing, on first look, as a curator having an ontology O2 that depends on the evolving dataset, can adapt/adjust O2 accordingly. But predictors are never 100% accurate, so, no matter how good the predictor is, it cannot tell us exactly how O1 will change; moreover, it cannot tell us exactly *when* O1 will change. Therefore, the curator still has to probe O1 periodically and ask for the changes. A good quality (=predictable) evolution only guarantees that, on average, the predictor will be "more correct". So what?
Then there is this issue about training and improving the predictors. Much of the paper deals with training the predictor to improve it and identify the most adequate features to serve as predictors. So, this training improves the predictions, essentially (by the accepted definition above) improving the quality of evolution. This sounds strange: by improving a learning algorithm one improves the quality of something unrelated to the algorithm.
Further, note that evolution patterns for a given ontology may change over time, essentially making the predictor less accurate. Does that mean that the evolution quality has degraded? Perhaps it has improved (i.e., a new optimized predictor could have achieved better results, by the authors' definition), but the old predictor is not suitable any more. There is no mention of this problem in the paper.
More detailed comments appear below.
I noticed that the related work includes papers on change detection. Change detection is indeed partially relevant to the problem. Since the area is reviewed, I would suggest including the canonical references to the area, namely [1] (best paper award ISWC-07), [2] (best student paper award ISWC-09), and/or the extended (journal) version of [2], namely [3].
Also, I would advise the authors to have a look at a recent FP7 IP called "DIACHRON", where some discussion on the quality of evolving datasets appears (an entire WP is devoted to that). Some of the metrics proposed there are classic (applicable also for static ontologies), but some (e.g., volatility) are relevant for quality assessment of evolving ontologies and may be useful.
There are several presentation problems, especially related to Section 3, where several notions (e.g., "change model", "predictors", "optimal change models", "rigid and non-rigid properties", ...) are not explained in their first appearance (or at all). Most of them become clear later, especially in Section 5, but that's too late for the reader.
How are the similarity functions (sim_int, sim_ext, sim_label) defined?
Section 3.2: children are usually defined through the rdfs:subClassOf relation ONLY. The authors seem to allow other properties as well, without specifying which ones. Does any property specify a "children" relation in their model?
For data-driven and usage-driven features, the presented "features" are not really features but examples of data-driven/usage-driven changes.
The Identity Aggregator is essentially an instance matching algorithm, right? Which one is used?
ROC should be explained. It is not obvious to non-experts.
The appendix "", mentioned at several points, is not provided.
References
[1] Dimitris Zeginis, Yannis Tzitzikas, Vassilis Christophides. On the Foundations of Computing Deltas Between RDF Models. In Proceedings of the 6th International Semantic Web Conference (ISWC-07), 2007.
[2] Vicky Papavassiliou, Giorgos Flouris, Irini Fundulaki, Dimitris Kotzinos, Vassilis Christophides. On Detecting High-Level Changes in RDF/S KBs. In Proceedings of the 8th International Semantic Web Conference (ISWC-09), 2009.
[3] Vicky Papavasileiou, Giorgos Flouris, Irini Fundulaki, Dimitris Kotzinos, Vassilis Christophides. High-Level Change Detection in RDF(S) KBs. Transactions on Database Systems (TODS), 38(1), 2013.
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