Review Comment:
The paper describes a "session-based" ontology alignment system that allows a user to provide feedback on a suggested partial mapping. This feedback is used immediately to improve the configuation of the matching algorithm (weights, thresholds, etc.).
The approach is orthogonal to the particular similarity metrics used in the matcher (and can be used with any such matcher), and is therefore of interest to many researchers in the field. The approach to recommending appropriate parameter values (e.g. weights and thresholds) is an under-researched area that is also of considerable interest.
The paper makes a point of stressing that caching any similarity computations improves computational performance, but I think this is actually fairly well-known and somewhat obvious. The code for several alignment systems that I've seen does this. I suspect that it is not mentioned more because it might be seen as "cheating" in the OAEI since runtime is reported there.
The experiments are well-conceived and explore the significant aspects of the approach. Experiments are conducted with only a single ontology pair, which somewhat limits the conclusions that can be drawn, however.
Some minor suggestions:
It would be useful to explain how the current paper expands upon [23].
The abbreviation PA is used on page 2 before it is defined on page 3.
On page 8 when the NaiveBayes classifier is discussed, it is not clear how the corpus of documents for each ontology is created. I realize that this is ancillary to this paper, but a sentence of explanation would be helpful.
The description of Sim2 on page 10 is not entirely clear.
On page 20, it is not clear what is meant by "due to the consistent group in the double threshold filtering."
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