Review Comment:
This paper proposes a predictive maintenance use case for Industry 4.0 use cases. The work is focused on combining data mining and semantic reasoning in order to improve the results of prediction and provide a better understanding of inferred knowledge. Outcomes of the data mining algorithms are used to build knowledge representing the prediction results which can be easily understood by novice users.
Paper is well-written and to some extent easy to follow. The authors have clearly laid out their contributions and structured the paper accordingly. Paper, as it stands, sounds like a decent contribution beyond the state of the art solutions. However, a few claims made by authors need further verification and justification. For example, authors claimed that having semantic representation will increase the readability and understanding for the novice users, such kind of claims need to be backed up by references. One can easily provide a counter-argument that even without using semantic representation, a better visualization technique can support a better understanding of trends and timely detection of future events.
Authors further need to motivate that how their approach of automatic creation of knowledge base and semantic representation is a better choice. A major advantage of semantics is the uniform information representation (data + metadata), which should have been demonstrated in this paper to show how the proposed approach is capable of providing semantic interoperability among various heterogeneous information sources.
The example given in the paper is a bit disconnected, thus making it hard to get a step by step picture of the overall flow. What about introducing a use case at the beginning of the paper by discussing the real-world scenario and portray the picture of how the existing system will fail and how the proposed solution will improve the status quo. I recommend authors to give an example scenario and then use this example throughout the paper.
Datasets and experimentation are also a bit vague and hard to show performance and value addition of the systems. I recommend improving the structure of Section 5 by properly introducing the dataset, aims of experiments and then discuss the achieved results.
Minor formatting issue: Figures/Tables are often on different pages than they are referred to in. I believe it is an automatic Latex setting, but perhaps some tricks can help to bring the figures/tables and text discussing them on the same page rather than turning between pages every now and then.
Overall, a decent contribution and a novel approach. Although hard to conceptualize overall achievement particularly in the practical scenario with real-world settings and usage by active workers within a smart factory.
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