Review Comment:
This paper describes a case study for manual ontology alignment in the context of wearable devices and bioinformatics in professional health care. The authors mainly discusses a possible strategy which ontologies could be linked to support physicians in locating wearable devices for a patient having a specific diagnosis. Their aim is to show the value of semantic integration by showing how medical professionals could benefit by having integrated access to biomedical models/repositories.
I agree in that overcoming semantic heterogeneities e.g. by aligning ontologies has not been addressed in all domains and further work is necessary. For instance, in the medical field there are still many open issues.
However this application paper does neither show how anyone can profit of this work nor gives a convincing evaluation of this matching problem. What is the concrete result of this work? How could any physician profit of the few detected correspondences? A manual alignment was only realized for four diseases. There is no discussion who concretely needs this case study - was there some “real world” motivation, e.g. due to a problem in a real collaboration with some hospital? If so – it is not mentioned in this paper.
Currently, a concrete alignment strategy of the five selected ontologies DOID, FMA, SYMO, QUDT and SSN is not described. The authors discuss semantic bridges that can assign a “high level” semantic type for the alignment that will be determined between the ontology concepts. Some given examples do not highlight the issues of aligning the concepts. For instance, there is a description of a manual web search strategy but I feel this is not “a solution to anything really problematic”. It is probably the standard technique for a medical expert ..
It is unclear what the authors mean with “data” – do you mean instances? What are examples of data, how did you use the data? Are their real annotations between data and ontological concepts?
I propose to combine this work with some existing automatic approach and do an evaluation using the manual alignment. Semantic bridges could be used by an automatic matching approach. It needs to be shown that concrete correspondences between all five ontologies have been detected, e.g. by showing the number of identified correspondences and measuring the quality of the results (precision, recall, fmeasure, logical coherence ...).
Overal the currently descibed methodology is poor looking at the huge amount of related work in this area. Moreover, the publication neither gives an evaluation nor shows a real application in some project nor gives user access to the methodology and/or results, such that it is not adequate for publication in SWJ.
Some specific comments:
In the abstract, triple stores and the importance of owl are mentioned. Why? Both are not used.. The sentence “Not only are OWL-based ontologies extremely versatile and extendable, but triple stores are robust against changes to ontologies and data.“ must be removed since there is no connection to the paper content at all.
The authors define some goals, that seem to be not achieved (?) but at least not discussed (!). Moreover, the four goals are not concrete (why four? what is the content of each goal?). I would prefer to highlight few real contributions in the introduction.
Related work:
Data and information integration have been studied intensively - a.o. there has been a lot of research on ontology matching/alignment in the last 15 years. In particular, a lot of approaches have been published describing domain-related methodologies, e.g. for the biomedical field. Some surveys on ontology mapping and alignment give an overview to common approaches, e.g. the book “Ontology matching” (Euzenat, Shvaiko, 2007). In the last years, the life science tracks (Anatomy & LargeBio) of OAEI (Ontology Alignment Evaluation Initiative) showed how successful several systems are in automatically aligning anatomy and other biomedical ontologies (AML, Logmap, GOMMA …) [http://oaei.ontologymatching.org/]. The authors discuss one interesting approach of Rance et al. suitable for their domain. However there are further approaches that could be used and discussed in this context, e.g. “Instance-based matching of large life science ontologies” (Kirsten et al 2007) or “Alignment of biomedical ontologies using life science literature” (Tan, 2006). Such approaches could be used in this paper, e.g. the authors mentioned some manual use of a search engine -> instead the search could be done automatically and results could be combined with some standard linguistic matching.
Overall, the current discussion of related work is poor, i.e. existing literature in field of ontology alignment needs to be discussed appropriately!!
What does this mean?:
“In each case, since the original models are curated by separate entities, new mapping models (aka semantic bridge ontologies [8,5] were created and then merged with the original..)” --> this is confusing. There is research on ontology merging, which is absolutely not trivial! What are mapping models? The herein used terms are confusing to some extent looking at existing work in the field of schema and ontology alignment /matching / mapping / merging etc.
Minor comments:
The corresponding ontologies and concept identifiers (accessions) should be shown in all illustrations, e.g. the reader might not know that “body part” is an FMA concept.
“but it wasn’t being used” - -> why do you mention?
currated --> curated
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