Review Comment:
The paper describes an approach for ontology matching that is based on a number of pattern-based rules that explore thesaurus relations to infer the meanings of the words composing ontology classes labels. I find the idea interesting, but the paper does not provide a proper motivation, it fails to describe the methodology with sufficient accuracy, and the evaluation does not really showcase the usefulness of the proposed method. Moreover, the writing is at times very confusing, with many typos and grammar mistakes that hinder readability.
The paper needs to be thoroughly reviewd and several sections rewritten for clarity.
Major issues:
1. The paper needs a stronger motivation. In page 2: "We consider that a greater set of measures does not
guarantee a successful comparison of equivalence between
two elements. But the results are more accurate
if we use measures derived from all the conceptual
rules since it decreases the disambiguation of the alignments."
This notion should be more deeply explored, perhaps with concrete examples.
2. There is a difficulty in handling the concepts of "class", "concept" and "label". In page 4: "We suppose that a class has only one meaning under a domain. Thus, the problem is to find out the best one sense of a class". This statement is inaccurate, since in a well designed ontology the meaning of a class should be inferrable by looking at a classes properties and relations. An ontology class has only one sense, a "label" may have multiple. Furthermore, referring to a class label as a concept is erroneous. Concepts can be described by labels, but they are not the labels themselves.
3. There are multiple examples throughout the paper that seem unrelated. It would be best to have one or two coherent examples that you refer to when needed.
4. The explanations of the algorithm are very confusing. In Section 3.1.2 it is particularly hard to discern how the selection of the appropriate sense is arrived at. In Section 3.2 it is very hard to understand how exactly mappings are computed and scored. These need to be thoroughly rewritten and possibly with a concrete example throught the explanation.
5. The evaluation does not adequately support the proposed approach. The authors themselves state taht "we could argue that OAEI catalogues are rarely ambiguous." Then why use the conference set for the only evaluation presented? It would be best to complement this benchmark evaluation with some specific examples of mappings captured by your WSD based approach, that cannot be captured with SD.
6.The role of WSD in the mapping strategy is not adequately addressed and discussed.
How is the "success ratio" for automated meaning discovery calculated? If the percentage means the number of classes for which the correct sense was identified, aren't 0.5% and 0.4% values really low? And if this is a typo, and you meant 40 and 50%, there are still not very high. The impact of these success rates in your matching approach should be discussed.
Minor Issues:
1. Authors claim to have reviewed the latest approaches in OM since 2003. This is not acurate, since the most recent systems mentioned are from 2011, four years ago.
3. This example " House_Mansion ≡ House1 : {S1 = ”the house...”, S2 = ”a place...”}." is perhaps not the best, since "a place" would actually be a hypernym of house? Looking up "house" in WordNet does not return "a place" as a possible synset.
4. In page 4, the "8x8" combinations are unclear. The explanation given later, should be moved to page 4.
Some typos:
"The network of terms regarding with a class" -> The network of terms regarding a class
"In any way, It seems that both words make reference to barriers" - > It seems...
"Moreover, the algorithm can
work with two external resources: WordNet and Roget’s
thesaurus. And it works using a set of specific
rules." - > Moreover, the algorithm can
work with two external resources, WordNet and Roget’s
thesaurus, and it works using a set of specific
rules.
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