S-Match: an open source framework for matching lightweight ontologies
This is a revised manuscript, which has now been accepted for publication. The reviews below are for the original submission.
Review 1 by Ming Mao:
This paper describes an open source semantic matching framework, called S-Match, which tackles the semantic interoperability problem by transforming tree-like data structures into lightweight ontologies and establishing semantic correspondences between them. The framework includes 3 algorithms to do basic semantic matching, minimal semantic matching and structure preserving semantic matching. The S-Match architecture also provides an extensible API for developing new algorithms and plug-in specific background knowledge, which brings in great flexibility to exploit different matching algorithms. As an open source ontology matching framework, S-Match will definitely lower the barriers for people to take the advantage of semantic technologies.
The paper is well-written, and logic is clear thus easy to follow. However it would be better if the authors describe more in details about how classifier and decider package work and explain whether two Oracles are needed in the architecture due to the performance issue.
Review 2 by Wei Hu:
In this paper, the authors introduces the overview of S-Match, which is an ontology matching tool that is continuously developed since several years ago. Currently, it is an open source tool and has proposed a lot of solid ideas for the ontology matching community.
In general, this paper is clearly written, easy to understand and has enough details. The reason why I gave a minor revision is that I expect the authors to add some comparison and citation to existing works.
(1) Please add a (brief) introduction to describe some performance (presicion, recall, run-time, etc.) on published benchmarks, such as OAEI. The purpose is not to compare with others, but at least can give users an intuition of the strength and weakness of the tool.
(2) Falcon-AO is also an open source tools under the Apache license. For the GUI, COMA++ gives a similar display panel. Although the intention of the paper is to introduce the architecture of S-Match, some important references should be highlighted.
My reviews according to each section is as follows.
In Sect. 1, since S-Match has been improved over years, so I would suggest the authors to add a very short description about the development history. For example, the year the project started.
In Sect. 2, when referring to the tree structure, can you deal with directed acyclic graph? Besides, please provide some example (tree structured) ontologies here? For example, UNSPSC, Yahoo directory?
In Sect. 3, I think citing the book "Ontology Matching" instead of  is better.
In Sect. 3.1, lack a blank between "of" and "humanistic discipline".
In Sect. 3.2, since considering the "more general", "less general", etc. relations, computing the minimal semantic matching is very important, which can prevent many trivial mappings in practice.
For Sect. 3.3, it seems that SPSM violates the natural heterogeneity of ontologies. Can you give an explanation on this?
For Fig. 5, what do "offline" and "online" mean?
I think that Fig. 6 is straightforward to understand. So it is not necessary to give such a detailed explanation.
Review 3 by Shenghui Wang:
This paper described the S-Match framework for matching lightweight ontologies which are transformed from taxonomies, catalogues, web directories, etc. S-Match is one of the mature ontology matching systems, openly available. The extensible API, command line and GUI interfaces provide the access to the users with different needs. It is highly valuable for the ontology matching community, as well as for the journal. As a system paper, it is clearly written with relatively detailed descriptions about the matching algorithms, the framework and different interfaces.
1. One thing not clear to me is that how well S-Match performs in the standard OAEI campaign. The authors only mentioned they have been providing the datasets for the OAEI in the past 5 years, which is indeed rather valuable for the community, but does not strength the paper itself.
2. The input formats are tab indented and XML, while more and more data are in RDF triples (or even OWL). It would be more flexible to have extra components transforming standard Semantic Web formats into the format the system can work with.
3. I presume the matching is currently done only at the schema level. How about using the instance data in the matching procedure?
4. The literature review is very poor. With half of the references being self-citations, the authors only pointed to the general book by Euzenat and Shvaiko. It would be much more convincing if some similar systems are also introduced, preferably with performance comparison.
5. The example given in Section 3.3 (Structure Preserving Semantic Matching) does not show the advantage of the SPSM algorithm. Instead, the resulting mappings seem to be problematic to me. This SPSM algorithm may be more suitable for matching functions such as web service descriptions or APIs, as the authors claimed. It is better to have the corresponding examples to support this claim.
6. Listed in the project website, quite a few external projects have used the S-Match, although it is not clear to which extend the listed projects used or are still using the system. The paper would be much more strengthened if examples of usage are given.