Review Comment:
The paper presents an approach for calculating a semantic similarity between predicates in Linked Data. The approach is based on the existing Normalized Semantic Web Distance to calculate context-based similarity between predicates. Furthermore, the authors propose a proximity-based similarity, which is calculated using Neighborhood Formation. The authors combine both the context-based and the proximity-based similarity into one final Predicate Semantic Similarity (PSS). The approach is evaluated on 3 dataset, and it is compared to a number of baselines and related approaches. The evaluation is in favor of the proposed approach.
The paper is well structured, well written and easy to follow and understand. The paper addresses an interesting problem, and the authors present some interesting ideas, however there are several drawbacks.
- While the presented approach is interesting and seems to perform well, the authors don't provide a good motivation for developing such an approach. For example, in which applications can this approach be used? The authors vaguely list several possible applications in the introduction, but then don't provide any details how their approach could be used in these applications. It would be beneficial to see the approach being used in a real task or application, such as ontology matching, or identifying similar predicates across different LOD datasets. An evaluation with a comparison to the corresponding related approaches for that task will significantly increase the value of the paper.
- The evaluation protocol is sound, however the size of the datasets is rather small and it does not allow to draw any statistical significant conclusions. The gold standard must be extended to at least couple of hundreds of predicate pairs in order to be considered valid.
- It is not clear why the authors don't compare their approach to [5]. Furthermore, it would be interesting to see an evaluation comparison to other graph embeddings, such as TransR and HolE, which also embed relations.
- The example depicted in Figure 2 would be more useful if it contained more nodes and different type of predicates. It would be interesting to show the advantage of the proposed approach over the related approaches.
Minor comments:
- The figures and tables should be placed on the same page where they are referenced, e.g., Table 1 should be moved to page 9, Table 2 on page 10, Table 3 on page 11 etc.
- Remove "and" from the list of authors
- There should always be space before a reference number, e.g., "similarity measure[8]" should be "similarity measure [8]"
- "Table1" -> "Table 1"; "Table2" -> "Table 2" etc..
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Comments
NSWD
There seems to be an error in equation 2: log f_lambda(x) is used twice in the min function. Maybe the second x should be a y? Also, it could be possible to simplify the formula by taking the log function outside the max and min functions.