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- Paper.80_Review.2 type ReviewVersion.
- Paper.80_Review.2 issued "2001-01-17T13:52:00.000Z".
- Paper.80_Review.2 creator Paper.80_Review.2_Reviewer.
- Paper.80_Review.2 hasRating ReviewRating..
- Paper.80_Review.2 hasReviewerConfidence ReviewerConfidence.4.
- Paper.80_Review.2 reviews Paper.80.
- Paper.80_Review.2 issuedAt easychair.org.
- Paper.80_Review.2 issuedFor Conference.
- Paper.80_Review.2 releasedBy Conference.
- Paper.80_Review.2 hasContent "This paper proposes an approach to perform entity resolution cooping with one of the utmost problems in the Semantic Web, namely entity duplicates. The authors deal with this problem proposing a work centered on the notion of similarity multi-dimensional distance across entities (using different entity features, whether not available it is assigned a -1) that is tailored to a given dataset utilizing a pre-processing analysis in order to identify the optimal threshold. This information is then utilized by a Random Forest classifier that learns through an (active) learning process that uses as input unlabeled entities and those labeled by the thresholding process. This is an unsupervised process that does not require human annotations. The paper presents nicely the problem statement, the experimental setups involves 6 datasets and the experimentation is made reproducible, this is a plus. However there are some flaws that prevent me to accept the paper (see below). Strengths: - good problem statement - experimental setup based on three different types of datasets (this points to a weakness) - the experiment is made reproducible thanks to the sharing of the code and data used for the evaluation, thus fostering the reproducibility Weaknesses: - lack of statistical analysis of the dataset. In other words, the authors claim that the thresholding works well on 3 types of datasets (nominally different), however are they really different? - I see the elbow point as dataset dependent. Presented as such, the measure of the elbow is a pre-processing task. However, this is much dependent how entities are "similar" from each other. For instance, I'd expect that a dataset with numerous duplicates has a point higher than a dataset with less entities, regardless the dimension of the dataset itself. I suspect that such an assumption works well with DBpedia since it models how the dataset has been created. Would not fit for instance other knowledge bases that are for instance created for specific domains such as tourism or health. This might limit the applicability of this work. Despite the authors stated the opposite that it's is domain independent in the value proposition. - not clear how the Query-by-Committee strategy works in detail. The intuition is to utilize an ensemble of classifiers to further filter the output of the active learning. If so, is this learning phase utilizing the same exact set of data from the noisy pool? Generally, the entire methodology would benefit from an illustration and a mathematical derivation (as it is presented as a sequence of packages without clear interfaces that make harder the understanding from a reader) - In 3.4, are the results of the resolution process over the full datasets? Ideally, I'd expect to learn a model from the training, and applying it to test. This does not seem to be the case from the illustrations since the performance improves at each iteration. However, this is not clear nor illustrated in Figures thus the doubt"".