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• Paper.80_Review.1_Reviewer type RoleDuringEvent.
• Paper.80_Review.1_Reviewer label "Anonymous Reviewer for Paper 80".
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• Paper.80_Review.1 issued "2001-01-16T11:39:00.000Z".
• Paper.80_Review.1 creator Paper.80_Review.1_Reviewer.
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• Paper.80_Review.1 reviews Paper.80.
• Paper.80_Review.1 issuedAt easychair.org.
• Paper.80_Review.1 issuedFor Conference.
• Paper.80_Review.1 releasedBy Conference.
• Paper.80_Review.1 hasContent "Overall evaluation: 2 (accept)"".
• Paper.80_Review.2_Reviewer type RoleDuringEvent.
• Paper.80_Review.2_Reviewer label "Anonymous Reviewer for Paper 80".
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• Paper.80_Review.2 issued "2001-01-17T13:52:00.000Z".
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• Paper.80_Review.2 issuedAt easychair.org.
• Paper.80_Review.2 issuedFor Conference.
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• 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"".
• Paper.81 type SubmissionsPaper.
• Paper.81 label "Handling Multi-chapter Inconsistencies in DBpedia Evolution".
• Paper.81 title "Handling Multi-chapter Inconsistencies in DBpedia Evolution".
• Paper.81 issued "2001-12-03T15:09:00.000Z".
• Paper.81 authorList b0_g215.
• Paper.81 submission Paper.81.
• Paper.81 track Track.Knowledge%20Graphs.
• Paper.81 preprint 121230081.pdf.
• b0_g215 first Author.81.1.
• b0_g215 rest b0_g216.
• Author.81.1 type RoleDuringEvent.
• Author.81.1 label "Túlio Brandão Soares Martins, 1st Author for Paper 81".
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• Paper.82 type SubmissionsPaper.
• Paper.82 label "Discovering Semantically Broken Links in LOD Datasets".
• Paper.82 title "Discovering Semantically Broken Links in LOD Datasets".
• Paper.82 issued "2001-12-03T15:11:00.000Z".
• Paper.82 authorList b0_g217.
• Paper.82 submission Paper.82.
• Paper.82 track Track.Knowledge%20Graphs.
• b0_g217 first Author.82.1.
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• Author.82.1 type RoleDuringEvent.
• Author.82.1 label "André Regino, 1st Author for Paper 82".
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• André_Regino name "André Regino".
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• Paper.83 type SubmissionsPaper.
• Paper.83 label "Background Knowledge in Schema Matching: Strategy vs. Data".
• Paper.83 title "Background Knowledge in Schema Matching: Strategy vs. Data".
• Paper.83 issued "2001-12-03T15:39:00.000Z".
• Paper.83 authorList b0_g219.
• Paper.83 submission Paper.83.
• Paper.83 track Track.Integration,%20Services%20and%20APIs.
• b0_g219 first Author.83.1.
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• Author.83.1 type RoleDuringEvent.
• Author.83.1 label "Jan Portisch, 1st Author for Paper 83".
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• Author.83.2 type RoleDuringEvent.
• Author.83.2 label "Michael Hladik, 2nd Author for Paper 83".
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• Author.284.1 type RoleDuringEvent.
• Author.284.1 label "Jan Portisch, 1st Author for Paper 284".
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• Michael_Hladik type Person.
• Michael_Hladik name "Michael Hladik".
• Michael_Hladik label "Michael Hladik".

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