Matches in ESWC 2020 for { ?s ?p This paper introduces a novel (RDF) entity summarizer, DRESSED, that takes user feedback into account to improve the set of triples presented to users. The approach uses reinforcement learning under the hood. The paper describes the approach, and reports on two experiments that evaluate the performance of DRESSED (compared to other entity summarization approaches). The paper is nicely written, and addresses a very important problem. Entity summarization is key in many ways for the Semantic Web, including, but not limited to, the display of information to users when, e.g., browsing the Web of Data. The approach is described with an appropriate level of detail, and an open source implementation is made available by the authors. While there does not seem to be much documentation about how to set it up and run it, it is already a good thing that the software prototype is made available upfront. A few fairly minor concerns: - Figure 1: maybe nitpicking, but isn't it weird to consider that the label is an irrelevant triple? - There is little reference/explanation of Figure 2, which limits its value. - The notation for significant/non-significant differences in Table 1 is unclear (circle and triangle). Is the first glyph referring to FACES-E and the second to IPS? This should be made clear. Also, if there were significant differences between FACES-E and IPS, it would be relevant to report them (using a less cryptic notation). - Section 4.2: give more detail about the random sampling of entities. Are the entities the same for all participants? What's their rdf:type? Is it a within subject experiment, all participants performing the three conditions? If so, is the presentation order of conditions counterbalanced to void asymmetric transfer? - Similar issue with Table 2 as with Table 1. Overall, the results are promising. This is an interesting piece of work, likely to be of interest to a broad audience at thr conference, and I recommend acceptance. -------------POST REBUTTAL-------------------- Rebuttal addresses my concerns. Rating and recommendation remain unchanged. Please revise the paper accordingly.". }
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- Paper.66_Review.0 hasContent "This paper introduces a novel (RDF) entity summarizer, DRESSED, that takes user feedback into account to improve the set of triples presented to users. The approach uses reinforcement learning under the hood. The paper describes the approach, and reports on two experiments that evaluate the performance of DRESSED (compared to other entity summarization approaches). The paper is nicely written, and addresses a very important problem. Entity summarization is key in many ways for the Semantic Web, including, but not limited to, the display of information to users when, e.g., browsing the Web of Data. The approach is described with an appropriate level of detail, and an open source implementation is made available by the authors. While there does not seem to be much documentation about how to set it up and run it, it is already a good thing that the software prototype is made available upfront. A few fairly minor concerns: - Figure 1: maybe nitpicking, but isn't it weird to consider that the label is an irrelevant triple? - There is little reference/explanation of Figure 2, which limits its value. - The notation for significant/non-significant differences in Table 1 is unclear (circle and triangle). Is the first glyph referring to FACES-E and the second to IPS? This should be made clear. Also, if there were significant differences between FACES-E and IPS, it would be relevant to report them (using a less cryptic notation). - Section 4.2: give more detail about the random sampling of entities. Are the entities the same for all participants? What's their rdf:type? Is it a within subject experiment, all participants performing the three conditions? If so, is the presentation order of conditions counterbalanced to void asymmetric transfer? - Similar issue with Table 2 as with Table 1. Overall, the results are promising. This is an interesting piece of work, likely to be of interest to a broad audience at thr conference, and I recommend acceptance. -------------POST REBUTTAL-------------------- Rebuttal addresses my concerns. Rating and recommendation remain unchanged. Please revise the paper accordingly."".