Matches in ESWC 2020 for { ?s ?p ?o. }
- Jian_Xu type Person.
- Jian_Xu name "Jian Xu".
- Jian_Xu label "Jian Xu".
- Jian_Xu holdsRole Author.114.2.
- Hao_Yuan type Person.
- Hao_Yuan name "Hao Yuan".
- Hao_Yuan label "Hao Yuan".
- Hao_Yuan holdsRole Author.114.3.
- Rujia_Shen type Person.
- Rujia_Shen name "Rujia Shen".
- Rujia_Shen label "Rujia Shen".
- Rujia_Shen holdsRole Author.114.4.
- Ming_Xu type Person.
- Ming_Xu name "Ming Xu".
- Ming_Xu label "Ming Xu".
- Ming_Xu holdsRole Author.114.5.
- Paper.115 type SubmissionsPaper.
- Paper.115 label "Semantics-based End-to-End Learning for Typhoon Intensity Prediction".
- Paper.115 title "Semantics-based End-to-End Learning for Typhoon Intensity Prediction".
- Paper.115 issued "2001-12-04T09:48:00.000Z".
- Paper.115 authorList b0_g298.
- Paper.115 submission Paper.115.
- Paper.115 track Track.Machine%20Learning.
- b0_g298 first Author.115.1.
- b0_g298 rest b0_g299.
- Author.115.1 type RoleDuringEvent.
- Author.115.1 label "Hamada Zahera, 1st Author for Paper 115".
- Author.115.1 withRole PublishingRole.
- Author.115.1 isHeldBy Hamada_Zahera.
- b0_g299 first Author.115.2.
- b0_g299 rest b0_g300.
- Author.115.2 type RoleDuringEvent.
- Author.115.2 label "Mohamed Sherif, 2nd Author for Paper 115".
- Author.115.2 withRole PublishingRole.
- Author.115.2 isHeldBy Mohamed_Sherif.
- b0_g300 first Author.115.3.
- b0_g300 rest nil.
- Hamada_Zahera type Person.
- Hamada_Zahera name "Hamada Zahera".
- Hamada_Zahera label "Hamada Zahera".
- Hamada_Zahera holdsRole Author.115.1.
- Mohamed_Sherif type Person.
- Mohamed_Sherif name "Mohamed Sherif".
- Mohamed_Sherif label "Mohamed Sherif".
- Mohamed_Sherif holdsRole Author.115.2.
- Author.116.1 type RoleDuringEvent.
- Author.116.1 label "David Schindler, 1st Author for Paper 116".
- Author.116.1 withRole PublishingRole.
- Author.116.1 isHeldBy David_Schindler.
- b0_g302 first Author.116.2.
- b0_g302 rest b0_g303.
- Author.116.2 type RoleDuringEvent.
- Author.116.2 label "Benjamin Zapilko, 2nd Author for Paper 116".
- Author.116.2 withRole PublishingRole.
- Author.116.2 isHeldBy Benjamin_Zapilko.
- b0_g303 first Author.116.3.
- b0_g303 rest nil.
- Author.116.3 type RoleDuringEvent.
- Author.116.3 label "Frank Krüger, 3rd Author for Paper 116".
- Author.116.3 withRole PublishingRole.
- Author.116.3 isHeldBy Frank_Krüger.
- David_Schindler type Person.
- David_Schindler name "David Schindler".
- David_Schindler label "David Schindler".
- David_Schindler holdsRole Author.116.1.
- Benjamin_Zapilko type Person.
- Benjamin_Zapilko name "Benjamin Zapilko".
- Benjamin_Zapilko label "Benjamin Zapilko".
- Benjamin_Zapilko holdsRole Author.116.2.
- Benjamin_Zapilko holdsRole Author.163.4.
- Author.163.4 type RoleDuringEvent.
- Author.163.4 label "Benjamin Zapilko, 4th Author for Paper 163".
- Author.163.4 withRole PublishingRole.
- Author.163.4 isHeldBy Benjamin_Zapilko.
- Frank_Krüger type Person.
- Frank_Krüger name "Frank Krüger".
- Frank_Krüger label "Frank Krüger".
- Frank_Krüger holdsRole Author.116.3.
- Paper.116_Review.0 type ReviewVersion.
- Paper.116_Review.0 issued "2001-01-26T19:59:00.000Z".
- Paper.116_Review.0 creator Paper.116_Review.0_Reviewer.
- Paper.116_Review.0 hasRating ReviewRating.2.
- Paper.116_Review.0 hasReviewerConfidence ReviewerConfidence.4.
- Paper.116_Review.0 reviews Paper.116.
- Paper.116_Review.0 issuedAt easychair.org.
- Paper.116_Review.0 issuedFor Conference.
- Paper.116_Review.0 releasedBy Conference.
- Paper.116_Review.0 hasContent "After rebuttal: I thank the authors for their response. I decided to keep my score (accept). ****************** This paper describes an approach for generating a knowledge graph of software mentions in scientific papers from the social sciences. The approach includes disambiguation and enrichment using DBPedia and Wikidata. The evaluation shows that the approach has a .82 f-score for detecting software mentions in the corpora. The paper is well written, easy to follow and highly relevant for the conference and track. I believe this is an important topic to measure both the impact of software and to properly credit authors for their work, and it's great to see that both the code used and the resultant knowledge graph are available online with examples to explore (even if the readme of the code still needs work to be reusable). In addition, the authors are straightforward with the limitations of the approach, which is very useful when comparing and assessing it for reuse. Therefore I think this paper should be accepted at ESWC 2020. I list below some comments, suggestions and questions that would be great to see addressed in the camera ready version of the paper. - Given that some manual rules are needed for the approach, how dependent is the approach on the chosen domain? - The authors acknowledge that the graph has errors. However there is no comment on how would these errors be fixed when detected by users. Is there a plan for a feedback mechanism? - The authors state that one benefit of the approach is for proper attribution to authors and citation. I don't see the difference between them; aren't we attributing the authors by properly citing their work? Maybe the authors of the paper are referring to tracking the impact of software? - The precision obtained for the SSC is in most cases very low. Training with SSC with distant supervision does not really add much to the precision, which I guess it's what the consumers of the KG will mostly care. I would have liked to see some discussion on whether the extra effort is really worth the gain in those cases. - In the evaluation, the comparison against the state of the art is not really fair, because they used different corpus and domain, although it's informative. Why not comparing against a simple classifiers as baselines? For example, a TF-IDF + binary classifier on whether a sentence is a software mention or not would have been easy to do with GSC. It would not tell you which software was mentioned, but it may have been a good alternative to SSC. - I am a little confused about using "String" as a class in the data model. String is usually a data type, and having it as class does not sound right. It looks redundant to have a mention which then refers to a software, and I can think of a few alternatives that would produce a cleaner data model (specifically for querying): - 1) Extend schema:mentions with skg:mentionsSoftware, (domain skg:SoftwareArticle, range skg:SoftwareApplication, both classes extensions of their respective schema.org.). That way you can have a direct link between paper and software. - 2) Instead of String, call the class skg:SoftwareMention, it will be less confusing for users. - I would like to suggest the authors to look at codemeta.org, an extension of schema.org for scientific software that includes some of the terms proposed by the authors to describe software. - Schema.org has the class SoftwareSourceCode, so the information about the repositories could be linked as well. - Content negotiation on the vocabulary (skg) does not work. I tried: 'curl -sH "accept:application/rdf+xml" https://data.gesis.org/softwarekg -L' with text/turtle and application/rdf+xml. In both cases, only html is returned. This means I cannot import this vocabulary in my application. I didn't find a link to download the rdfs/owl file of the data model in the documentation. Since the paper does not emphasize the vocabulary as a contribution, I will not penalize this in my review, but I still think it should be addressed."".
- Paper.117 type SubmissionsPaper.
- Paper.117 label "How good is this merged ontology? Towards a customizable quality evaluation".
- Paper.117 title "How good is this merged ontology? Towards a customizable quality evaluation".
- Paper.117 issued "2001-12-04T09:56:00.000Z".
- Paper.117 authorList b0_g304.
- Paper.117 submission Paper.117.
- Paper.117 track Track.Ontologies%20and%20Reasoning.
- b0_g304 first Author.117.1.
- b0_g304 rest b0_g305.
- b0_g305 first Author.117.2.
- b0_g305 rest b0_g306.
- Author.117.2 type RoleDuringEvent.