Fwd: [SWJ] Decision letter, #3810-5024

[Şefika EFEOĞLU]

Dear Guest Editors KG Gen from Text 2023, 

I submitted the revised version of the paper, but it doesn’t appear on the 'Under Review' page.

Could you check if everything is working properly?

Thanks in advance, 

Best regards,

From: Şefika EFEOĞLU <sefika...@gmail.com>
Date: Sun, 15 Jun 2025 at 13:14
Subject: Re: [SWJ] Decision letter, #3810-5024
To: www.semantic-web-journal.net <con...@semantic-web-journal.net>

Dear Guest Editors KG Gen from Text 2023, 

Thanks for your decision and comments.

We submitted a revised version of the paper.

Best regards.

On Mon, 19 May 2025 at 10:43, www.semantic-web-journal.net <con...@semantic-web-journal.net> wrote:

Dear Author,
We have reached a decision for your paper. Please find below the details.

Authors: Sefika Efeoglu, Adrian Paschke1
Title: Retrieval-Augmented Generation-based Relation Extraction
Submission Type: 'Full Paper'
URL:
https://www.semantic-web-journal.net/content/retrieval-augmented-generation-based-relation-extraction-0
Tracking number: 3810-5024

Decision Letter:
Assigned editor: Guest Editors KG Gen from Text 2023
(text2...@googlegroups.com)

Dear authors,

your paper requires minor revisions before it can be published in the
Semantic Web journal. Please read the reviewer reviews carefully as they
suggested for some improvements, please improve the paper accordingly.

Please find the reviews below as well as on the paper's page on the journal
website.

Please take all comments by the reviewers into account when preparing your
revision. The revised manuscript will be send back to all (or a selection of)
reviewers for a second round of reviews. Please provide a letter to the
reviewers detailing the improvements made for the resubmission.

We expect your revised paper within 4 weeks, however if you require more
time, please let us know.


   Review #1
Submitted by Anonymous
Recommendation: Accept
Detail Comments
        This paper presents a relation extraction method based on
retrieval-augmented generation.
The central idea involves enhancing the prompt given to the
language model by appending a similar sentence retrieved from the training
subset of the same dataset.
The results demonstrate that this strategy improves performance across three
out of four datasets,
regardless of the language model used.

Most of the points from the review in the first round are addressed (e.g.,
more in-depth analysis; Table 4 contains a direct comparison to related work;
related work is updated).
Still, it would be interesting to see how larger models (Llama-13b or 70b)
and also more recent models (Llama3) would perform on these tasks,
because in many cases they perform better (especially in cases where more
reasoning is required, such as in the proposed tasks).
Another minor improvement can be made by keeping the figures readable when
printed in black/white (appendix C).


   Review #2
Submitted by Garima Agrawal
Recommendation: Accept
Detail Comments
        This manuscript was submitted as 'full paper' and should be reviewed along
the usual dimensions for research contributions which include (1)
originality, (2) significance of the results, and (3) quality of writing.
Please also assess the data file provided by the authors under “Long-term
stable URL for resources”. In particular, assess (A) whether the data file
is well organized and in particular contains a README file which makes it
easy for you to assess the data, (B) whether the provided resources appear to
be complete for replication of experiments, and if not, why, (C) whether the
chosen repository, if it is not GitHub, Figshare or Zenodo, is appropriate
for long-term repository discoverability, and (4) whether the provided data
artifacts are complete. Please refer to the reviewer instructions [1] and the
FAQ [2] for further information.

   Review #3
Submitted by Anonymous
Recommendation: Accept
Detail Comments
        I see few typos, please review for the finalized version and also would be
nice to see the discussion on ethical aspect.


Sincerely,
Guest Editors KG Gen from Text 2023


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