PONE-D-24-59861KGMP: Argumenting Retrieval Knowledge Graph with Multi-hop PerceptronPLOS ONE

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PLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper proposes the Knowledge Graph Multi-hop Perceptron (KGMP) module to enhance Knowledge-Based Question Answering (KBQA) systems by addressing the challenge of multi-hop questions. The proposed framework leverages fine-tuned open-source Large Language Models (LLMs) for retrieving and generating answers from knowledge graphs. A key innovation of KGMP is its ability to assess the sufficiency of the current subgraph information for transforming a natural language query into a Graph Query Language (GQL) and request higher-order subgraphs when necessary. Experimental results show significant improvements in accuracy on WebQSP and CWQ datasets, with increases of 6.2% and 5.3%, respectively, demonstrating the effectiveness of the approach. Additional validation is provided using models like StructGPT. There are some remaining issues:

While the proposed framework effectively addresses multi-hop queries, the mechanism for determining when additional subgraph information is needed could benefit from more detailed explanation.

The paper could elaborate more on potential limitations of the KGMP module, such as the scalability of the subgraph retrieval process and its impact on performance with very large knowledge graphs. Additionally, what happens when there is no higher-order subgraph available? Does the model still return an answer, or does it fail gracefully?

Missing references:

Think-on-graph 2.0: Deep and interpretable large language model reasoning with knowledge graph-guided retrieval

Retrieval-augmented generation with knowledge graphs for customer service question answering

OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs

Reviewer #2: Some sentences in the abstract were confusing, suggestion of comment attached in the reviewer file comment. Please add the study contributions and potential enhancement for future directions. Please also add discussion findings in the paper.

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Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: ReviewerComment.pdf

KGMP: Augmenting Retrieval Knowledge Graph with Multi-hop Perceptron

PONE-D-24-59861R1

Dear Dr. Chen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Sergio Consoli

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewer #1:

Reviewer #3:

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study proposes Knowledge Graph Multi-hop Perceptron (KGMP) - a retrieval-generation framework fine-tuned based on open-source large language models. The revised version has addressed my concerns.

Reviewer #3: The paper is well-written and tackles an important problem in KBQA.

The proposed framework is clearly described and experimentally validated.

The comparison with existing literature is thorough and useful.

Results show promising improvements over strong baselines.

The work has potential impact in advancing LLM–KG integration.

A few areas (e.g., broader datasets, error analysis, generalizability) could be expanded in future versions

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: Yes: Dalin Almbaidin

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