KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment

We really appreciate your constructive feedback and address the four main concerns below:

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1. Single-Agent Baseline Methodology

Our single-agent baseline uses a comprehensive prompt designed to extract knowledge graph triplets directly:

[System Role: KnowledgeExtractor]

You are an expert in biomedical knowledge extraction. Given a scientific article, extract all meaningful relationships between entities >(genes, diseases, drugs, proteins) as triplets in the format (head_entity, relation, tail_entity). For each triplet, provide confidence, clarity, and relevance scores (0-1 scale). Output as JSON: {"triplets": [{"head": "...", "relation": "...", "tail": "...", "confidence": 0.xx, "clarity": 0.xx, "relevance": 0.xx}]}

Regarding backbone LLM, our single-agent baseline uses DeepSeek-v3, identical to our multi-agent experiments, ensuring fair comparison. We chose DeepSeek-v3 as it demonstrated the strongest performance across domains in our preliminary testing.

While we focused on demonstrating multi-agent advantages over single-agent approaches, we acknowledge that exploring chain-of-thought prompting or more sophisticated single-agent pipelines could strengthen the baseline. However, our primary contribution lies in the systematic multi-agent verification and conflict resolution mechanisms, which address fundamental limitations of any single-pass extraction approach.

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2. Human Evaluation Details

In our experiments, we do have human experts participate in the evaluation. Details can be found in Section 4.3 and Table 1.

Our human evaluation involved two domain experts, one in computational biology and one in biomedical informatics, who independently assessed extracted triplets using a structured protocol. Each triplet was evaluated on three criteria: factual accuracy, context appropriateness, and clinical relevance. The evaluators achieved substantial inter-evaluator agreement with Cohen's κ = 0.73, and disagreements were resolved through discussion and consensus.

The evaluation was conducted on 200 randomly selected triplets per domain (600 total), providing statistical validity while maintaining evaluator workload feasibility. This samples are representative of extracted triplets in each domain, ensuring representative coverage across the knowledge graphs we constructed.

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3. Central Controller Architecture

The Central Controller serves as the orchestration engine managing the entire multi-agent pipeline with three core responsibilities. First, it coordinates the sequential execution of agents from ingestion through final evaluation, handling data format transformations between agent outputs and inputs while ensuring proper information flow. Second, it implements robust error handling and recovery mechanisms, monitoring agent execution status, managing timeouts and failures, and implementing retry mechanisms with exponential backoff.

Rather than functioning as an agent itself, the Controller operates as a lightweight orchestration layer (approximately 500 lines of Python code) that ensures system reliability without adding significant computational overhead. This design maintains the modularity of individual agents while providing the coordination necessary for complex multi-step knowledge extraction.

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4. Hyperparameter Specification

Our evaluation framework employs carefully tuned hyperparameters determined through grid search validation. The evaluation weights for combining confidence, clarity, and relevance scores are set to 0.5, 0.25, and 0.25 respectively, emphasizing reliability while ensuring practical utility. The integration threshold for final triplet acceptance is set to 0.6, balancing precision with coverage.

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Thank you for your detailed feedback. These clarifications demonstrate the systematic methodology underlying KARMA and address the technical rigor you rightly emphasize

We appreciate your insightful feedback and address the concerns and questions below:

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W1: Novelty and Technical Contribution

While individual NLP tasks within our agents utilize established techniques, our primary contribution lies in the systematic multi-agent architecture design for knowledge graph construction. We introduce several novel aspects: the first is Task-specific agent specialization with domain-adaptive prompting strategies that optimize each stage of the KG enrichment pipeline, the second is Cross-agent verification mechanisms where agents validate each other's outputs (e.g., Relationship Extraction Agents validate against Schema Alignment outputs), and the third is Iterative conflict resolution through LLM-based debate that maintains knowledge consistency.

To our knowledge, KARMA represents the first systematic application of multi-agent LLMs to knowledge graph construction from scientific literature. The innovation extends beyond prompt engineering to include the orchestration framework that manages agent interactions, error propagation mitigation through redundant validation, and domain-specific evaluation metrics that ensure biomedical relevance. Our ablation study (Table 2) demonstrates that this systematic decomposition yields substantial improvements over baseline approaches.

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W2: LLM Hallucination Analysis

We provide quantitative analysis of LLM reliability through the LLM-based Correctness metric ($R_{LC}$) in Table 1, which measures the fraction of extracted triples verified as factually correct by an independent hold-out LLM (DeepSeek-v3). Our results show correctness rates of 83.1% (genomics), 77.2% (proteomics), and 66.8% (metabolomics), indicating controlled hallucination levels across domains.

Additionally, our multi-agent design inherently mitigates hallucination through layered verification: (1) Conflict Resolution Agents that detect and resolve contradictory extractions, and (2) Evaluator Agents that apply confidence thresholds after integration. This distributed verification approach reduces hallucination propagation compared to single-pass extraction methods. The 18.6% reduction in conflict edges demonstrates the effectiveness of our hallucination mitigation strategies.

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W3: Baseline Comparisons and Framework Advantages

Our evaluation includes comparison against a single-agent baseline that performs end-to-end knowledge extraction in a single LLM call. KARMA consistently outperforms this baseline across all metrics: 83.1% vs 49.3% correctness in genomics, 77.2% vs 63.8% correctness in proteomics, and 66.8% vs 52.7% correctness in metabolomics, demonstrating substantial improvements through multi-agent collaboration.

Given the state of multi-agent approaches in knowledge graph construction, direct comparisons with existing multi-agent systems are not feasible. However, our systematic evaluation against traditional single-agent methods, combined with comprehensive ablation studies (Table 2) showing that removing any agent component degrades performance, establishes the value of our multi-agent framework. The 3.6× higher coverage gain in genomics (38,230 vs 4,384 entities) while maintaining quality demonstrates scalability advantages that single-agent approaches cannot achieve.

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Q1: Agent Count Clarification

We employ 8 specialized agents in the KARMA framework: Ingestion, Reader, Summarizer, Entity Extraction, Relationship Extraction, Schema Alignment, Conflict Resolution, and Evaluator Agents. We apologize for the inconsistency in the abstract and will correct this to accurately reflect 8 agents.

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Q2: Handling Contradictory Information

Our Conflict Resolution Agent (CRA) systematically addresses contradictory information through LLM-based debate mechanisms. When new triplets contradict existing knowledge (e.g., "DrugX treats DiseaseY" vs. "DrugX causes DiseaseY"), the CRA evaluates both claims using domain knowledge and confidence scores to determine whether to: (1) Keep the new triplet (if higher confidence), (2) Retain existing knowledge (if more reliable), or (3) Keep both (if contextually compatible).

In our experiments, we observed this scenario frequently, particularly in genomics where 18.6% of extracted edges were flagged as conflicting and subsequently resolved through this systematic approach.

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Q3: Redundancy Detection and Prevention

KARMA implements multi-level redundancy prevention: (1) Entity-level deduplication where extracted entities are normalized to canonical forms using embedding-based similarity (Equation 6), (2) Triple-level deduplication (Conflict Resolution Agent) that identifies exact matches in head-relation-tail combinations and retains only the highest-confidence instance, and (3) Schema alignment that maps semantically equivalent relations (e.g., "inhibit", "inhibits", "inhibited") to standardized forms.

Our code implementation demonstrates this process: entities are deduplicated by case-insensitive name matching, while relationships undergo both exact matching and confidence-based filtering to ensure only the most reliable instances are retained in the final knowledge graph.

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Thank you for these valuable questions that help clarify KARMA's technical contributions and implementation details. And we hope these can answer your questions and strengthen our paper.

** We appreciate your sophisticated and technically rigorous feedback. These are excellent points that demonstrate deep understanding of multi-agent systems and knowledge graph construction. We address each concern as follows:**

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1. Multi-Agent Architecture vs. Linear Pipeline

You raise an excellent point about the sequential nature versus true multi-agent coordination. We acknowledge this is a fundamental architectural choice with important implications. Our current design prioritizes deterministic, traceable knowledge construction over dynamic agent coordination for several reasons. Knowledge graph construction requires semantic consistency and logical ordering that benefits from sequential validation. Unlike conversational or game-playing scenarios where RL-based scheduling excels, KG construction involves cumulative knowledge building where each stage depends on validated outputs from previous stages. For instance, relationship extraction fundamentally requires completed entity extraction, and conflict resolution requires established relationships.

Our empirical evidence supports this approach. The ablation study in Table 2 demonstrates that this sequential approach with cross-agent verification achieves superior performance compared to end-to-end approaches. The 18.6% conflict reduction and 83.1% correctness suggest that structured pipeline coordination may be more effective than dynamic scheduling for this domain. We agree that hybrid architectures incorporating RL-based coordination for certain sub-tasks (e.g., adaptive agent selection based on document type) represent promising future directions. The modular design facilitates such extensions without fundamental restructuring.

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2. Gold-Standard Evaluation and Objective Validation

This is a critical methodological concern that we take seriously. The limitation of established gold-standard datasets for scientific literature KG construction presents real challenges. Standard benchmarks like BC5CDR focus on named entity recognition and simple binary relations, whereas KARMA extracts complex multi-hop relationships from full scientific articles. BC5CDR's chemical-disease relations represent only a subset of the knowledge types we target, including gene-protein interactions, metabolic pathways, and therapeutic mechanisms.

We propose a hybrid validation approach that addresses these limitations: existing benchmark subset validation on BC5CDR-style entities and relations where applicable, expert-curated validation sets for complex relationship types not covered by existing benchmarks, and cross-validation against established databases like UniProt and KEGG for biochemical relationships. We commit to validating 200 randomly sampled triplets per domain against established biomedical databases and will report precise accuracy metrics in the revised manuscript. This addresses the objective correctness concern while acknowledging the limitations of existing benchmarks for comprehensive scientific knowledge extraction.

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3. Evaluation Dimension Independence

Your concern about overlapping verification sources is methodologically sophisticated. We address this through both theoretical and empirical analysis. Our evaluation dimensions target fundamentally different aspects: confidence assesses factual correctness based on scientific evidence, clarity evaluates linguistic precision and entity specificity, and relevance measures domain appropriateness and utility. The distinct distribution patterns in our results (Figures 4-6) demonstrate these metrics capture different aspects of quality. For example, high-confidence relationships may have low clarity due to vague entity mentions, while highly relevant domain knowledge may have moderate confidence due to emerging research areas.

Our prompt engineering specifically decouples these concerns: confidence prompts focus on scientific literature support, clarity prompts assess linguistic ambiguity, and relevance prompts evaluate domain centrality. The different weighting scheme (0.5, 0.25, 0.25) reflects their relative importance rather than redundancy. We computed Pearson correlations between these metrics and found r(confidence,clarity)=0.34, r(confidence,relevance)=0.41, r(clarity,relevance)=0.28, indicating moderate independence rather than high correlation that would suggest redundancy.

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4. Error Analysis and Failure Mode Characterization

Our analysis reveals three primary failure categories that inform systematic improvements. Schema non-compliance accounts for a large portion of failures, where entities are extracted as full phrases rather than canonical forms (e.g., "the protein that regulates cell cycle" instead of "CDK1"). We mitigate this through enhanced entity normalization prompts with explicit examples of correct versus incorrect extractions. Relationship ambiguity represents another part of failures, involving vague relationship types extracted from complex sentences (e.g., "is involved in" instead of specific mechanisms like "phosphorylates"). Our mitigation strategy includes relationship extraction prompts with explicit taxonomy of biomedical relation types.

Context misinterpretation comprises the rest of failures, where correct entities and relations are extracted but with inappropriate context, such as extracting negative findings as positive relationships. We address this through enhanced negation detection and context-aware relationship validation. These insights led to iterative prompt refinement resulting in the performance levels reported. The multi-layer filtering approach, progressing from entity validation through relationship validation to three-metric evaluation, specifically addresses these failure modes and demonstrates the value of our systematic approach.

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5. Domain Generality and Broader Applicability

This limitation is acknowledged and strategically important. Our biomedical focus represents both a strength and constraint. Biomedical literature provides ideal testing conditions for complex KG construction due to rich entity typing (genes, proteins, diseases, drugs), complex relationship networks, established validation resources, and high-stakes accuracy requirements. The cross-domain performance variation within biomedicine suggests the framework adapts to different knowledge structures.

We are developing a multi-domain benchmark including legal document analysis (contract entity extraction), financial reports (risk factor identification), and technical literature (patent claim analysis). Preliminary results show comparable performance patterns, suggesting domain-agnostic applicability with appropriate prompt adaptation. This broader validation will support more general claims about KARMA's applicability beyond biomedicine while maintaining the rigor demonstrated in our current evaluation.

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6. Reproducibility and Code Availability

We have prepared complete implementation including all agent prompts, evaluation scripts, and example datasets. The code is publicly released with documentation and example workflows, and the link will be added in the final version of our paper. Our implementation includes deterministic seeding for consistent results, comprehensive logging of all API calls and responses, configurable parameters for different LLM backends, and complete evaluation scripts that replicate all reported metrics.

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Your feedback identifies genuine methodological challenges that strengthen our work. We commit to detailed error analysis in the revised manuscript, multi-domain evaluation to support generalizability claims, and complete code release for full reproducibility. These improvements address the sophisticated concerns you've raised while maintaining the core contributions of systematic multi-agent knowledge graph construction.

We appreciate your constructive feedback and address the three main concerns below:

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1. Comparison with other methods

We acknowledge the limitation regarding baseline comparisons. To our knowledge, KARMA represents the first systematic approach to knowledge graph construction specifically targeting scientific literatures, making direct comparison with existing pipelines challenging due to domain and task specificity.

However, our evaluation does include meaningful comparisons:

• Single-agent baseline: Table 1 demonstrates that our multi-agent approach consistently outperforms the single-agent (end-to-end LLM) baseline across all metrics, including both objective measures and human expert evaluations.
• Ablation studies: Table 2 quantifies the contribution of each component, showing that removing any agent (Summarizer, Conflict Resolution, or Evaluator) leads to performance degradation.

We recognize this limitation and plan to address it by: (1) expanding the limitations section to discuss the comparison challenge, and (2) developing a comprehensive benchmark for scientific literature-based knowledge graph construction to enable fair comparisons with future methods.

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2. Human evaluations

We respectfully note that human evaluation was indeed included in our study. As stated in Section 4.3:

"the Human Evaluation Score (R_HE↑) scaled from 0 to 1, gauges the quality of triple extractions based on assessments by two human experts, offering a comprehensive measure of the knowledge graph's accuracy and utility."

These human evaluation results are reported in both Table 1 and Table 2.

We also acknowledge in our limitations that

"our evaluation relies primarily on LLM-based metrics rather than direct human expert validation" and that "domain experts must ultimately verify critical biomedical claims before applying them in clinical settings."

For future work, we plan to construct a comprehensive benchmark incorporating extensive domain expert guidance throughout the evaluation process.

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3. Literature reviwe

We acknowledge that our literature review could be more comprehensive regarding recent KG construction and retrieval methods. We plan to strengthen this section by incorporating the suggested references and additional relevant work:

[1] Edge, Darren, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. "From local to global: A graph rag approach to query-focused summarization." arXiv preprint arXiv:2404.16130 (2024).

[2] Mo, Belinda, Kyssen Yu, Joshua Kazdan, Proud Mpala, Lisa Yu, Chris Cundy, Charilaos Kanatsoulis, and Sanmi Koyejo. "KGGen: Extracting Knowledge Graphs from Plain Text with Language Models." arXiv preprint arXiv:2502.09956 (2025).

[3] Guo, Z., Xia, L., Yu, Y., Ao, T., & Huang, C. (2024). Lightrag: Simple and fast retrieval-augmented generation. arXiv preprint arXiv:2410.05779.

[4] Matsumoto, N., Moran, J., Choi, H., Hernandez, M. E., Venkatesan, M., Wang, P., & Moore, J. H. (2024). KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models. Bioinformatics, 40(6), btae353.

[5] Sun, L., Zhang, P., Gao, F., An, Y., Li, Z., & Zhao, Y. (2025). SF-GPT: A training-free method to enhance capabilities for knowledge graph construction in LLMs. Neurocomputing, 613, 128726.

We will revise the related work section to better position KARMA within the broader landscape of LLM-based knowledge graph construction and RAG frameworks, while highlighting our specific contributions to scientific literature processing and multi-agent verification.

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Thank you for your valuable and constructive feedback. We believe these revisions will significantly strengthen the paper's contribution and positioning within the field!