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STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking

Tek Raj Chhetri, Yibei Chen, Puja Trivedi, Dorota Jarecka, Saif Haobsh, Patrick Ray, Lydia Ng, Satrajit S. Ghosh · Jul 4, 2025 · Citations: 0

Automatic Metrics
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Extracting structured information from scientific literature is critical for accelerating discovery, yet Large Language Models (LLMs) often struggle in specialized domains that require expert knowledge and generalize poorly across tasks. We introduce \textsc{StructSense}, a modular, task-agnostic, open-source framework that integrates ontology-guided symbolic knowledge, agentic self-evaluative refinement, and human-in-the-loop validation for robust domain-aware extraction. We evaluate \textsc{StructSense} on three tasks of increasing semantic complexity: schema-based extraction of assessment instruments (91--100\% accuracy), metadata and resource extraction from scientific papers (86--93\% overall), and named entity recognition (NER) from neuroscience literature (58--75\% label accuracy across 8,882 entities). On two biomedical NER benchmarks (NCBI Disease and S800 Species), the system achieves $\geq$90\% relaxed recall and 62.5--85.8\% strict recall while extracting 1,000--3,600 additional entities beyond gold annotations. The local concept mapping service achieves Hits@1 of 62--82\% under strict matching and 68--86\% under semantic matching. These results across three domains demonstrate that \textsc{StructSense} generalizes across tasks while maintaining source grounding and provenance transparency.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 35%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Extracting structured information from scientific literature is critical for accelerating discovery, yet Large Language Models (LLMs) often struggle in specialized domains that require expert knowledge and generalize poorly across tasks."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Extracting structured information from scientific literature is critical for accelerating discovery, yet Large Language Models (LLMs) often struggle in specialized domains that require expert knowledge and generalize poorly across tasks."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Extracting structured information from scientific literature is critical for accelerating discovery, yet Large Language Models (LLMs) often struggle in specialized domains that require expert knowledge and generalize poorly across tasks."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Extracting structured information from scientific literature is critical for accelerating discovery, yet Large Language Models (LLMs) often struggle in specialized domains that require expert knowledge and generalize poorly across tasks."

Reported Metrics

partial

Accuracy, Recall

Useful for evaluation criteria comparison.

"We evaluate \textsc{StructSense} on three tasks of increasing semantic complexity: schema-based extraction of assessment instruments (91--100\% accuracy), metadata and resource extraction from scientific papers (86--93\% overall), and named entity recognition (NER) from neuroscience literature (58--75\% label accuracy across 8,882 entities)."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Extracting structured information from scientific literature is critical for accelerating discovery, yet Large Language Models (LLMs) often struggle in specialized domains that require expert knowledge and generalize poorly across tasks."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Expertise required: Medicine

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

accuracyrecall

Research Brief

Metadata summary

Extracting structured information from scientific literature is critical for accelerating discovery, yet Large Language Models (LLMs) often struggle in specialized domains that require expert knowledge and generalize poorly across tasks.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Extracting structured information from scientific literature is critical for accelerating discovery, yet Large Language Models (LLMs) often struggle in specialized domains that require expert knowledge and generalize poorly across tasks.
  • We introduce \textsc{StructSense}, a modular, task-agnostic, open-source framework that integrates ontology-guided symbolic knowledge, agentic self-evaluative refinement, and human-in-the-loop validation for robust domain-aware extraction.
  • We evaluate \textsc{StructSense} on three tasks of increasing semantic complexity: schema-based extraction of assessment instruments (91--100\% accuracy), metadata and resource extraction from scientific papers (86--93\% overall), and named entity recognition (NER) from neuroscience literature (58--75\% label accuracy across 8,882 entities).

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • We introduce StructSense, a modular, task-agnostic, open-source framework that integrates ontology-guided symbolic knowledge, agentic self-evaluative refinement, and human-in-the-loop validation for robust domain-aware extraction.
  • We evaluate StructSense on three tasks of increasing semantic complexity: schema-based extraction of assessment instruments (91--100\% accuracy), metadata and resource extraction from scientific papers (86--93\% overall), and named entity…
  • On two biomedical NER benchmarks (NCBI Disease and S800 Species), the system achieves \geq90\% relaxed recall and 62.5--85.8\% strict recall while extracting 1,000--3,600 additional entities beyond gold annotations.

Why It Matters For Eval

  • We introduce StructSense, a modular, task-agnostic, open-source framework that integrates ontology-guided symbolic knowledge, agentic self-evaluative refinement, and human-in-the-loop validation for robust domain-aware extraction.
  • On two biomedical NER benchmarks (NCBI Disease and S800 Species), the system achieves \geq90\% relaxed recall and 62.5--85.8\% strict recall while extracting 1,000--3,600 additional entities beyond gold annotations.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: accuracy, recall

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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