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arxiv: 2605.08222 · v1 · submitted 2026-05-06 · 💻 cs.CV · cs.AI· cs.IR

Recognition: 2 theorem links

· Lean Theorem

From Historical Tabular Image to Knowledge Graphs: A Provenance-Aware Modular Pipeline

Sarah Binta Alam Shoilee , Victor de Boer , Jacco van Ossenbruggen , Susan Leg\^ene
Authors on Pith no claims yet

Pith reviewed 2026-05-12 00:45 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.IR
keywords handwritten archival tablesknowledge graphsdata provenancemodular pipelinehistorical datatable reconstructionimage to KG
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The pith

A modular pipeline with systematic origin tracking converts handwritten archival table images into traceable knowledge graphs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out a workflow that splits the conversion of handwritten historical tables into knowledge graphs into three separate stages so each can be checked and fixed independently. It adds data provenance at every step so that every entity and value in the final graph can be traced back to specific locations in the original image. This structure is meant to replace opaque end-to-end AI systems with one that supports ongoing human inspection and correction. The method is shown on real military career records, where different table reconstruction approaches can be swapped in and evaluated without rebuilding the whole system.

Core claim

The central claim is that a three-stage modular pipeline consisting of table reconstruction, information extraction, and KG construction, when combined with systematic data provenance that keeps every extracted entity and literal linked to its visual and textual source, produces transparent and collaboratively controllable image-to-KG conversions for complex handwritten archival material.

What carries the argument

The three-stage modular pipeline with integrated data provenance, which decomposes the workflow into inspectable steps and maintains traceability from original image pixels to final graph elements.

If this is right

  • Different table reconstruction algorithms can be inserted and compared while keeping the rest of the extraction and graph-building stages unchanged.
  • Every node and literal in the final knowledge graph carries explicit links to its source region in the original handwritten image.
  • The separation of stages reduces the risk that an error in one part of the process remains hidden inside a single black-box model.
  • Experiments on military career tables show that modularity allows targeted evaluation of each reconstruction variant on real archival images.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same staged approach with provenance could be tested on other historical document formats such as handwritten ledgers or correspondence.
  • Provenance records could be used to generate automatic confidence scores or to route uncertain items to human reviewers.
  • The pipeline design suggests a template for making other image-to-structured-data tasks in cultural heritage more auditable.

Load-bearing premise

Making intermediate representations visible and attaching origin information to every data item will be enough for humans to perform meaningful oversight, evaluation, and correction in practice.

What would settle it

A controlled user study in which participants supplied with the intermediate outputs and provenance links still fail to detect or correct a measurable fraction of extraction errors in the resulting knowledge graphs would show the approach does not deliver the claimed collaborative control.

Figures

Figures reproduced from arXiv: 2605.08222 by Jacco van Ossenbruggen, Sarah Binta Alam Shoilee, Susan Leg\^ene, Victor de Boer.

Figure 1
Figure 1. Figure 1: Illustrative architecture of our provenance-aware pipeline. Blue elements correspond to core com￾ponents, yellow elements to operations within each component, purple shapes to input resources, and green shapes to generated outputs. Solid arrows indicate the direction of data flow throughout the pipeline and dotted lines visually illustrates some of the intermediate outputs. 2 Image NL-HaNA 2.10.50 45 0143 … view at source ↗
Figure 2
Figure 2. Figure 2: Automatically detected cell bounding box visualisation of image NL-HaNA 2.10.50 45 0143 ing the textual content. More details on how these metrics can be implemented in practice are provided in the Appendix A with the illustrative running example. 3.1.2. Implementation Specification To demonstrate the possibility of implementing different approaches, we implemented three table reconstruction variants drawn… view at source ↗
Figure 3
Figure 3. Figure 3: On the right, the image displays a snippet of the structured information extracted from the HTML table (in the left), together with its corresponding cell- and text-level provenance, where available. Note that, some cell provenance is lost when the extracted information cannot be directly (string) matched to the original cell text as explained in Section 3.2 [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Snippet of assertion graph for image NL-HaNA 2.10.50 45 0143 URIs, assigning entity types, and mapping property assertions according to the schema. To maintain traceability, every triple is also added to three provenance-specific named graphs: row-level (e.g., :prov row 1), cell-level (e.g., :prov cell 12), and text-span (e.g., :prov span 241 250). Each links the assertion to its corresponding row, cell, a… view at source ↗
Figure 5
Figure 5. Figure 5: Snippet of provenance graph for image NL-HaNA 2.10.50 45 0143 3.3.1. Evaluation Metrics As the assertion graph is constructed via a direct mapping from the structured informa￾tion produced by the previous component, we assume that precision, recall, and F1-score directly reflect the semantic correctness of the resulting assertion graph. The consistency of the provenance graph is evaluated using SHACL shape… view at source ↗
Figure 6
Figure 6. Figure 6: Event extraction from a degraded handwritten military career register using proposed pipeline and human correction. (a) Predicted cell bounding boxes (maP=0.1), (b) Reconstructed HTML table (with TED=0.549, TED-struct=0.622), (c) Human-corrected HTML, (d) Pipeline-extracted events (e) Human-cor￾rected events, Military biography reconstruction offers an important use-case: tracing personnel movements across… view at source ↗
Figure 7
Figure 7. Figure 7: Predicted Cell Bounding Box [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ground truth Cell Bounding Box [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Predicted HTML table [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Ground truth HTML table B. Information Extraction Evaluation Metrics For the evaluation of the Information Extraction component, we compute Precision, Re￾call, and F1-score for 1 by comparing it against 2. We first apply the Hungarian algorithm [35] to obtain an optimal one-to-one align￾ment between the predicted and gold-standard person entities. For each aligned entity pair, corresponding property value… view at source ↗
read the original abstract

Handwritten archival tables contain rich historical information, yet transforming them into structured representations, such as Knowledge Graphs, requires integrating table structure recognition, handwriting recognition, and semantic interpretation - a complex multimodal process. End-to-end AI implementations can obscure these steps, resulting in opaque algorithmic operations that hinder human oversight, critical assessment, and trust. To address this, we present a modular, provenance-aware pipeline to convert handwritten tabular images into KGs supporting human-AI collaboration. The pipeline decomposes the workflow into three stages - table reconstruction, information extraction, and KG construction - while exposing intermediate representations for inspection, evaluation, and correction. A key contribution of our approach is the systematic integration of data provenance at every stage, ensuring that all extracted entities and literals remain traceable to their visual and textual origins. The proposed pipeline is demonstrated through a number of experiments on real-world archival material concerning military careers. The results across three different table reconstruction variants highlight the importance of modularisation. By coupling modularity with data provenance, our work advances transparent and collaboratively controllable image-to-KG pipelines for complex historical data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents a modular, provenance-aware pipeline that decomposes handwritten tabular image processing into table reconstruction, information extraction, and knowledge graph construction stages. It integrates systematic data provenance to expose intermediate representations for human inspection, evaluation, and correction, and demonstrates the approach on real-world military-career archival tables using three table-reconstruction variants to illustrate the value of modularity.

Significance. A well-executed demonstration of this pipeline could advance transparent, human-AI collaborative systems for historical document digitization by making complex multimodal workflows auditable and correctable. The conceptual integration of provenance with modularity addresses a genuine need in archival AI, but the manuscript's contribution remains primarily architectural rather than empirically validated on the oversight benefits.

major comments (2)
  1. [experimental demonstration / results on archival material] The experimental demonstration on military-career archives compares three table reconstruction variants and concludes that modularity matters, yet reports no quantitative metrics, user studies, correction-success rates, time-to-fix measurements, or ablation comparing provenance-enabled versus provenance-free versions. This leaves the central claim that 'coupling modularity with data provenance' produces 'collaboratively controllable' pipelines untested and unsupported by evidence.
  2. [pipeline description and abstract] The abstract and pipeline description assert that exposing intermediates and provenance traces enables 'meaningful human oversight, evaluation, and correction,' but no protocols, interfaces, or evaluation criteria for human correction are specified or tested. This assumption is load-bearing for the paper's contribution yet remains a design hypothesis rather than a demonstrated outcome.
minor comments (2)
  1. [abstract] The abstract would be strengthened by including at least high-level quantitative indicators (e.g., reconstruction accuracy or entity extraction F1) from the three variants if they exist in the full manuscript.
  2. [pipeline overview] Figure or diagram clarity: a visual overview of the three stages with provenance arrows would help readers trace how entities remain linked to visual origins.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for stronger empirical grounding of the pipeline's oversight benefits. We address each major comment below, acknowledging the primarily architectural nature of the contribution while outlining targeted revisions to improve clarity and framing.

read point-by-point responses

  1. Referee: The experimental demonstration on military-career archives compares three table reconstruction variants and concludes that modularity matters, yet reports no quantitative metrics, user studies, correction-success rates, time-to-fix measurements, or ablation comparing provenance-enabled versus provenance-free versions. This leaves the central claim that 'coupling modularity with data provenance' produces 'collaboratively controllable' pipelines untested and unsupported by evidence.

    Authors: We agree that the experiments provide a qualitative demonstration on real archival material rather than quantitative metrics, user studies, or ablations of provenance-enabled versus free variants. The manuscript uses the three variants to illustrate how modularity supports different reconstruction strategies with full traceability, but does not empirically measure oversight improvements such as correction success or time savings. In revision we will add a limitations subsection explicitly stating the current evaluation's scope, include concrete examples of provenance traces supporting inspection and correction, and note that controlled user studies remain future work. This will better contextualize the architectural contribution without overstating empirical validation. revision: partial

  2. Referee: The abstract and pipeline description assert that exposing intermediates and provenance traces enables 'meaningful human oversight, evaluation, and correction,' but no protocols, interfaces, or evaluation criteria for human correction are specified or tested. This assumption is load-bearing for the paper's contribution yet remains a design hypothesis rather than a demonstrated outcome.

    Authors: The current manuscript describes the exposure of intermediates and provenance for inspection but does not detail specific correction protocols, interfaces, or evaluation criteria. We accept that this leaves the oversight claim as a design hypothesis. We will revise the abstract and pipeline section to use more precise language stating that the pipeline provides traceable intermediates that support human oversight, rather than asserting it directly enables 'meaningful' correction. We will also add a short subsection outlining example correction workflows that leverage the provenance links, thereby making the hypothesis more concrete as a design proposal. revision: yes

Circularity Check

0 steps flagged

No significant circularity; architectural description with empirical demonstration on real data

full rationale

The paper presents a modular pipeline decomposing image-to-KG conversion into table reconstruction, information extraction, and KG construction stages, with systematic provenance tracking. It demonstrates the approach via experiments comparing three table-reconstruction variants on military-career archives, concluding that modularity matters. No equations, first-principles derivations, fitted parameters, or predictions appear in the text. The central claim (coupling modularity with provenance advances transparent, controllable pipelines) is a design assertion supported by the described architecture and reconstruction results, without reducing to self-definition, self-citation chains, or renaming of known results. The untested assumption regarding human oversight is a limitation of evidence strength, not a circular reduction of any derivation to its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on standard assumptions from computer vision (table structure recognition is feasible) and knowledge representation (entities and relations can be extracted and linked). No free parameters, ad-hoc axioms, or invented entities are introduced in the abstract.

pith-pipeline@v0.9.0 · 5507 in / 1125 out tokens · 37599 ms · 2026-05-12T00:45:34.579098+00:00 · methodology

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Reference graph

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