# An end-to-end pipeline for knowledge graph population from 19th-century land registry digitised tables

Solenn Tual, Nathalie Abadie, Joseph Chazalon, Bertrand Dumenieu, Julien Perret

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Solenn Tual, Nathalie Abadie, Joseph Chazalon, Bertrand Dumenieu, Julien Perret. An end-to-end pipeline for knowledge graph population from 19th-century land registry digitised tables. 29th International Conference on Theory and Practice of Digital Libraries (TPDL25), Sep 2025, Tampere, Finland. hal-05118320

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<figure><img src="image_1.png" /></figure>
# An end-to-end pipeline for knowledge graph population from 19th-century land registry digitised tables

Solenn Tual¹[0000-0001-8549-7949], Nathalie Abadie¹[0000-0001-8741-2398], Joseph Chazalon²[0000-0002-3757-074X], Bertrand Duménieu³[0000-0002-2517-2058], and Julien Perret²[0000-0002-0685-0730]

¹ LASTIG, Univ. Gustave Eiffel, IGN-ENSG, Champs-sur-Marne, France
[solenn.tual,nathalie-f.abadie,julien.perret]@ign.fr

² EPITA Research Laboratory (LRE), Le Kremlin-Bicêtre, France
[joseph.chazalon@epita.fr]

³ CRH-EHESS, Paris, France
[bertrand.dumenieu@ehess.fr]

**Abstract.** Historical tables, such as administrative registers, represent vast and valuable sources of information for researchers. However, despite large-scale digitization efforts, extracting and structuring their content remains challenging. The French 19th-century Land Registry is a notable example: rich in detailed land use information, yet highly heterogeneous, and still largely underexploited. Although recent deep learning methods have improved information extraction (IE) from digitised documents, they often lack semantic structuring. Conversely, Semantic Table Interpretation (STI) techniques, mostly applied to natively digital tables, offer structuring and linking capabilities but are rarely used on historical sources. In this work, we propose a pipeline that combines deep learning-based IE with STI, guided by a domain ontology. The approach produces a knowledge graph that enables querying and exploration of historical records. We evaluate the resulting knowledge graph using several metrics, demonstrating the potential of our method for semantic enrichment of historical data.

**Keywords:** Information Extraction · Semantic Interpretation of Tables · Knowledge Graph · Historical Document Analysis.

## 1 Introduction

Archives are filled with documents containing tables, such as administrative registers. These constitute vast corpora of semi-structured information of great interest to researchers in various fields such as history or geography. Although historical tables are increasingly being digitised on a large scale by GLAMs<fnref n="4" />, information retrieval from them remains complex and requires handcrafted work.

<footnote n="4">⁴ Galleries, Libraries, Archives, Museums</footnote>
The French 19th-century Land Registry (also called the *Napoleonic Land Registry*) is a characteristic example of such historical archival corpora that would greatly benefit from large-scale extraction and dissemination. Established in the early 19th-century, it contains detailed information about land use and land cover changes at the plot scale across the entire country. Although the initial plot geometries are depicted on maps, detailed historical knowledge, including landmark's evolution, is described in registers whose structure and content vary between departments<fnref n="5" /> and over time. It was used until the middle of the 20th-century (from 1930 to 1980 depending on the municipality). Some exploitation of these registers has been performed, but the spatial and temporal depth of these studies is often limited and always has required costly manual work [23,14,18].

Recent advances in deep learning have opened new possibilities for information extraction (IE) from such digitised historical sources [2,6], including text recognition and layout analysis. However, most processing pipelines rarely perform finer structuring steps or linking between automatically extracted entities. On the other hand, semantic table interpretation (STI) is a field of the Semantic Web mainly applied to annotate natively digital format tables (e.g., HTML, CSV) using generic knowledge graphs (e.g., Wikidata, DBpedia). It helps in structuring data and establishing meaningful links between different table values. STI is rarely applied to domain-specific tables [19] and even less so to historical tables. However, existing works [22] show that populating knowledge graphs using automatically extracted content from historical documents is possible and opens up new possibilities for research on them. Thus, by combining deep learning techniques with semantic technologies, it becomes possible to generate RDF resources, enabling the structuring, enrichment, and querying of the extracted information from historical tables such as the Napoleonic Land Registry.

After presenting the land registry tables (Section 2), we survey existing work on information extraction from historical tables, semantic table interpretation, and automated knowledge graph population from historical documents (Section 3). Then, we present the complete processing pipeline (Section 4) whose

<figure><img src="image_2.png" /><figcaption>Fig. 1: Our end-to-end processing pipeline for knowledge graph population from digitised historical tables. The domain ontology guides information extractions steps, and is finally enriched by the KG population step.</figcaption></figure>

<footnote n="5">⁵ In France, the departments are the administrative units just above the municipalities.</footnote>
goal is to populate a knowledge graph using tables from the 19th-century French Land Registry (as described in Figure 1), including image annotation and semantic table interpretation methods that handle noisy text. Both steps are driven by a domain ontology dedicated to the description of landmarks and addresses evolution at the attribute scale. Finally, we present the implementation details of the pipeline applied to the Val-de-Marne land registry as well as the graph evaluation (Section 5).

Code, datasets and extra material are available at the following URL: https://github.com/solenn-tl/landRegistry_tables_processing_TPDL_2025.

## 2 The 19th-century French Land Registry: description and critical analysis

The French land registry, known as the *Napoleonic Land Registry*, was established in 1809 under the First Empire to improve land tax assessment and distribution. By 1850, most French municipalities had such a registry, composed of maps and registers.

There are two map types: a municipal map (scale 1:10,000) showing section boundaries (identified by letters), and index maps (scale 1:1,000-1:5,000) detailing plots within each section, each plot assigned a unique number. These maps were static and not updated, new plots from divisions or mergers retained existing numbers in registers, even if they no longer represented the same entity.

Two types of registers complement the maps. The initial register (*états de sections*) describes each plot at the time of mapping, grouped by section (as chapters), with a table listing the plots in ascending order. The mutation register (*matrice cadastrale*) tracks plot changes over time, grouping them by taxpayer. Each change generates a new row, with old ones crossed out. Changes in taxpayer property accounts can be traced using account numbers.

Produced and managed by departments, 19th-century land registry documents are now held by Departmental and sometimes Municipal Archives. These records form a large-scale source for studying land use, building history, and ownership evolution. Despite digitization, information retrieval is complex, involving manual navigation across document types, and large-scale extraction remains challenging [23,14,18]. A crowd-sourced transcription project has been initiated<fnref n="6" />. Existing automated approaches focus mainly on map vectorization [13,25], with no known work on automated knowledge extraction from the registers.

This work focuses on land registry documents digitized by the Val-de-Marne Departmental Archives<fnref n="7" />, which covers 47 municipalities. The corpus originates from the former Seine and Seine-et-Oise departments, split and reorganized in 1968. In May 2025, 820 map images, 24,012 initial register images, and 158,677 mutation register images were digitized. This article focuses on initial registers, though the proposed method is designed to be generalized to mutation registers and other departments.

<footnote n="6">⁶ https://archives.vendee.fr/participer/reconstituez-les-paysages</footnote>

<footnote n="7">⁷ https://archives.valdemarne.fr</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 2: Two different pre-printed tables extracted from the initial registers of Val-de-Marne Archives. (A) Municipality of Marolles-en-Brie (1810) and (B) Municipality of L'Hay (1842). Columns are colored by type of information: plot address (blue), plot number (green), taxpayer (red), nature (purple), taxpayer index number (yellow) which was added by writers in B but is absent in A.</figcaption></figure>

Initial registers describe municipal plots at a single point in time. Their structure, pre-printed by the department and manually filled, is divided into chapters by section. Tables often repeat information (e.g., taxpayers, addresses, land type), and resemble relational tables but include many manual exceptions (merged cells, brace-distributed values). Repeated data are sometimes replaced with variants of *idem* (e.g., “id”, "", or column-wide vertical marks).

The Val-de-Marne digitized registers exhibit variations in pre-printed table formats—column order, titles, presence, and content (e.g., taxpayer details) differ between municipalities (see Figure 2). The proposed pipeline aims to robustly extract and structure data across such layout differences.

# 3 Related Works

In this section, we review three areas that are complementary to the goal of building a knowledge graph from historical tables: information extraction from historical tables, semantic table interpretation using knowledge graphs, and approaches to populating knowledge graphs from historical documents.

## 3.1 Information Extraction applied to historical tables

Information extraction from digitised documents aims to locate, transcribe, and organise the text they contain into a structured computer-readable representation. There are two main types of information extraction system. Traditional approaches consist of a sequence of independent models chained together, and *end-to-end* approaches where the same model performs several steps at once.
This field is making significant progress thanks to advances in deep learning. For these two types of approach, the regions containing the tables are generally isolated beforehand using a page classification or region detection system and might include a step of image preprocessing (segmentation of double-pages into pages, straightening).

*Constum et al.* [5] detect the table on the page, segment the rows and transcribe them using a HTR model [7]. A special character is used to implicitly materialise the separation of the row into cells. The nature of each cell is then deduced from its position in the row. This is possible because the order of the columns is known *a priori* by the authors and the structure of all the registers considered does not vary. *Petit-Pierre et al.* [24] propose to detect the columns and segments of text on the page and then group these segments into rows *a posteriori*. The text is transcribed using the HTR engine proposed by *Puigcerver* [26]. *Granell et al.* [15] deal with weather records recorded in navigation logs. The row segmentation task is performed by a pixel classification model [27]. Text transcription is performed at the row level using a *Convolutional Recurrent Neural Networks* (CRNN) model.

The main drawback of approaches involving prior segmentation of documents is that errors in dividing pages images into rows or columns are propagated to subsequent stages. Furthermore, once the segmentation has been carried out, the models can only rely on a reduced visual and textual context. Evaluation datasets must be produced for each stage (zone detection, text recognition).

*Boilet et al.* [2] propose a large-scale pipeline to process the 19th-century census registers of various French departments. The content of each page is transcribed in a structured way using the Document Attention Network (DAN) model [8], a convolutional encoder followed by a Transformer [32] decoder. This approach enables information of the same type to be retrieved without segmenting the image or materialising the exact position of rows and columns. The annotations are based on a ‘key-value’ model: for each area (row), the list of information to be extracted (corresponding to columns or named entities) is defined.

In parallel with approaches specialising in a particular type of document, some approaches such as DONUT [17] have aimed to offer models trained in a large number of tasks in order to be able to adapt quickly to new problems or document types. These approaches have been improved with the use of large language models (LLM) in the construction of vision-language models (VLLM), which then have superior reasoning capabilities, but so far show limited performance in the analysis of modern tables, as noted by *Scius-Bertrand et al.* [29].

## 3.2 Semantic Interpretation of Tables

Semantic Table Interpretation (STI) involves annotating table elements (rows, columns, cells, or entire tables) and their relationships using a knowledge graph (KG) to enhance semantic understanding. Applications include information retrieval, question answering, data enrichment, and KG population. Interest in
STI has grown, especially through the SemTab competition<fnref n="8" />, which introduced
standardized tasks and evaluation protocols.

Research mainly targets CSV or HTML tables—native numerical formats—as
reviewed by Liu et al. [19] and Cremashi et al. [9]. The former offers a taxonomy
and benchmarks up to 2021; the latter extends them through 2024, incorporating
LLM-based methods and refining STI subtasks. The main tasks include:

- **Column Type Annotation (CTA)**: assigns types (entity classes or datatypes) to columns [9].

- **Column Property Annotation (CPA)**: links column pairs through KG properties.

- **Cell Entity Annotation (CEA)**: maps the cell content to the KG entities.

- **Cell-New Entity Annotation (CNEA)**: detects mentions of entities absent from the KG [21].

- **Thematization**: associates the entire table with a KG concept.

- **Row-to-Instance Annotation**: assigns a KG entity to each row as its subject.

CEA resembles Entity Linking in NLP, while CTA and CPA align table struc-
ture with ontologies [9]. Typical pipelines include: data preparation (formatting,
cleaning, normalization) [4,11], column classification [34], annotation of literal
columns, subject column detection [3], and entity resolution (CEA and CNEA)
using mention detection, candidate retrieval, and disambiguation (often with
NER). Then, the entity types and intercolumn relations are predicted.

Approaches vary from heuristics (e.g. similarity measures, TF-IDF, voting),
to feature engineering (traditional ML using lexical/statistical features), and
deep learning (embedding-based models of table elements and KG entities).

Most of the STI works focus on simple relational tables. Complex formats,
especially historical tables with nested or composite subjects, remain underex-
plored. General KGs such as Wikidata [33], DBpedia [20], and YAGO [30] are
often used but lack coverage for domain-specific or historical data. Challenges
include KG incompleteness, detection of NIL entities [9], and effective use of
table metadata and context [19].

3.3 Knowledge Graph Population Using Data Extracted from
Historical Documents

Currently, no studies jointly perform information extraction (IE) and semantic
interpretation of historical tables. However, various approaches have been devel-
oped to extract information from historical documents and populate knowledge
graphs (KGs) based on domain-specific ontologies. These methods target diverse
sources, including trade directories [31], newspapers, scientific publications [22],
notarial registers [12], and heterogeneous thematic corpora [16,10]. Few ap-
proaches offer a full pipeline from images to the KG population [31], including
segmentation and OCR transcription. Most focus on later stages — extracting

<footnote n="8">⁸ https://www.cs.ox.ac.uk/isg/challenges/sem-tab/</footnote>
structured knowledge from pretranscribed texts [22,16,10], whether transcribed manually or automatically. These methods apply a variety of NLP techniques such as keyphrase identification, Named Entity Recognition (NER) [31,22], Relation Extraction (RE), Entity Linking (EL), and coreference resolution to build triplets. For NER, both fine-tuned [31,22] and off-the-shelf models [16,10] are used to detect entity mentions in full texts or key sentences [22]. Dependency parsing helps identify relations between entities or attributes [22]. Often, entities and relations are aligned with domain ontologies [31,22,10]. In contrast, Open Information Extraction (OIE) approaches discover them without predefined schemas [16]. The knowledge extracted serves to initialize or enrich KGs. Generalist KGs (e.g., Wikidata, DBPedia) can complement domain-specific data but lack granularity. While Large Language Models (LLM) show promise for entity and relation extraction, their tendency to hallucinate can reduce output quality [10]. Fine-tuning pre-trained language models on domain-annotated corpora offers the best trade-off between accuracy and reliability.

# 4 End-to-end pipeline for historical ontology population

In this section, we present the pipeline developed to generate a knowledge graph from the 19th-century French Land Registry's initial registers. At the core of this pipeline is the PeGaZus ontology [1], designed to describe geographical features — represented as the Landmark class in the ontology — and their evolution over time at the attribute level. It defines concepts and properties related to land plots. The ontology is coupled with a geohistorical knowledge graph population algorithm that handles heterogeneous and fragmentary references to landmarks from diverse sources. The concepts of the PeGaZus ontology are applied throughout the pipeline, from image annotation to knowledge graph population.

## 4.1 Pre-processings

**Registers collection** The first step is to collect digitised documents from the target corpora along with their metadata. These metadata provide the context for the documents to be processed. They usually come from the inventory sheet. If these sheets are not structured (or not all in the same way), they need to be restructured to make them usable automatically [2]. It serves as a valuable source of knowledge for thematising the tables. For example, in the case of the land registry, we want to know the name of the municipality associated with each register, its type, its date of creation, and its period of validity. These metadata are leveraged in the step described in section 4.3.

**Images classification** Once the digitised registers have been collected, an automated classification step is required to identify the images that contain the information to be extracted. In the case of the initial registers, we are targeting, on the one hand, the tables describing the plots and, on the other hand, the cover pages describing the sections containing the plots listed on the pages
which follow. We consider five classes: cover pages, main tables, second page of main tables, intermediate summaries, and syntheses.

## 4.2 Information Extraction from digitised tables

Table and text structure recognition, performed using *end-to-end* approaches based on Transformer architectures such as DAN [8], enables the extraction of content from each page without prior image segmentation or text localization. We chose to fine-tune such type of model for several reasons: (1) page-level extraction prevents error propagation between chained information extraction (IE) tasks; (2) the model leverages the full-page context to transcribe each line, which is particularly useful for handling merged cells; and (3) it significantly reduces the time needed to produce training datasets, as no intermediate annotations (such as drawing boxes around words or rows) are required. Annotations are stored in structured text files (JSON).

Moreover, full-page recognition is highly compatible with the semantic table interpretation process, as annotated images can be treated as raw text-based numerical tables. Consequently, we propose an image annotation model guided by the concepts of the PeGaZus ontology. The columns to be extracted correspond to ontology concepts, allowing us to manually address some semantic table interpretation (STI) tasks early in the pipeline: Column-Type Annotation (CTA) and Column-Property Annotation (CPA). The type of entity associated to each row is also known (Landmark of type Plot). In the case of the initial registers, we extract columns containing plot addresses (Landmark as street or locality names), plot numbers, plot nature (Nature as house, mill, wine, garden, etc.), taxpayer information (Taxpayer), and, when available, the taxpayer index number from the taxpayers' summary (found either in the initial register or in the mutation register). The attention mechanism in architectures such as DAN enables the handling of variations in column labels, order, and presence across different versions of the initial registers, depending on the pre-printed table used. Finally, a post-processing step is required to replace the various forms of *idem* with the values they refer to.

## 4.3 Semantic Table Interpretation and KG Population

This step aims to enrich the raw transcription produced in the previous step. As Column-Type annotation (CTA) and Column-Property annotation (CPA) tasks of STI have already been solved previously, we focus on the table Context Retrieval and the Cell-Entity Annotation task (CEA) including to new entities discovery.

**Context retrieval** The initial land registry records are organized into chapters, each corresponding to a municipality section identified by a letter (A, B, etc.). To later link plots with other related landmarks (section, municipality etc.) and create plot identifiers, we have to retrieve the context of each extracted row
A pipeline for KG Population from land registry digitised tables

<figure><img src="image_4.png" /><figcaption>Fig. 3: Entity linking algorithm used to link instances of *Nature* with mentions of natures. The two steps help to deal with cells containing both single and enumerated nature mentions.</figcaption></figure>

*(which chapter of which register ?)*. We therefore need to retrieve the identifier of the last previous page cover before the considered digitised image and the section letter written in it. The pages have been classified as described in Section 4.1. Information written on non-table pages are stored in the metadata.

**CEA and CNEA** This task aims to link the raw text produced by the IE step to the KG existing resources or to create new entities and add them to the KG. These new entities can be used to annotate the table.

*Case 1: Entity Linking with existing resources* For column types that already have entities in the ontology (e.g. the plot nature), we perform an entity linking (EL) process described in Figure 3. In the case of the plot nature, 160 candidate SKOS concepts have been defined in the ontology, based on the most common natures observed in the old registers and from a list of natures in the current French land registry. They are represented as instances of the class *Nature*. They can have several alternative labels (including abbreviations) in the registers. The entity linking algorithm is as follows: first, the full cell text is compared with the candidate labels using a similarity measure. The mention is matched to the top 1 candidate. If there is no match and the mention consists in several words (potential enumeration), the similarity measure between the candidate labels and each word is computed. If there is still no match, the cell is associated with the NIL concept and should be handled by an expert. The Gestalt pattern matching approach [28] is used to compute a similarity measure between entities labels and mentions. We choose this measure because it is not too sensitive to noisy text. It could be replaced by others character string or text embeddings similarity measures.

*Case 2: New Entity Creation* For column types that do not already have associated entities in the ontology (e.g., taxpayers, plot addresses), new entities must be created and each cell must be linked to the corresponding resource. The specific algorithm used depends on the type of entities being created, with a focus on grouping similar mentions.

For plot addresses, we perform mention comparisons using a character string similarity measure (Normalized Levenshtein distance). Mentions that are similar
<figure><img src="image_5.png" /><figcaption>Fig. 4: Algorithm used to create instances of Taxpayer class. Each step aims to precise groups of mentions based on properties comparison.</figcaption></figure>

within a specified threshold are grouped together, and an instance of Landmark is created for each group.

For taxpayers, the algorithm is more sophisticated (see Figure 4). A fine-tuned named entity recognition (NER) model is employed to categorize the cell values into the following classes: name, firstnames, familystatus, title, activity, and address. Mentions with similar "name" values within a threshold T1 are grouped. Then, mentions within each group are further clustered according to similar "firstnames" using threshold T2. The groups are refined if there are more than two dissimilar "familystatus" values within a group, using threshold T3. Missing values for "firstnames" and "familystatus" are treated as empty strings. Finally, a post-processing step is applied to refine the groups. First, words of firstnames are compared word by word to ensure that they meet the similarity threshold before merging. Mentions are merged regardless of first name similarity if their index numbers match. When first names are similar, but index numbers differ, merging occurs only if the first names are highly similar. Once these steps are completed, an instance of Taxpayer is created for each group.

**Row-to-Instance annotation** This last task aims to create an entity corresponding to a row. By definition, a row in an initial register describes an instance of Landmark of type Plot. The entity is created and associated with its identifier, whose value is equal to the concatenation of the section identifier and the plot number.

## 4.4 Knowledge Graph Population

Once the entities associated with each row have been identified, we can populate the knowledge graph according to the domain-ontology data scheme. This includes creating the relations between each plot and its attributes (e.g. the row and its entities). To keep track of the source of information used to create each KG instance, we also create instances describing rows, pages, and registers represented according to the RICO ontology. Then, we link them to the instance
created from these pieces of documents. In further work, automatic processing of the mutation registers with this pipeline, in addition to the initial registers, would make us able to build a multidate knowledge graph using the PeGaZus algorithm by retrieving all the mentions of the same plot in the whole land registry corpus of a given area.

# 5 Implementation and Evaluation

In this section, we present the pipeline implementation to process the corpus of the initial registers of the land registry of the Val-de-Marne department. We also describe the datasets produced to train and evaluate the deep learning models and the gold standard graph used to evaluate the final output. The graph evaluation metrics are detailed, and the results are provided.

The following metrics are used to assess the various tasks in the pipeline and the end results. Their value is expressed as a percentage.

$$ \text{Character Error Rate (CER)} = \frac{S + D + I}{N} $$

$S$, $D$ and $I$ are the number of substitutions, deletions, and insertions. $N$ is the total number of characters in the reference.

$$ \text{Precision} = \frac{|P \cap G|}{|P|} \quad \text{Recall (Agreement)} = \frac{|P \cap G|}{|G|} \quad \text{F1} = 2 \cdot \frac{\text{precision} \cdot \text{recall}}{(\text{precision} + \text{recall})} $$

$$ \text{False Discovery Rate (Surplus)} = \frac{|P \setminus G|}{|P|} \quad \text{False Negative Rate (Deficit)} = \frac{|G \setminus P|}{|G|} $$

$P$ is the set of automatically predicted elements and $G$ the set of elements in the gold standard. In the following evaluation, elements can be table rows and cells (information extraction) as well as instances, relations between instances, and datatype properties associated to each instance of the graphs.

## 5.1 Pre-processing

To ensure that all processed pages are simple pages, images with a width greater than their height are split into two parts. The page classification step is performed using a fine-tuned YOLOv11-CLS model<fnref n="9" />, trained on the dataset described in Table 1a. The images in the dataset are stratified by type and grouped by municipality. Training was carried out on a single NVIDIA A40 GPU with early stopping (detailed parameters in Table 1b). The best model was obtained at epoch 54. The accuracy reaches 100% on the validation set and 99.3% on the test set, with the remaining errors due to a single confusion between an intermediate summary page and the main table page.

<footnote n="9">⁹ https://docs.ultralytics.com/fr/tasks/classify/</footnote>
<table><caption>Table 1: Page classification dataset and parameters</caption><caption>(a) Page classification subsets composition</caption><thead><tr><th></th><th colspan="3">Train Val Test</th></tr></thead><tbody><tr><th>Main table</th><td>413</td><td>57</td><td>86</td></tr><tr><th>Intermediate summary</th><td>198</td><td>38</td><td>30</td></tr><tr><th>2nd page of main table</th><td>89</td><td>22</td><td>27</td></tr><tr><th>Summary</th><td>30</td><td>3</td><td>4</td></tr><tr><th>Cover page</th><td>15</td><td>3</td><td>1</td></tr></tbody></table><table><caption>(b) YOLOv11 training parameters</caption><tbody><tr><th>Model</th><td>YOLOv11-CLS M</td></tr><tr><th>Epochs</th><td>200</td></tr><tr><th>Batch size</th><td>6</td></tr><tr><th>Seed</th><td>42</td></tr></tbody></table>

## 5.2 Information Extraction from digitised tables

To perform the text and table recognition step, we fine-tune the DAN model<fnref n="10" /> (Document Attention Network) [8] following the strategy used on the census registers [2]. A dataset of 139 pages (2,650 rows) was created to train and evaluate the model. To select the pages, a stratified sampling was performed based on the three main types of pre-printed tables. Then, pages from each municipality are assigned to a subset. There are 97 pages in the training set, 22 in the validation set, and 28 in the test set. Special tokens are used to identify cells for each column. Additional tokens (see Figure 5) are inserted into the text to capture text typography (crossed out, exponent) and layout characteristics (line breaks, *dito* marks, merged cells). These additions may provide a better understanding of the documents.

<figure><img src="image_6.png" /><figcaption>Fig. 5: Example transcription of three table rows. Typography tokens aim to help convey the writer's formatting and table completion behavior.</figcaption></figure>

<footnote n="10">¹⁰ https://pypi.org/project/atr-dan/</footnote>
<table><caption>Table 2: Performance on cell recognition per column type on the test set.</caption><thead><tr><th>Cell type</th><th>Predicted</th><th>Matched</th><th>Precision</th><th>Recall</th><th>F1</th></tr></thead><tbody><tr><td>Plot number</td><td>487</td><td>341</td><td>70.02</td><td>73.02</td><td>71.49</td></tr><tr><td>Plot nature</td><td>490</td><td>335</td><td>68.37</td><td>71.58</td><td>69.94</td></tr><tr><td>Taxpayer index number</td><td>384</td><td>251</td><td>65.36</td><td>74.04</td><td>69.43</td></tr><tr><td>Taxpayer</td><td>485</td><td>301</td><td>62.06</td><td>64.59</td><td>63.30</td></tr><tr><td>Plot address</td><td>328</td><td>178</td><td>54.27</td><td>57.42</td><td>55.80</td></tr><tr><td>Plot number(s) in prev. land registry</td><td>95</td><td>49</td><td>51.58</td><td>37.12</td><td>43.17</td></tr><tr><td>Plot nature(s) in prev. land registry</td><td>93</td><td>41</td><td>44.09</td><td>37.61</td><td>40.59</td></tr><tr><td><strong>ALL</strong></td><td>2362</td><td>1496</td><td>63.34</td><td>65.30</td><td>64.30</td></tr></tbody></table>

The model was fine-tuned over 2,000 epochs with a learning rate of 0.0001, optimized using AMSGrad, and a batch size of 2. Training was performed with two NVIDIA A40 GPUs. The results are encouraging, but largely improbable, with a Character Error Rate (CER) of 35.3% on the test set. Additional typography tokens are considered characters.

Several observations can be made regarding the metrics by cell types (see Table 2). They have been calculated using the Nerval library<fnref n="11" /> with a threshold of 0 which means that only perfect matches are allowed. First, the cells in the columns related to "plot number" and "plot nature" from previous land registries show lower F1-scores. This can be explained by the fact that these fields are present only in one of the three types of pre-printed tables in the dataset and are underrepresented in the training set. Second, the highest F1-scores are achieved for cells containing numeric values ("plot number" and "taxpayer index number"). The "plot nature" column also achieves a F1-score close to 70%, probably due to (1) the over-representations of certain values (as land, garden, house), and (2) the absence of special formatting like vertical or merged text for this particular element. The F1-score for "taxpayer" reaches 63% and 55% for "plot address" due to variable complexity.

## 5.3 Semantic Table Interpretation and Graph Population

**Evaluation dataset** Semantic table interpretation results and the final population of the graphs are evaluated together because they are highly linked. We set up an evaluation process which includes the creation of a gold standard knowledge graph. First, the pages of the DAN test set are ranked using their CER. The municipality (L'Haÿ-les-Roses) whose pages have representative and diverse scores (ranked 1, 13 and 25 among 26) was selected. Then, using the fine-tuned DAN model, the table pages of a whole section (representing 26 pages) are transcribed. Missing rows are added, and erroneous rows are discarded. For both valid and missing rows, transcriptions are corrected or created. Cells in the "plot nature" column are manually annotated with the corresponding instances of the Nature class from the ontology. Similar cells in the "plot

<footnote n="11">¹¹ https://pypi.org/project/teklia-nerval/</footnote>
address" and "taxpayer" columns are grouped under a unique identifier for each
instance of Landmark and Taxpayer. Detailed information for taxpayers is also
structured (name, first names, etc.). Both graphs are populated following the
ontology structure and matched to proceed to evaluation.

**Knowledge Graph Evaluation** Before evaluating the KG himself, we compute some metrics on the table row detection produced by DAN, as they are relevant to explain several parts of the results regarding the final KG. 344 rows have been correctly predicted, 19 are missing, and 13 are erroneous (most of them are hallucinated rows). The precision, recall and false discovery rate are respectively equal to 96.4%, 94.8% and 3.6%.

The graph evaluation aims to assess the completeness and semantic precision
of the knowledge graph (KG). We report agreement, surplus, and deficit rates in
three elements: instances, object properties (relations between instances), and
datatype properties.

Overall, agreement/surplus/deficit rates are 87.26%, 12.74%, 11.94% for in-
stances, 78.5%, 21.5%, 19.9% for relations (object properties) and 87.35%, 12.65%,
16.66% for datatype properties attached to instances (values of datatype prop-
erties are not considered here). Table 3 presents a detailed analysis for class in-
stances. A perfect agreement rate (100%) is achieved for instances derived from
metadata (e.g., Record and RecordSet). The Instantiation class also exhibits
a very high agreement rate, since every Record (page) and RecordSet (register)
object in the gold standard has a corresponding instance in the Instantiation
class. Discrepancies are mainly due to errors in table row recognition, which is the
source of most of Instantiation objects. Regarding the creation of Landmark
instances, two main factors influence the performances. The successful recogni-
tion of table rows directly implies high recall on Plot-type Landmark since each
row corresponds to such type of object (and so on to the all Landmark instances
as plots are the most represented type). The surplus and deficit in this class are
attributed to hallucinated rows, which led to the creation of nonexistent plots,
and to failure to correctly extract landmark mentions embedded in merged cells
in the plot address column. These cells often contain special typographical tokens
that are poorly detected by our IE model, preventing cell merging and causing
segmentation errors in landmark labels. These same issues directly affect the
LandmarkRelation class: when landmarks are not correctly instantiated, the
spatial or semantic relations (e.g., between plots and addresses) are incorrectly
represented in the KG. Taxpayer instances has also mixed results (60.53% of
agreement). Transcription errors impact the creation of the groups of mentions
either as the variation of the detail level associated to each mention to describe
each taxpayer in the registers (i.e. name only or name and one or many first-
names). Linking accuracy for plot nature mentions reaches 97%. A strict match
on ?plot dcterms:identifier ?plotid yields 92.54% recall, indicating that
most plot identifiers present in the gold standard are correct in the generated
graph. This result meets the good scores on "plot number" cell recognition with
DAN. The pipeline's implementation on the Val-de-Marne historical land reg-
<table><caption>Table 3: Instances by class evaluation</caption><thead><tr><th>Type</th><th>Agreement</th><th>Deficit</th><th>Surplus</th><th>Actual</th><th>Predicted</th></tr></thead><tbody><tr><td>addr:Event</td><td>100</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>rico:RecordSet</td><td>100</td><td>0</td><td>0</td><td>2</td><td>2</td></tr><tr><td>rico:Record</td><td>100</td><td>0</td><td>0</td><td>30</td><td>30</td></tr><tr><td>rico:Instantiation</td><td>95.44</td><td>4.56</td><td>3.33</td><td>395</td><td>390</td></tr><tr><td>addr:Attribute</td><td>95.20</td><td>4.80</td><td>3.43</td><td>1084</td><td>1050</td></tr><tr><td>addr:AttributeVersion</td><td>95.20</td><td>4.80</td><td>3.43</td><td>1084</td><td>1050</td></tr><tr><td>addr:Landmark</td><td>94.28</td><td>5.72</td><td>7.24</td><td>367</td><td>373</td></tr><tr><td>addr:Change</td><td>93.91</td><td>6.09</td><td>3.48</td><td>1446</td><td>1407</td></tr><tr><td>cad:Taxpayer</td><td>60.53</td><td>39.47</td><td>50.27</td><td>152</td><td>185</td></tr><tr><td>addr:LandmarkRelation</td><td>47.39</td><td>52.61</td><td>51.54</td><td>728</td><td>712</td></tr></tbody></table>

registry confirms its robustness, achieving 99.3% accuracy in page classification, 94.8% recall in row recognition, and around 80% agreement with the gold standard graph. Most residual errors are due to handwritten text recognition issues, particularly noisy outputs, and missed special tokens, which impact entity and relation extraction.

## 6 Conclusion

This article presents an end-to-end pipeline for the automated extraction and semantic structuring of information from digitised 19th-century French land registry tables. By combining deep learning-based information extraction with semantic table interpretation guided by the PeGaZus ontology, the approach enables the automated generation of a knowledge graph from complex handwritten historical documents. Applied and evaluated for a complete section of a municipality in the Val-de-Marne department, the pipeline demonstrates strong potential for large-scale analysis and dissemination of historical land registry data.

A key contribution of this work is the development of a complete processing chain for the RDF-based knowledge graph population from noisy and heterogeneous historical tables. The approach includes a semantic table interpretation method tailored to handwritten data using a domain ontology specifically designed to describe the evolution of landmarks and addresses over time. Future work will focus on extending the DAN training dataset to improve handwritten text and typography tokens recognition. Enhancing text recognition result would benefit to the automatic generation of Taxpayer and Landmark entities. Increasing the gold standard KG to produce an extended evaluation and adapting the pipeline to mutation registers to enable tracking of plot changes with the PeGaZus algorithm are relevant perspectives. We also plan to explore the potential of Vision-Language Large Models (VLLMs) to improve the IE process.

**Acknowledgments.** This work was funded by the Defense Innovation Agency of the French Armed forces Ministry.
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