=Paper=
{{Paper
|id=Vol-3759/paper9
|storemode=property
|title=A systematic approach towards higher quality linked open
data at Nieuwe Instituut
|pdfUrl=https://ceur-ws.org/Vol-3759/paper9.pdf
|volume=Vol-3759
|authors=Nora Abdelmageed,Lois Hutubessy
|dblpUrl=https://dblp.org/rec/conf/i-semantics/AbdelmageedH24
}}
==A systematic approach towards higher quality linked open
data at Nieuwe Instituut==
https://ceur-ws.org/Vol-3759/paper9.pdf
A systematic approach towards higher quality linked
open data at Nieuwe Instituut
Nora Abdelmageed1,∗ , Lois Hutubessy1
1
Nieuwe Instituut, Museumpark 25, 3015 CB Rotterdam, Netherlands
Abstract
Nieuwe Instituut (NI) houses the Dutch National Collection of Architecture and Urban Planning. This
collection consists of about 4 million objects, including design drawings, 3D models, and photographs.
As part of the program Disclosing Architecture, which is now in its sixth and final year, the Linked
Open Data (LOD) project aims to share the richness of data within the collection with the public through
semantic web technologies. This project will ultimately facilitate the exchange of cultural heritage
data with related national and international institutions. Currently, Nieuwe Instituut (NI)’s collection
management system contains inconsistent records due to changes in registration guidelines and the
migration from older collection management tools. Without documentation of these guidelines, it is
impossible to establish consistent rules for the entire dataset. Yet, clean data is crucial for effectively
showcasing NI’s collection to the public. In response, this paper introduces a framework for higher-
quality LOD data, the Data Cleaning Initiative (DCI). The first implementation of the DCI is through a
series of steps planned for the year 2024 with the goal of cleaning and enriching the collection data at NI.
Keywords
Linked Open Data, Cultural Heritage, Data Quality, Entity Linking, Entity Resolution
1. Introduction
The Dutch Cultural Heritage sector has undergone a significant transformation by adopting
semantic web technologies and converting their data into Linked Open Data (LOD). The dataset
register1 developed by Netwerk Digitaal Erfgoed2 lists 71 publishers that expose their datasets
in one or more linked data formats3 . Rijksmuseum is a pioneer in this area, having published
its collection data as LOD as one of the first [1], thereby diversifying search results in other
applications [2]. Another notable effort is by Van Wissen [3], who targeted named entity
extraction from archival records with the help of LOD. However, ensuring high-standard and
clean data remains an open challenge.
Nieuwe Instituut (NI)4 houses the National Collection of Architecture and Urban Planning,
which consists of approximately 4 million items ranging from design drawings to photographs
SEMANTiCS - 20th International Conference on Semantic Systems, Sep 27–19, 2024, Amsterdam, Netherlands
∗
Corresponding author.
Envelope-Open n.abdelmageed@nieuweinstituut.nl (N. Abdelmageed); l.hutubessy@nieuweinstituut.nl (L. Hutubessy)
Orcid 0000-0002-1405-6860 (N. Abdelmageed)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
1
https://datasetregister.netwerkdigitaalerfgoed.nl/
2
https://netwerkdigitaalerfgoed.nl/
3
Access June 2024 using search feature with n-triples*, rdf+xml*, turtle, and trig.
4
https://nieuweinstituut.nl/
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
and 3D models. As part of the program Disclosing Architecture (AD)5 , which is now in its sixth
and final year, LOD project aims to share the richness of data within the collection with the public
through semantic web technologies. In parallel, NI develops a new online Collection platform
that will further increase accessibility to a wider audience, facilitating discovery by design.
While this platform is set to launch in November 2024, the underlying data is already exposed
through a separate endpoint6 , with more than 18 million triples available in the customized
Triply7 environment.
In this paper, we propose our systematic approach Data Cleaning Initiative (DCI) at NI to
enhance the data quality of our LOD. We explain its scope, tasks, and how we implement such
an approach in practice. DCI is a vision that could be applied in any Cultural Heritage instituut
for cleaning and enriching their LOD.
1.1. Motivation & Problem Definition
We aim to increase the exposure of Architecture and Urban Planning data. For instance, we
built a central point or an encyclopedia on top of the Dutch Heritage Data collection in NI.
However, due to human errors and changes in guidelines and collection registration tools,
the data has become heterogeneous and inconsistent on a large scale. As such, publishing
the current data version may not be the best approach. The data currently contained in the
collection management system (Axiell Collections8 ), while suitable for internal purposes, would
benefit from further cleaning and enrichment to ensure a higher quality of data for public use.
This would facilitate collaborations with third parties and attract a wider audience.
Records in NI’s collection management system are inconsistent due to changes in registration
guidelines and migrations from older collection management tools. In addition, there is no
documentation of these guidelines, making it difficult to establish rules for the entire dataset.
Cleaning these records in their entirety is challenging for two reasons. On the one hand,
understanding the meaning of sparse heterogeneous records within the same catalog is difficult.
E.g., “Library catalog” registers 16 types, including books and audio materials. On the other
hand, the sheer volume of heritage records adds to the complexity.
1.2. Objectives & Tasks
The Data Cleaning Initiative (DCI) has two main objectives: (i) Proposing to split catalogs
containing broad relations of data into smaller, closely related datasets. This facilitates the
semantic grouping of the original catalogs within the collection management system. (ii)
Applying cleaning and enrichment strategies to the resulting semantic categories to improve
the data quality.
We propose four categories of data cleaning and enrichment tasks in the context of DCI. We
define these main categories as:
5
https://nieuweinstituut.nl/en/projects/architectuur-dichterbij
6
https://collectiedata.hetnieuweinstituut.nl/the-other-interface/knowledge-graph/sparql
7
triply.cc
8
https://www.axiell.com/solutions/product/axiell-collections/
Table 1
People & Institutions Examples
No. use_count name name.type
1 10 Erp, T. van ?
2 10 Erp, Th. van person & author
3 2 Vugt, Theo van author
4 3 Erp, Theo van person & author
5 3 Erp, Theodoor van person & author
1. Data Cleaning: This category involves all tasks concerning primitive data cleaning. E.g.,
handling inconsistencies like using different formats or tackling missing values. The
latter influences the guidelines for filling in this metadata. E.g., discovering a potential
required field.
2. Entity Resolution: This category aims at similar entities discovery and grouping. Since
all data is manually entered by domain experts, they might use different representations
describing the same entity. E.g., Doesburg, Theo van is a different representation for
Doesburg, Th. van.
3. Entity Linking This group maps internal records of our heritage data collections to
external resources, or Knowledge Graphs (KGs), e.g., Wikidata9 . For example, Doesburg,
Theo van would be mapped to wd:Q160422 10 .
4. Entity Enrichment This category aims to fetch external properties and pieces of infor-
mation that exist in external resources and do not exist in the local collection management
system. E.g., we save the image of Doesburg from Wikidata in our Axiell Collections.
Table 1 summarizes the scope of each task of the DCI. The first record shows an empty
name.type where the Data Cleaning task covers this kind of issue. Records 2, 4, and 5 represent
the same person in different formats; Entity Resolution groups them all as the same person.
These records after grouping, Entity Linking will map these records to the Wikidata page of
Theodoor van Erp -wd:Q2759953 11 . Finally, we can store new information in our catalog to
enrich the displayed data at the application level.
2. Approach
In this section, we describe the initiative’s approach. Initially, we explain the concept of semantic
grouping; then, we give details of the DCI pipeline.
2.1. Semantic Grouping
Currently, domain experts enter metadata for multiple semantic categories into the same Axiell
catalog. For instance, they use “People & Institutions” for registering persons, architects,
publishers, universities, etc. The same case applies to the “Library catalog”, where domain
9
https://www.wikidata.org/wiki/Wikidata:Introduction
10
http://www.wikidata.org/entity/Q160422
11
https://www.wikidata.org/wiki/Q2759953
Entity Enrichment
Entity Linking
Entity Resolution
Data Cleaning Semantic
Groups
2 4 Discuss &
6 Approve &
Analyze
•Get a snippet of the •Investigate and Validate •Apply the final Import
target semantic document how to solution to the target
group to fix •Find issues solve these issues •Validate the •Get the green light to
•Log them (backlog) proposed solution import these fixes
•Discuss it with •Get the stuff back to
domain expert Axiell
Export Group Propose Solve
1 3 5
Figure 1: DCI Proposed Pipeline
experts register sixteen semantic types, including Books, serials, Audio/Visual Material, and
Articles. This yields a large catalog but is sparse in terms of the metadata. Thus, we decided to
split each catalog into smaller chunks that share the semantic type, i.g. extracting only Books
from the Library. The main idea is the DCI relies on the semantic division of the Axiell catalogs.
By this means, it facilitates human intervention and yields a better statistical view of the data.
The target of this phase of the DCI is to obtain named groups to be cleaned.
Creating a semantic group requires determining representative fields for each group. To
find these groups, we instigated Axiell Collections’ fields for each category. In addition, we
held several meetings with domain experts to determine those fields and ensure their correct-
ness and scope. For instance, “People & Institutions” catalog contains for example: name,
birthDate, birthPlace, deathDate, deathPlace, biography, ISBN_publisher_prefix. This group of
fields represents two semantic groups: 1) Persons, and this group contains: name, birthDate,
birthPlace, deathDate, deathPlace, biography. 2) Institutes and this group contains name and
ISBN_publisher_prefix.
2.2. Pipeline & Workflow
Figure 1 depicts the proposed pipeline for DCI. The figure represents the separate steps that we
follow til we reach the final goal or the end of the year for each semantic group. Our pipeline
consists of six iterative steps taking into consideration four scopes of work: Data cleaning, entity
resolution, entity linking, and entity enrichment. Our pipeline starts with: 1) Export the target
semantic group given the representative group fields in a CSV file. The CSV file allows batch
processing and facilitates the general overview of the exported records. 2) Analyze the data and
log the encountered issues in our backlog system. 3) Propose a solution for individual issues
and determine if it is possible to solve them automatically or if it needs manual intervention.
4) Discuss the proposed solution with domain experts and stakeholders to validate it. If it is
not a valid solution, we go back to the proposing stage otherwise, we move to, 5) Solve the
target issue by applying the proposed solution on the CSV file directly. Finally, 6) Approve and
Import, we seek approval from our manager, and if agreed, then we import the CSV with fixes
back to the Axiell Acceptance environment.
Executive
Domain Product
Expert Manager
Start
No? Propose Analyze
Executive
Executive App. Manager
Yes?
Import Approve Discuss
Program Product Domain Executive
Manager Manager Expert
Solve Validate
Executive Domain
Expert
Figure 2: Simplified workflow of the DCI
Figure 2 shows a simplified data flow diagram that explains the interaction among DCI actors
regarding their tasks. It starts with an executive that analyzes the target semantic group to be
cleaned. Then, the executive proposes a solution that starts an iterative process (discuss, validate,
solve, and approve). The domain expert is the only required actor to validate a proposed solution.
If and only if the application manager approves the solution and its results, the executive can
import the fixed fold back to the collections management system.
Acknowledgments
We would like to thank our domain experts, Inge van Stokkom, Christel Leenen, Ernst des Bouvrie,
Evelien Dekker, Kelly James, and program manager Gijs Broos, Nieuwe Instituut (NI). Moreover, we
would like to thank the Dutch Ministry of Culture, Education, and Science for funding the Disclosing
Architecture program.
References
[1] C. Dijkshoorn, L. Jongma, L. Aroyo, J. van Ossenbruggen, G. Schreiber, W. ter Weele,
J. Wielemaker, The rijksmuseum collection as linked data, Semantic Web 9 (2018) 221–230.
URL: https://doi.org/10.3233/SW-170257. doi:10.3233/SW- 170257 .
[2] C. Dijkshoorn, L. Aroyo, G. Schreiber, J. Wielemaker, L. Jongma, Using linked data to
diversify search results a case study in cultural heritage, in: Knowledge Engineering
and Knowledge Management - 19th International Conference, EKAW 2014, Linköping,
Sweden, November 24-28, 2014. Proceedings, volume 8876 of Lecture Notes in Computer
Science, Springer, 2014, pp. 109–120. URL: https://doi.org/10.1007/978-3-319-13704-9_9.
doi:10.1007/978- 3- 319- 13704- 9\_9 .
[3] L. van Wissen, C. Latronico, V. Zamborlini, J. Reinders, C. van den Heuvel, Unlocking the
archives. a pipeline for scanning, transcribing and modelling entities of archival documents
into linked open data.(short paper), in: DH Benelux 2020-Online:# Goesonline, 2020. URL:
http://2020.dhbenelux.org/. doi:http://10.0.20.161/zenodo.3862817 .