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 efectively showcasing NI's collection to the public. In response, this paper introduces a framework for higherquality 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.
CEUR ceur-ws.org
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 [
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.
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 dificult 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 dificult. 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.
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:
1. Data Cleaning: This category involves all tasks concerning primitive data cleaning. E.g., handling inconsistencies like using diferent 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 diferent representations describing the same entity. E.g., Doesburg, Theo van is a diferent 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:Q16042210. 4. Entity Enrichment This category aims to fetch external properties and pieces of information 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.
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.
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
Entity Enrichment Entity Linking Entity Resolution
Data Cleaning
Semantic
Groups •Get a snippetof the target semantic groupto fix 1 Export Group 2
Analyze •Find issues •Log them(backlog) •Investigateand documenthowto solve theseissues 3
Propose 4 Discuss &
Validate •Validate the proposedsolution •Discussit with domain expert •Applythe final solution to the target 5
Solve 6 Approve &
Import •Get thegreenlight to import thesefixes •Get thestuff backto Axiel 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 ifnd 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 correctness and scope. For instance, “People & Institutions” catalog contains for example: name, birthDate, birthPlace, deathDate, deathPlace, biography, ISBN_publisher_prefix. This group of ifelds 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.
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. Domain Product Expert Manager
Propose
Analyze
Executive
Start Discuss
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.