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Journal of eScience Librarianship 13 (1): e845
DOI: https://doi.org/10.7191/jeslib.845

ISSN 2161-3974 
Full-Length Paper

Automatic Expansion of Metadata Standards for 
Historic Costume Collections
Caleb McIrvin, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

Chreston Miller, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, chmille3@vt.edu

Dina Smith-Glaviana, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

Wen Nie Ng, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

Abstract

Objective: This project focuses on Artificial Intelligence (AI) supported enhancement of descriptive 

metadata for fashion collections (otherwise known as costume or dress and textile collections) through 

expanding costume-specific controlled terms. The authors use Natural Language Processing (NLP) 

techniques along with a human-in-the-loop process to support selection of descriptive terms for inclusion 

in the controlled terms of a metadata schema. This project seeks to expand upon existing domain-

specific schema, Costume Core, by enhancing the schema with a comprehensive set of descriptors. This 

enhancement will allow for more accurate and detailed descriptions of costume artifacts. This article 

describes this process and the outcomes of AI approaches for providing this metadata expansion, who 

this process is for, ethical considerations, and lessons learned.

Received: November 15, 2023 Accepted: February 5, 2024 Published: March 6, 2024

Keywords: Natural Language Processing, library, word embeddings, metadata, costume collections, artificial intelligence, AI 

Citation: McIrvin, Caleb, Chreston Miller, Dina Smith-Glaviana, and Wen Nie Ng. 2024. “Automatic Expansion of Metadata 
Standards for Historic Costume Collections.” Journal of eScience Librarianship 13 (1): e845. https://doi.org/10.7191/jeslib.845.

Data Availability: The data presented in this study are available on request from the corresponding author.

The Journal of eScience Librarianship is a peer-reviewed open access journal. © 2024 The Author(s). This is an open-access 
article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits 
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 
See https://creativecommons.org/licenses/by/4.0.

 OPEN ACCESS

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mailto:chmille3%40vt.edu?subject=
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Abstract continued

Methods: We approached our problem with an investigation into using word embeddings to aid in 

supporting the suggesting of new metadata terms. Several word embedding models were applied with 

the more descriptive one chosen for final use in a human-in-the-loop selection process. This selection 

process provided domain experts to identify which terms chosen by the model are of relevant value. We 

then compare what was chosen by the domain experts and what the model produced to get an idea as 

to the value the model provides in the process of metadata expansion.

Results: The metadata expansion process was a success. An AI supported process aided domain 

experts in choosing relevant terms to include in their metadata schema. Therefore, the results were a 

methodology for using identified AI models for the problem, an interactive system to aid the domain 

experts (software system), and how to evaluate the results.

Conclusion: The application of AI technologies (word embeddings) provided a successful pipeline for 

supporting domain experts to expand the metadata schema with additional descriptors.  Enhancing 

the metadata schema with additional descriptors improves its usability for fashion collection managers 

and allows for a more precise description of the artifacts. As a result, many new terms that were expertly 

chosen were added to the metadata schema.

Introduction

We postulate that problems in cataloging efforts in the historical costuming domain can be mitigated through 
implementation of a standardized metadata schema. Existing metadata schemas that utilize controlled 
descriptive terminology for fashion artifacts, such as historic costume or dress, and items related to the 
process and product of dressing the body (Eicher and Evenson 2014), which include clothing, textiles, and 
accessories, are often constrained by the insufficient number of description fields and a limited vocabulary 
set. By expanding the number of terms available using Natural Language Processing methods, we can 
develop high-quality, consistent metadata enabling better data sharing across collections and increasing 
cataloging accuracy, resulting in improved dress record searchability. Confirming generated descriptor 
choices via a human-in-the-loop approach allowed us to alleviate ethical concerns surrounding the quality 
of the descriptors chosen for our updated metadata schema. This approach also allowed us to ensure our 
descriptor selections for the updated schema drew from an ethnically diverse array of sources while avoiding 
misleading or culturally insensitive terms. Additionally, we emphasized adding terms that would allow for 
inclusive language, for example enhancing the use of colloquial terms such as “robe,” which could refer 
either to “bathrobe” or traditional Chinese robes, by adding more precise terms.

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Project details 

This project was a collaboration between the Virginia Tech Fashion Merchandising and Design (FMD) 
department and Virginia Tech University Libraries. An expert in dress and students from the FMD 
department participated in selecting terms. The expert supervised the student assistants and was the final 
decision-making authority on choosing the descriptors used to expand the metadata schema. The Data and 
Informatics Consultant within the University Libraries worked with an undergraduate Computer Science 
student to develop the Natural Language Processing (NLP) approach and provide analysis of the resulting 
chosen descriptors. Our dress domain expert collaborated with our digital collections specialists within the 
University Libraries to support the process of finalizing the expanded metadata schema. The collection used 
for this work was the Oris Glisson Historic Costume and Textile Collection. The collection was a suitable 
collection since the dress domain expert had previously cataloged the items using Costume Core (Kirkland 
2018), the metadata schema our project strives to expand. To aid in this expansion, we used a pretrained 
NLP model to generate word embeddings. These word embeddings can be used to identify descriptors that 
have conceptually similar meanings but also can identify descriptors that are a little conceptually “further” 
away. This also allows us to introduce descriptor diversity and explore terms that were previously not present 
in the schema. To help with sifting through the identified potential descriptors, we used a lightweight server 
to host an application we developed to aid in sifting through NLP suggested descriptors. The main challenge 
we encountered during our pipeline was how to best share the NLP results with the students and the dress 
domain expert for selection and confirmation. We additionally emphasized ethical consciousness when 
generating the terms, ensuring the terms we generated and confirmed for addition to the schema would be 
valuable for alleviating the confusion of costume collection users..

Background

An accurate understanding of historical cultural eras allows historians / fashion experts to better make 
judgments about social values of the period. To achieve this understanding, it is important to consider 
the historical costuming conventions of the day. Recognizing the importance of costuming artifacts to a 
proper cultural understanding, many universities, libraries, and museums have amassed large collections 
of historical and contemporary dress items. However, these pieces are frequently poorly described, with 
little to no interaction between collections on how to standardize piece descriptions. Although an increased 
emphasis on the advantages associated with collection digitization (Zeng 1999) has started to appear in 
recent years, there is a dearth of research in this area regarding standardization of piece description.

Metadata expansion efforts can be found across a variety of fields, though relatively few use NLP to improve 
development speed. Specifically in the fashion domain, several pushes have been made towards a unified 
ontology through metadata expansion. The Kent State University collection that utilized Dublin Core 
was one such effort (Zeng 1999). Additionally, Costume Core (Kirkland 2018), upon which our metadata 
schema is built, serves as an effective groundwork for metadata expansions. Both of these schemas, however, 

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suffer from a lack of granularity due to fewer metadata levels or controlled terms than required for accurate 
cataloging. In (Valentino 2017), another such linked metadata schema is presented. Ryerson University has 
also made efforts to more clearly display their fashion collection (Eichenlaub, Morgan, and Masak-Mida 
2014) using Dublin Core. A crowdsourcing effort based on Costume Core uses survey data as a promising 
tool to combat the lack of generality present in many of the above schemas (Cai et al. 2012), but their work 
is specific to Chinese-style costumes.

Numerous efforts have been made towards ontology development as well, both in the fashion domain and 
elsewhere. A work in the fashion domain (Bollacker, Díaz-Rodríguez, and Li 2016) claims that ontologies 
taking only garment attributes into account provide insufficient information and seeks to build a subjective 
influence network to incorporate more data into the ontology. In Novalija and Leban 2013, work is done to 
construct an ontology of designer garments, connecting pieces based on Wikipedia link structures.

The primary benefits of a more comprehensive metadata schema include improvement of searchability and 
discoverability. Streamlining the expansion of metadata schema can be efficiently achieved by employing 
NLP techniques. Outfit2Vec (Jaradat, Dokoohaki, and Matskin 2020) uses clothing metadata to build 
machine learning models that can better recommend garments to consumers. Tahsin in (Tahsin et al. 2016) 
uses NLP to extract geographic metadata from text corpuses to increase location specificity. One approach 
towards solving this problem, taken by (Cai et al. 2012), is crowdsourcing—researchers use NLP techniques 
coupled with input from 100 students with regards to metadata element importance. However, the techniques 
used in the above paper did not generate new descriptors, only assisted in confirming previously selected 
categorizations.

A standardized set of descriptors needs to be developed in order to allow visitors to digital collections to 
quickly search for a particular type of garment. Such a set of descriptors is Costume Core, as mentioned 
above. Unfortunately, the scope of Costume Core is limited by its size – many potentially useful descriptors 
and several valuable categories are left out, restricting the utility of the project. In our work, we seek to 
expand upon the Costume Core vocabulary by using NLP techniques to efficiently identify new descriptors 
previously not included in the schema, expanding the size of the Costume Core vocabulary to enhance 
digital collection cataloging and search capabilities.

Methodology

Our process to identify high-quality costume descriptors consisted of multiple steps. Firstly, we generated 
hundreds of potential descriptors using word embeddings, an NLP technique, from the initial Costume Core 
schema. Afterwards, we used our Model Output Confirmative Helper Application (MOCHA) to facilitate 
the review process by our trained fashion students. Finally, our domain expert reviewed all selections, 
trimming the choices down to ensure quality.

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To obtain the initial descriptors used in similarity generation, we adapted a popular set of descriptors 
found in the Costume Core vocabulary commonly used in fashion metadata description tasks (Kirkland 
2018). While the keywords contained in the vocabulary were acceptable in many cases, some categories 
could be removed, as new, meaningful descriptors were unlikely to be generated for the category. One such 
example is the “Socio-economic class” category, which, in the original Costume Core vocabulary, contains 
the descriptors “middle class,” “upper class,” and “working class.” As models are unlikely to create useful 
descriptors for this category, this category and similar others were removed from our analysis. In addition, 
slight manual lemmatizations (such as changing  “coatdresses” to “coatdress”) were made to generate more 
accurate predictions.

Data preprocessing

Additional data pre-processing was necessary to convert keywords to a format usable by our selected models. 
As the keywords were initially stored in an Excel file, we needed to convert this to a file more conducive to 
model format. To accomplish this, we removed characters our models wouldn’t recognize, such as “$” and 
“!,” from the file using a regular expression. Additionally, we performed some minor manual tweaking of 
the initial keyword selections to maximize the number of potential new descriptors output by our model. 

Model selection

Similar descriptors can be generated efficiently using a cosine similarity method, a technique that uses 
distance between vector representations of tokens, also known as word embeddings, as a measure of 
similarity between words. Gensim’s Word2Vec, a Python library developed to work with word embeddings, 
provides functionality to quickly and easily generate the most similar words for a model from a vocabulary. 
For accurate comparison testing, we tested three separate gensim Word2Vec models, specifically the Google 
News, Mpnet, and Sentence-t5 models. While these models were not specifically fine-tuned on fashion 
literature, they were still capable of accurately modeling the relationships between descriptors, as seen in 
Figure 2. After initial data analysis and feedback from our reviewers, we elected to narrow down our focus 
to emphasize solely the Google News model.

Initial Costume Core Network Data Visualization

In order to gain an understanding of the different connections between Costume Core keywords as captured 
by the models, we create visualizations of relationships between keywords. To create these visualizations, we 
first load in the Costume Core keywords, organized by category, as well as our three separate models. For 
our model, we iterate over model keywords twice, calculating the cosine similarity values between keywords 
and storing the values greater than a set percentage in a tabular format (Figure 1).

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We then use the Python NetworkX (Hagberg, Swart, and Schult 2008) library to export a graph as a format 
readable by the Orange (Demsˇar et al. 2013) software. In addition, we create separate files to specify Orange 
visualization format by providing additional keyword category information. To assess the quality of the 
model’s representations, we graphed our initial descriptors by category, connecting them by the strength of 
their cosine similarity weights. As seen in Figure 2, the model performs reasonably well at clustering similar 
Costume Core keywords, as evidenced by the tightly clustered “color” and “material” categories.

Figure 1: Cosine similarity weights between the Costume Core keywords (keyword_1) and their most 
similar tokens (keyword_2) as predicted by the model (weight).

Figure 2: Google News representation of Costume Core keywords. Colors represent categories of 
descriptors, e.g. material, neckline, technique, etc.

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Descriptor generation

Once we had our descriptors and models loaded into the correct format, we ran the descriptors through 
our Word2Vec models to generate new potential descriptors for later analysis. For each valid Costume Core 
descriptor, we generated the top 25 most similar potential descriptors. After the models finished generating 
similar potential descriptors, we saved the results to separate .csv files for storage later—these similar 
potential descriptors contained important information for analysis.

Supporting Human in the Loop term selection

Before the potential descriptors generated by the model could be released, we needed a way to confirm the 
descriptors were actually valid descriptors for historical costuming metadata. We determined that the most 
effective way of confirming these descriptors was a human-in-the-loop approach, in which fashion metadata 
experts would check over the model selected descriptors, selecting the most accurate / relevant descriptors. 
This approach is partially motivated by the fact that their selections would allow us to calculate several 
statistics measuring the accuracy of the models, definitively demonstrating the effectiveness of NLP models 
in generating new descriptors in the fashion metadata domain. Our process is displayed in more detail in 
Figure 3.

Figure 3: Descriptor confirmation loop using a Model Output Confirmative Helper Application (MOCHA).

To expedite the process and allow our domain experts to easily and efficiently process the generated 
descriptors, we created a Model Output Confirmative Helper Application (MOCHA) to present the  
model-generated descriptors. To operate the web application, users load in model-generated words, at 
which point, they can visually select a subset of descriptors to classify as confirmed descriptors. These  
model-generated words came in the form of the top 25 descriptors similar to each token in the Costume Core 
vocabulary. Multiple usability enhancements allow users to navigate quickly between pages of descriptors, in 
case labeling needs to be broken up into multiple sessions. Functionality for clearing selected descriptors is 
added to allow for greater flexibility. After all descriptors have been confirmed / rejected, or after a labeling 
session has ended, users can download the descriptors they’ve confirmed for analysis.

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To confirm our selections, we first had a group of three trained fashion students confirm model-generated 
descriptors using the web application, as seen above. To use the web application, descriptors were loaded 
in as textual data, where they appeared as a word cloud in column 2. After clicking on terms to select 
beneficial ones, they would appear in column 3 and could be downloaded for analysis. Terms which 
remained unselected were placed in column 1, where they would be discarded. After our students had 
finalized reviewing potential descriptors, our domain expert edited / revised the students’ selections. After 
finalizing revision, we converted the collected confirmed descriptors to a form more suitable for analysis, 
combining the descriptors generated by the domain experts into a single file. Due to input from our domain 
expert / students on the relevance of the generated descriptors produced by the three models, we decided to 
proceed with the Google News model, as the descriptors generated were found to be more relevant to the 
domain-specific task. As a result, analyses presented in the following section were obtained from descriptors 
generated by the Google News Word2Vec model.

Results 

As mentioned above, the domain experts processed the top 25 similar words for each word in the Costume 
Core vocabulary. We also created files of the top 20, 15, 10, and 5 most similar words, as generated by the 
models. Below are the plotted graphs of cosine similarity score (x-axis) versus the percentage of words 
having that cosine similarity score (y-axis), for both model-generated and reviewer-confirmed tokens. As 
expected, the two graphs in Figure 5 show that the top 5 most similar words generated by the models have 
higher cosine similarity values on average than the top 25 most similar words. 

Figure 4: MOCHA application. Column 1 stores descriptors not selected by our reviewers, column 2 stores 
descriptors currently being processed, and column 3 stores descriptors confirmed by our reviewers.

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However, the measure of the model’s efficacy in predicting descriptors is displayed in the gap between the 
cosine similarity scores of the confirmed descriptors and the overall generated descriptors. If the model’s 
predictions are accurate, we would expect words with higher cosine similarity scores to have a larger chance 
of being confirmed by our domain experts. 

Figure 5: Comparison of top n overall terms (left) with top n confirmed terms (right), plotting cosine 
similarity score against the percentage of descriptors with the same score.

Figure 5: Comparison of top n overall terms (left) with top n confirmed terms (right), plotting cosine 
similarity score against the percentage of descriptors with the same score.

As seen from Figure 6, there is a clear distinction between the original, model-generated descriptors and 
the descriptors that were actually confirmed. To further demonstrate this difference, we show the relative 
averages of confirmed and overall descriptors in Table 1. Consistently, the confirmed descriptors had a 
higher cosine similarity score on average than the overall model-generated descriptors. 

In Table 1, we see that consistently, across multiple different groups of generated vs confirmed terms, the 
confirmed terms had higher cosine similarity scores on average. This gap between generated and confirmed 
terms leads us to believe that our model was effective at generating high-quality descriptors, as descriptors 
which the model deemed more likely to be included in the schema (higher cosine similarity score) were 
indeed selected more frequently on average, indicating that the model performed well here.

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Table 1: Relative averages of confirmed and overall descriptors.

Top 25 Top 20 Top 15 Top 10 Top 5

Confirmed CS Score 0.6063 0.6145 0.6244 0.6329 0.6575

Overall CS Score 0.5688 0.5777 0.5901 0.6071 0.6370

Hit rate 14.2% 15.6% 17.8% 21.3% 27.5%

Additional statistics on percentages of term cosine similarity scores - hit rate = % of generated descriptors that were confirmed.

Ethical considerations 

The main objective of this work was to minimize the confusion felt by many costume collection users by 
providing an expanded set of metadata descriptors for use in cataloging efforts. An important priority for us 
was to ensure that our generated descriptors were diverse enough to encompass a wide array of periods and 
cultures, in order to avoid discriminatory exclusions of garment types. In pursuit of this goal, we used models 
trained on a wide variety of different sources to generate our descriptors, ensuring that these models would 
have been exposed to data from many different areas, hopefully alleviating many of these potential concerns. 
In addition, our human-in-the-loop approach allowed us to exclude potentially negative or harmful terms 
from being added to our schema by providing an additional layer of protection.

A point that may be considered is the impact of the human-in-the-loop approach to term biases—did the 
fact that we had one final reviewer potentially cause an opportunity for bias to be created? To combat this, 
our initial term selections were made by separate reviewers, so that our fashion metadata expert only made 
final confirmation decisions on descriptors judged to be valuable from a variety of sources. Additionally, our 
fashion metadata expert has extensive experience with different terminologies and is knowledgeable on best 
practices in the field, two characteristics which help to ensure the quality of our schema. These measures, 
taken on both the generation and filtration ends of the process, serve to minimize the risk of potentially 
harmful terms being added to the schema. However, despite these precautions, it may still be valuable to 
consolidate opinions from a variety of stakeholders, such as users, curators, and collection managers.

Who is affected by this project? 

Our efforts to use NLP to select accurately controlled vocabularies can benefit any professional or institution 
that manages fashion collections, artifacts including private archives and museums owned by fashion 
companies such as Michael Kors and Armani Silos (Franceschini 2019), regional historical societies/
museums, and university fashion study collections (Green and Reddy-Best 2022) as it would allow them 
to select from controlled vocabularies that precisely describe and catalog artifacts. This project can also 

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benefit digital librarians and other personnel collaborating with fashion domain professionals to create 
online digital libraries. Because the use of NLP has led to the addition of accurate/sufficient metadata 
elements to Costume Core, which provides a means for structuring data (Kirkland 2018), digital librarians 
can more easily map Costume Core vocabularies to those of pre-existing schemas such as Dublin Core when 
preparing to export metadata to online portals and aggregators.

Several university fashion collections have committed to using the Costume Core metadata schema to 
support two inter-institutional projects that aim to contribute to initiatives to standardize metadata across the 
historic dress and fashion domain (Kirkland 2018). In addition, standardizing the metadata has implications 
for online users of digital fashion collections. Without standardized metadata, online users may experience 
failed searches which limit the reach and accessibility of online fashion digital collections. Thus, the benefits 
of NLP may extend to online users as it contributes to an initiative to standardize metadata within the 
fashion domain.

Lessons learned and future work 

While our process was fairly straightforward, there were a few issues we encountered along the way that, if 
not properly addressed, could have become stumbling blocks. One such area was our choice of model used 
to generate the terms. Two models we originally attempted to use were deemed unsatisfactory for our use 
case due to the low quality of terms generated. However, trying a variety of models allowed us to select a 
model, Google News, with excellent representations of our space. The model outputs and processing code 
are currently being prepared for dissemination, but the categorization tool code (MOCHA) is available.

Another issue we encountered was that of sharing our web application. Bundling up the tool and sending 
the files via a messaging service seemed likely to cause version control issues as well as potentially being 
difficult to set up for non-technical users. As a result, we found it efficient to set our application up on AWS 
Lightsail, a virtual server designed for running lighter-weight applications like MOCHA. This provided an 
easily accessible platform for our fashion students and domain experts to use while allowing us to perform 
minor updates easily, without needing to resend large files after every update.

As for future work, we would like to create a visual thesaurus tool with Costume Core metadata (old and 
new) to help catalogers choose the most accurate term(s). Also, because (Kirkland et al. 2023) found that 
users searched for holdings on historical collection websites using retail or lay terminology, future work 
will / may include reviewing fashion lay and retail terms, comparing them with other established controlled 
vocabularies, including the International Council of Museums (ICOM) Vocabulary of Basic Terms for 
Cataloguing Costume and Getty Art and Architecture Thesaurus, and adding them to Costume Core.

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Data Availability
The data presented in this study are available on request from the corresponding author.

Acknowledgements
This project was made possible by an internal grant from the Virginia Tech University Libraries.

The research case study was developed as part of an IMLS-funded Responsible AI project, through grant 
number LG-252307-OLS-22.

Competing Interests
The authors declare that they have no competing interests.

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