Assessing knowledge organization systems from a gender perspective: Wikipedia taxonomy and Wikidata ontologies

2.1 Gender gap in Wikipedia

The gender gap on Wikipedia has been the subject of extensive academic research, with numerous studies exploring biases in content, participation, reading and potential strategies to address this gap. These studies emphasize the importance of recognizing and addressing biases and barriers to create a more diverse and inclusive Wikipedia community (Ferran-Ferrer et al., 2023).

The under-representation of women as editors and as subjects of biographical coverage is a widely recognized issue in the academic field (Hube, 2017; Falenska et al., 2021). Some articles discuss how gender bias intersects with race, sexuality, security and marginalization on Wikipedia (Lam et al., 2011; Ju and Stewart, 2019; Tripodi, 2023). Various factors, such as the demographics of editors, platform structure and cultural values, contribute to these biases, which have significant social implications, affecting the visibility and participation of women and perpetuating existing disparities (Ford and Wajcman, 2017).

Regarding the gender gap in content, research reveals that women are underrepresented among the main figures in all language editions of Wikipedia (Miquel-Ribe and Laniado, 2021). Articles for deletion is a possibility within the decision-making process in Wikipedia article editing. It is the process that determines what constitutes knowledge and what does not in the encyclopedia. Biographies of women and LGBTQ+ individuals are often subject to deletion, resulting in a higher proportion of biographies of women nominated for deletion compared to biographies available about men (Morgan et al., 2013; Hollink et al., 2018; Tripodi, 2023). While there are indications of bias, some authors conclude that there is no clear bias resulting from deletion activity (Worku et al., 2020).

Studies also identify significant gender differences in Wikipedia content, such as biographies of women featuring more prominent family, gender and relationship themes (Wagner et al., 2016). Linguistic bias in terms of language abstraction and positivity can be observed, along with structural differences in metadata and hypertext links. In addition, citation practices reveal that female authors are cited less than expected, suggesting a preference for citing male publications (Zheng et al., 2022). These biases may further marginalize female authors, especially in non-Anglophone countries. The gender gap in content creation and participation on Wikipedia perpetuates an unbalanced coverage of topics, creating a cycle where the lack of diversity in content fails to attract and engage different editors, thus exacerbating the existing gender gap (Konieczny and Klein, 2018).

Research on the gender gap in editing and participation highlights various barriers that hinder women’s involvement on Wikipedia. These barriers include negative reputation, lack of recognition, fear of deletion, rejection and alienation. Often, research suggests that women lack confidence in their abilities, feel uncomfortable with editing and face negative responses to constructive feedback (Collier and Bear, 2012). Factors such as the digital skills gap (Gardner, 2011) and the availability of time for editing (Gruwell, 2015) also contribute to the gender gap. However, visible female editors and constructive comments can help mitigate the gap, as the presence of visible female peers promotes collaborative editing (Evans et al., 2015). Some authors have investigated the gender gap in Germany and suggested a proactive approach to training and educating women to enhance their motivation for writing (Buchem and Kloppenburg, 2013). It has also been highlighted the impact of family responsibilities on women’s ability to write, so efforts may need to focus on addressing gender disparities in domestic work (Ferran-Ferrer et al., 2021).

The gender gap extends beyond editing and includes the underrepresentation of individuals. Female participation varies by topic, with a greater presence in gender studies or feminism categories, reflecting traditional gender stereotypes. Generic site restrictions limit the digital credibility and authority of women, hindering their contributions. The complex relationship between the gender gap and harassment requires better understanding, and it is important to create a safe environment for women on and off Wikipedia. Feminist interventions, such as exclusive edit-a-thons for women, have proven effective in countering gender inequality on the platform.

2.2 Gender and Wikidata

The gender gap on Wikidata has been extensively explored in academic research. We can delineate three main categories of studies. A first set of research has delved into the gender gap within Wikidata, presenting diverse methodologies, findings, and recommendations to address this disparity. Meanwhile, a second set aims to quantitatively assess the biographical gender gap in Wikipedia, across various language editions, leveraging Wikidata’s multilingual support to facilitate this cross-cultural research. Lastly, a third set of studies emphasize the advocacy and visibility of content pertaining to women in industries traditionally dominated by men, utilizing Wikidata for this purpose.

Regarding the initial group of discussions aimed at presenting diverse methodologies, findings and recommendations to address this disparity, Zhang and Terveen (2021) delved into the gender content gap in Wikidata, seeking to uncover the source of bias. Through a quantitative case study, they examined how individuals were represented in Wikidata compared to existing gender biases. Their findings revealed a prevalence of male-dominated professions among the most frequently represented categories, closely mirroring real-world gender distribution.

Similarly, Abián, Meroño-Peñuela and Simperl (2022) sought to understand the impact of content gaps in knowledge graphs on downstream applications, with a particular focus on gender disparities within Wikidata. To achieve this, they introduced a framework that compared edit metrics with Wikipedia pageviews, facilitating a quantitative evaluation of discrepancies between knowledge graph content and user needs. As a result, they identified no inherent gender or recency gaps within Wikidata’s production, with only a few under-represented entities standing out. A group of articles has focused on analyzing gender bias on Wikidata concerning occupations or professional domains. In this line, Das et al. (2019) conducted a holistic analysis of bias measurement on the knowledge graph, specifically focusing on biases in Wikidata across different demographics selected from seven continents. They utilized extensive experiments on a wide range of occupations sampled from various demographics, examining the impact of algorithm bias on the measurement of biased occupations. Results indicated that the inherent data bias in Wikidata can be influenced by specific algorithm bias and underscored the importance of understanding biases based on sociocultural differences across demographics. Within this same field, there are three works that concentrate on specific occupations or professional domains:

Lemus-Rojas and Lee (2019) in the STEM fields, Zhu et al. (2023) in Chinese culture and heritage, and Conroy (2023) in French and Francophone literature. The outcomes align with the conclusions observed in the aforementioned comprehensive studies. In the first two cases, Wikidata is highlighted as a critical collection for enhancing the visibility of women. Conroy (2023) found that the gender gap in both subsets closely resembles the global average, with a higher-than-average representation of writers of other genders.

Finally, Pellissier and Suchanek (2019) and Bourli and Pitoura (2020) analyzed gender bias on Wikidata through advanced automated processing techniques. Pellissier and Suchanek (2019) proposed a system to index changes in the Wikidata graph and enable users to answer complex SPARQL queries regarding historical changes, while Bourli and Pitoura (2020) introduced measures for identifying bias in the dataset, tested methods for amplifying bias in embeddings, and introduced a debiasing approach. A special case is Mandiberg and Sarıoğlu (2022), who aimed to address the challenges associated with defining a dataset to analyze changes in Wikipedia’s gender gap for articles about visual art. The dataset is constructed from the intersection between Wikipedia and Wikidata. The researchers describe the process of using a topic model algorithm to identify a dataset by analyzing the words within each article and grouping articles into topics. Their aim was to create a dataset that more closely reflects visual artists' articles on English Wikipedia, addressing potential systemic biases. The topic model algorithm provided a dataset that encompassed a majority of the two WikiProject datasets and the Wikidata sets, while adding additional art-related individuals. It was found to be superior to other options, offering a detailed list of articles about visual arts that mitigated Wikipedia’s existing imbalances. The study also highlighted challenges in Wikidata’s taxonomies and called for further research on systemic biases reflected in taxonomy systems.

A second set of articles addresses the application of Wikidata, capitalizing on its multilingual capabilities to facilitate comprehensive cross-cultural research, for measuring gender bias in Wikipedia editions and for resolving this issue. Three of these studies feature contributions from Maximilian Klein and Piotr Konieczny. Klein and Konieczny (2015) and Konieczny and Klein (2018) introduce the Wikipedia Gender Inequality Indicator (WIGI) developed from Wikidata. WIGI calculates, for each country, a score based on the ratio of female and nonbinary gendered biographies to the total number of biographies. This Wikipedia-derived indicator is correlated with four contemporary, widespread gender inequality indices (GDI, GEI, GGGI and SIGI). Through analyzing methodologies and the relationship with Wikipedia data, evidence suggests that the bias in Wikipedia’s biographical coverage is aligned with gender bias in socially powerful positions. Concerning the results, Klein and Konieczny (2015) find that the strongest correlations are with individuals born around 1,910, indicating that Wikipedia’s representation may more accurately reflect current rather than historical gender statuses. The same authors Konieczny and Klein (2018) utilize cultural clusters to highlight how gender inequality can be examined through diverse cultural perspectives.

Klein et al. (2016) delve deeper into the gender bias of content, focusing on women’s biographies on Wikipedia. The article underscores the importance of precisely measuring the gender content gap and the critical examination of initiatives intended to mitigate this disparity. The team formulates the Wikidata Human Gender Indicators (WHGI), a robust, longitudinal dataset to monitor gender disparities. It monitors biographical data across multiple facets – such as time, geography, culture, occupation and language – providing an extensive instrument for elucidating and quantifying the gender bias in Wikipedia’s content. The research signals a changing representation of women in 11 dimensions utilizing WHGI. Validations against three external datasets back the indicator’s accuracy, and reassessment of Wikipedia’s gender bias with WHGI suggests that it could enhance depth and impact in future research on the subject.

In a similar line of work, Hollink et al. (2018) tackle the challenge of measuring gender inequalities on Wikipedia, especially when considering multiple languages. The difficulty in finding objective methods to measure and compare gender inequality is underlined, and the potential differences across language editions of Wikipedia are acknowledged. Their methodology focuses on comparing coverage of male and female Members of the European Parliament (MEP) across various Wikipedia language editions using open data. This approach allows for a fair comparison due to the MEPs' notable actions in the real world, and it examines gender discrepancies in both the coverage on Wikipedia and the content within Wikidata entries. An analysis of Wikidata entries for male and female MEPs reveals equal amounts of property-value pairs, contradicting earlier studies that found Wikipedia content related to women emphasized family and relationships. Differences related to real-world disparities suggest that the structured data of Wikidata might be less prone to bias. Moreover, aggregation of data from various Wikipedia language editions might contribute to a more diversified and equitable dataset in Wikidata.

Delving into the characteristics and virtues of Wikidata, Hermoso Pulido (2021) discuss how Wikidata has become a significant tool within the Wikimedia ecosystem, improving data linkage and reuse. Specifically, it mentions the adoption of Wikidata in Catalan Wikipedia, noting how its integration with infoboxes and list generation has advanced the project. The article suggests that such technical innovations could be part of the solution in addressing Wikipedia’s gender gap. Methodology highlights the use of structured data from Wikidata to evaluate new biographical articles, aiming to encourage user engagement in diversity issues and track vandalism or errors. This methodology suggests a proactive approach to using structured data for maintaining quality and diversity in biographical content, directly impacting the reduction of Wikipedia’s gender gap. Technical challenges are highlighted, such as execution timeouts during SPARQL queries for live data analysis. While some limitations exist for large datasets, initiatives like WCDO show promise in identifying and acting upon content gaps. The article advocates for enhanced cross-collaboration between Wikidata and Wikipedia, suggesting that embedding certain tools could encourage editors to address discrepancies more effectively.

Leveraging the potential of Wikidata, Laouenan et al. (2022) focus on studying different intersectionalities, specifically, they aim to construct a comprehensive and accurate database of notable individuals by cross-verifying the information from various editions of Wikipedia and Wikidata, focusing on specific social science questions about gender, economic growth, urban and cultural development. The researchers collected a significant amount of data from Wikipedia and Wikidata, utilizing deduplication techniques and cross-verifying the retrieved information. They found varying degrees of completeness and error rates dependent on notability distribution, classifying the presence of an Anglo-Saxon bias in the English edition of Wikipedia. The strategy resulted in the creation of a cross-verified database of 2.29 million individuals, shedding light on an Anglo-Saxon bias in the English edition of Wikipedia. The study also emphasized the implications of this bias and identified individuals not present in the English edition of Wikipedia.

Finally, the last research strand in this set of papers aims to emphasize the promotion and visibility of content related to women in male-dominated professional spheres through the utilization of Wikidata. Among these, two articles are authored by Thornton and Seals-Nutt, both affiliated with the Stories Services Collaborative. Thornton and Seals-Nutt (2018) introduce the creation of a web application called Science Stories. This application utilizes structured data from Wikidata along with images to narrate compelling science stories, especially focusing on the experiences of women who have contributed to scientific research. The primary goal is to elevate the visibility of these women. The authors illustrate how the use of free software and open standards can lead to the development of visually captivating and interactive science communication experiences. These experiences involve the integration of images with structured statements within a web of interconnected data, all supported by references to published sources. Four articles focus on leveraging Wikidata to promote and illuminate the contributions of women in male-dominated professional fields. In a similar vein, Thornton et al. (2022) delve into how Semantic Web capabilities can consolidate disparate materials to craft narratives, as demonstrated by the WeChangEd research project, which centers on women editors of periodicals in Europe from 1710 to 1920. The methodology involves developing applications that aggregate data from Wikidata to harness a versatile knowledge graph, facilitating the swift creation of interactive platforms to captivate fresh audiences. The outlined process holds potential value for researchers and cultural heritage institutions seeking web-based avenues for presenting data-driven storytelling.

4.1 Inspection of standards and guidelines for the evaluation of taxonomy (Wikipedia) and ontologies (Wikidata)

Our study begins by reviewing the most widely accepted standards for the analysis of KOSs, and using them as the basis for designing an evaluation guide tailored to the taxonomic and ontological criteria relevant to Wikipedia and Wikidata. Subsequently, we employed a standards inspection method to assess whether the KOSs of Wikipedia and Wikidata conform to the international specifications and standards defined by recognized organizations.

In the theoretical framework of our study, we draw upon the taxonomic classification proposal of (Souza et al., 2012) and the critical insights of Mazzocchi (2018) into KOS. These foundational works underpin our proposed evaluation criteria for Wikipedia and Wikidata.

Specifically, in the context of Wikipedia, Albuquerque (2017) presents an information architecture framework for the development and management of controlled vocabularies in the context of programming vocabulary projects. Kaplan et al. (2022) introduce an evaluation method for taxonomies, including structural quality criteria such as generality, appropriateness-attainment and orthogonality, and provide generalized metrics for quantifying generality and appropriateness.

In the domain of ontologies, da Costa et al. (2022) provide an updated review of software architectures, including ontology usage for managing large volumes of data. Wilson et al. (2022) outline a methodology for evaluating ontology quality that considers intrinsic and extrinsic aspects. Amith et al. (2018) offer insights into ontology evaluation within the field of biomedical KOS, which we adapt for evaluating Wikidata. Bolotnikova et al. (2011) propose practical methods for ontology evaluation, especially in automated contexts. Aghaebrahimian et al. (2022) explore the validity of Wikipedia categories for topic labeling, further contributing to the development of our evaluation criteria.

The extrinsic criteria (Kless and Milton, 2010) assess the measurement of external qualities, their application and the domain, making reference to elements of the outcome as experienced by users. In contrast, quality indicators analyze aspects of structure and domain independently of their use in application contexts. To gain a comprehensive understanding of the efforts to unify the reviewed theories and the proposed methodology for ontology evaluation, see Table 2.

4.2 Proposed heuristic evaluation of taxonomies

Heuristic evaluation, aiming to assess whether the taxonomy of Catalan Wikipedia, complies with the standards of sound knowledge organization not only concerning user experience but also formally within the realm of KOS. Based on the theoretical framework, a selection of indicators were selected that have been highlighted in our analysis and achievable with the access and technical resources available to us (see Table 3). When identifying and measuring these indicators, we have considered contributions from specialists and specific standards within the KOS sector, particularly taxonomies, to conduct an inspection analysis of Wikipedia’s category scheme.

4.3 Analysis of usage logs for the profession case study on gendered professions

For the analysis of logs of the Catalan edition of Wikipedia we have used Pageviews Analysis (https://pageviews.wmcloud.org) which is a suite of eight tools designed for the examination of page views and unique device statistics on Wikimedia Foundation wikis. These tools, namely Pageviews, Langviews, Topviews, Siteviews, Massviews, Redirect Views, Userviews and Mediaviews, collectively form a comprehensive toolkit for data analysis. The foundation of these tools relies on data sourced from Wikimedia’s RESTBase API, which is structured in alignment with the definitions outlined in the Research: Page view and Research: Unique Devices documentation. Presently, this suite of tools is under the maintenance and stewardship of Community Tech.

To address this analysis, we have chosen the field of professions, and based on state statistical data (INE: Instituto Nacional de Estadística, 2024), we have selected the most masculinized (STEM) and feminized professions (nursing, library science and teaching) in Spain.

4.4 Heuristic assessment concerning structure and coverage

It is essential to clarify that in Wikidata, property P21 encompasses both gender and sex. However, it is crucial to recognize that these two terms pertain to distinct aspects of human identity and biology. Sex is primarily associated with an individual’s physical and genetic characteristics and has historically been classified into two categories: male or female. In contrast, gender is a social and cultural construct that encompasses a broad spectrum of roles, behaviors, expectations and identities. It extends beyond a binary system, acknowledging that people can identify as male, female, both, neither, or a different gender altogether. It is imperative to comprehend the differentiation between sex and gender, as it is fundamental for fostering inclusivity and honoring the diverse experiences and identities of individuals (García Dauder and Pérez Sedeño, 2017).

Apart from this feature of gender or sex of Wikidata, the members of the Ontology project have identified the limitations that make it not qualify as a proper ontology (Wikimedia, 2022). These limitations can be divided into two groups. The first group was initially identified in WikidataCon 2021, and they are aimed at overcoming barriers to the reuse of data by other services and projects. And the second group is considered to be issues existing in the knowledge representation in Wikidata. In the context of this study, we are primarily interested in the first group, as it identifies elements to overcome if it is to be applied in the categorization of Wikipedia content.

Based on the barriers to reuse formulated by the project members, we present examples related to the classes that make up the range restriction of property P21 (gender or sex). The indicators have been selected considering their relevance and their suitability for the retrieval of gender-related articles; however, this can be extrapolated to other evaluator needs.

4.5 Performance of the Wikidata search system

The data from Wikidata can be used for various purposes. Beyond the specific querying of an item or a set of items, Wikidata provides users with methods of data access for linking data without having to download it to another server, for enriching third-party data, or for generating local search services. In all cases, Wikidata’s data can be consumed by human users or by automated systems or bots (Wikimedia, 2023b).

In one of the Wikidata guides, “Data Access” (Wikimedia, 2023a), eight methods for accessing Wikidata data are identified and described, three of which are oriented toward direct interaction with users who need to retrieve limited quantities of results (See Table 4).

All methods of accessing Wikidata data operate on a foundation formed by the RDF data management system, or RDF repository, Blazegraph (Vrandečić et al., 2023) (see Table 5).

Undoubtedly, these figures are impressive and represent the largest open secondary database currently in existence. Nevertheless, in recent years, assessments of the degree of compliance with processes, accessibility and the use of search services have shown worrisome signs of stagnation. The Wikidata authorities are fully aware of these limitations and, in fact, have set their sights on the need to replace the underlying software of Wikidata, Blazegraph, with one that can better address the challenges of growth and quality.

And, regarding the ontology inconsistencies we mentioned earlier, the evaluation requirements established incorporate the use of more advanced integrity-checking languages than SPARQL functions. Specifically, the WDQS report refers to the Shapes Constraint Language, or SHACL. SHACL allows for graph validation and includes not only the ability to specify a severity level for validation results but also the possibility of providing suggestions on how to fix the data if a validation result occurs.

The performance assessment of Wikidata follows the overarching evaluation framework introduced by Malyshev et al. (2018). The performance tests cover the period from 2015 to 2022, as specified by the SPARQL query service (Everett, 2015).

5.1 Heuristic evaluation of SOK Wikipedia

The heuristic evaluation of the Catalan Wikipedia category scheme has been carried out using the technique of standards inspection, in which a usability expert analyzes whether the interface follows the agreed-upon specifications and the standards defined on an international level. In the case at hand, a set of identified indicators has been generated, particularly based on normative sources. These sources also provide us with methods for obtaining evidence for each indicator, the applied metrics, and, when possible, optimal values.

• a) Evaluability

The category schema of Wikipedia is valuable because category creators have various agreed-upon tools for their practice. We highlight the following:

• Categorization guideline (Wikimedia, 2023d) resulting from the discussion and decision-making process specific to the encyclopedia

• Help for category creators: Help:Category (Wikimedia, 2018) and the “Style Book on Categorization” section in the Categorization guideline (Wikimedia, 2023d)

• Templates for category creators: Category:Maintenance templates for categories (Wikimedia, 2015)

• There is a control over the pages that do not contain categories, for maintenance purposes.

The level of knowledge about these tools has been informally assessed with some individual administrators of the Viquipèdia community, and their lack of awareness regarding them has been conveyed.

• b) Reusability

Each category has a unique instance and a single identifier, regardless of its various locations within the schema hierarchies. Wikipedia’s schema categories are available for database dumps in three data interchange formats: sql, json and xml. These formats do not provide semantic information about the concepts and relationships between the concepts in Wikipedia’s categorization schema, as the simple knowledge organization systems (SKOSs) data model could. Consequently, the possibilities for reusing Wikipedia categories in other datasets or information retrieval systems are greatly restricted.

• c) Stability

The most notable stability-related metrics during the period 2004–2022 can be seen in Table 6.

The annual growth rate remained high during the period 2004–2007, and from 2008 onwards, it experienced a significant decline until 2012. Starting from that year, the rate demonstrates a gradual reduction in the increase, and it stabilizes until 2020 when it experienced a very remarkable increase. This increase may be linked to one of the effects of the Covid-19 pandemic: greater availability of time for contributing to the Catalan edition of Wikipedia.

The most abundant categories are in the fields of science and culture, followed by technology, humanities and events. The three areas with the fewest categories are biographies, information and places.

• d) Number of categories (concepts)

As of December 31, 2022, Wikipedia’s category schema included 102,159 categories. We can compare this size with other similar KOSs that consist of pre-coordinated concepts and aim to represent encyclopedic knowledge.

The List of Subject Headings of the National Catalan Library (LEMAC) contains 112,200 headings, considering both accepted and non-accepted ones. It originates from the translation of the Spanish version of the Library of Congress Subject Headings (LCSH), which was preliminarily published by the Library Services of the Generalitat de Catalunya in 1988.

Making growth rate comparisons between Wikipedia’s categorization schema and LEMAC is challenging. Wikipedia’s schema is relatively young, still in its first two decades of existence, while LEMAC has been in existence for 35 years, with an even longer history if we consider its origins. However, the following examples show clear indications of faster growth in Wikipedia’s categorization schema.

In 2009, LEMAC experienced a growth of 2,663 new headings, whereas Wikipedia added 8,080 new categories. By 2021, LEMAC’s growth amounted to 1,219 new headings, while Wikipedia introduced a staggering 8,616 new categories.

The magnitude of Wikipedia’s categories and its growth rate are not comparable to those of KOS applied to digital encyclopedias. The knowledge tree of the Gran Enciclopèdia Catalana (GEC) comprises 425 categories, and the categories in the Encyclopedia Britannica total 123. In both cases, the hierarchy of the KOS is restricted to two levels.

• e) Number of semantic relationships

We lack access to complete data on all hierarchical relationships between supercategories and subcategories. Still, we have a partial count covering the first three levels of the categorization schema, specifically involving main thematic categories and their second-level and third-level subcategories, resulting in 1,122 hierarchy relationships.

• f) Enrichment or granularity index

In the first three levels of the category schema, there are 143 categories and 1,122 hierarchy relationships. The corresponding average enrichment index is 7.8461, significantly surpassing the optimal range, usually between 2 and 5.

• g) Degree of precoordination

In the entirety of Wikipedia’s category schema, the average number of words that make up category labels is 3.6766, exceeding the maximum value typically recommended by experts, which is between 1.5 and 2 words. Other data indicating a deviation from this optimal value include the median number of words in category labels, which is three. However, there are exceptions, with some category labels having a maximum of 18 words, such as in the case of “Resolucions del Consell de Seguretat de les Nacions Unides sobre el Tribunal Penal Internacional per a l’antiga Iugoslàvia" (Resolutions of the United Nations Security Council on the International Criminal Tribunal for the former Yugoslavia).

• h) Number of levels in hierarchy or depth

For the evaluation of this indicator, we reviewed all hierarchical chains within the main thematic category “Biographies.” In all cases, we found more than five levels. Consequently, this exceeds the maximum value recommended by experts.

• i) Number of categories in the same hierarchy level or breadth

To assess this indicator, we examined the first two levels of subcategorization beyond the eight main thematic categories. In total, this section includes 144 categories, with 130 of them containing subcategories, while the remaining 14 link directly to Wikipedia pages.

Among these 130 categories, there are 17 that have only one subcategory, constituting 13.07% of the assessed section, and in fact, this is the most common case. These instances violate the minimum requirement of two subcategories recommended by experts.

Additionally, there are 35 categories with more than 12 subcategories, making up 26.92% of the evaluated section. These cases exceed the maximum of twelve subcategories suggested by experts. The highest breach of this limit occurs in the “Religion” category, which includes 43 subcategories.

When we sum up violations of both the minimum and maximum subcategory limits, we find that 40% of the assessed categories do not adhere to the optimal values of breadth (52 out of 130 categories).

5.2 Usage logs for the case study Wikipedia

To provide a glimpse of Viquipèdia usage (Catalan Wikipedia), the total number of viewed pages of the Catalan Wikipedia in one month is 49,338,638 and the number of unique devices accesses is 3,480,772 in September 2023 (Wikimedia, 2023c).

And in this section, we present the data derived from our analysis of log entries for feminized professions (such as librarians, nurses and teachers), juxtaposed with STEM professions (see Table 7). The table encompasses two categories: “Feminized Professions” and “STEM Professions”. Each category is further broken down into specific professions, and the corresponding user visualizations statistics are presented. We examine access patterns beginning in June 2023, focusing on the Catalan edition of Wikipedia, and encompassing data from various devices. The table illustrates the engagement and activity levels across different professions within feminized or masculinized professions.

The findings from the examination of feminized professions reveal that there is an average of 798 monthly accesses, with a mean of 2 editions monthly. And the outcomes obtained from the examination of STEM professions indicate that there is an average of 1,073 monthly accesses, with a mean of 0 editions monthly.

5.3 Heuristic evaluation of Wikidata

These are the results of the heuristic evaluation of the ontologies of Wikidata. Examining Table 8 reveals significant challenges stemming from unproductive class hierarchies in navigation and search. Users are constrained from selecting multiple individuals of the same type within nodes, introducing complexity in search contexts. This limitation hampers quick decision-making, impacts reasoning by disrupting inference and consistency assessments and impedes automated interoperability in data cooperation. Ascending through upper chains in search contexts confuses users and complicates processes in both search and axiom-based reasoning. The inability to determine generality or specificity transfers the challenge to the search process, hindering statement validation and new knowledge inference. Overall, unproductive class hierarchies present intricate obstacles across various facets of ontology usage.

5.4 Performance Wikidata

An in-depth assessment of Wikidata’s performance from 2015 to 2022 is presented in Table 9, utilizing the framework established by Malyshev et al. (2018).

Table 9 furnishes a comprehensive evaluation of query performance within the Wikidata platform through several key metrics. Under the section denoted as “Query Metrics,” distinct trends come to the forefront. Notably, a substantial count of “Good Queries” signifies operations executed with success, contributing significantly to the platform’s operational prowess. Conversely, a noteworthy number of “Bad Queries” denotes instances where queries faced issues or failed to deliver intended results, thereby illuminating potential areas for refinement.

Moreover, the metric detailing the “Total Query Execution Time” offers a panoramic view of Wikidata’s efficiency, encapsulating the cumulative time required for executing the entire array of queries. This temporal dimension serves as a pivotal indicator of the platform’s responsiveness. In tandem, the metric revealing the “Total Result Rows” speaks volumes about the sheer magnitude of information generated across the spectrum of queries conducted on Wikidata. This voluminous outcome underscores the platform’s extensive capacity in producing relevant and diverse information.