Exploring Cross-Language Differences in Wikidata-based
Hyperlink Types for Enhanced Editorial Support on Wikipedia
Nhu Nguyen

Hideaki Takeda

nhunt@nii.ac.jp
Graduate University for Advanced Studies, SOKENDAI,
National Institute of Informatics
Tokyo, Japan

National Institute of Informatics
Tokyo, Japan
takeda@nii.ac.jp

ABSTRACT
Wikipedia has become one of the most widely used language resources in more than 330 languages, attracting contributions fromeditors around the world. However, a considerable gap still exists
among language editions, encompassing variations in article count,subject coverage, and even the number of community editors. To
bridge this gap, research efforts have sought to capitalize on diverse
factors such as cross-language integration and hyperlink utilization.
Despite of these efforts, the full potential of hyperlinks remains
unexplored. Therefore, in this study, we introduced a novel approach to explore the hyperlinks by focusing on hyperlink types
based on Wikidata. We aim to extract and analyze the patterns
associated with these hyperlink types across different languages,
then use them as the recommendation solution to enhance theeditorial activities of editors. In our initial collaborative filteringexperiment, we observed improved performance when combining
multiple languages, rather than using a single language.

CCS CONCEPTS• Information systems → Evaluation of retrieval results; •Computing methodologies → Cross-validation.

KEYWORDS
Wikipedia, Wikidata, Hyperlink, Recommendation, Collaborative
Filtering
ACM Reference Format:
Nhu Nguyen and Hideaki Takeda. 2018. Exploring Cross-Language Differences in Wikidata-based Hyperlink Types for Enhanced Editorial Support on Wikipedia. In Proceedings of The 12th International Joint Conference on Knowledge Graphs (IJCKG ’23). ACM, New York, NY, USA, 6 pages.
https://doi.org/XXXXXXX.XXXXXXX

1INTRODUCTION

In recent years, Wikipedia has become a big source of information innumerous languages thanks to the local communities of editors [15].
Wikipedia currently stores more than 20M topics in multi-domain.
Notably, the English edition stands as a significant contributor,
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https://doi.org/XXXXXXX.XXXXXXX

covering about 6M topics, constituting a noteworthy one-third
proportion[21]. Furthermore, the top 15 languages (https:// meta
.wikimedia.org/wiki/ List_of_Wikipedias) in terms of article counthave collectively accounted for nearly half of Wikipedia’s total
articles [1]. It becomes evident that a significant disparity exists
among language editions, with smaller language editions lacking
a considerable number of topics and receiving comparatively lessattention. In addition, this disparity also extends to articles with thesame topics across different languages. The primary source of thesedifferences largely stems from Wikipedia’s open editing policy
and the community of editors. Researchers have tried to bridge
this gap by leveraging cross-language links and hyperlinks among
languages, aiming to support weaker languages. Nevertheless, many
aspects have not been thoroughly examined and explored [1].
Therefore, we propose the novel exploration of Wikidata-based
hyperlink type in supporting languages and editors. A hyperlink
type is a classification or category assigned to a hyperlink and determined through the “P31: instance of” relation in Wikidata. Each
Wikipedia article is associated with a unique identifier in Wikidata. These identifiers reinforce Wikipedia’s collaborative natureby connecting diverse language editions of the same article. This
allows for easy exploration of topics and concepts on a global scale[21]. Our objective is to investigate the disparity in hyperlink types
among cross-language articles. Additionally, we aim to leverage
this information for the recommendation system and assess the effectiveness of our approach in various language aggregations. This
enables us to offer relevant recommendations to support editors
in creating and updating Wikipedia articles, particularly for small
communities.
In the experimental phase, we employed collaborative filtering
for our recommendation system on the acquired data from three languages: English, Japanese, and Vietnamese. Although both Japaneseand Vietnamese are ranked within the top 15 Wikipedia editions byvolume, they still lag significantly behind English. Furthermore, thecommunities of editors in these two languages are notably smallerwhen contrasted with the English community (refer to Fig. 1). Thus,
using these languages can also be sufficiently representative toassess the effectiveness of our approach. The experimental results
demonstrate that the collaborative filtering models perform better, and a greater number of recommendations are obtained on
multilingual data.
The remainder of this paper is organized as follows. After reviewing the related works (Section 2) and introducing the problem
statement (Section 3), we describe the statistical analysis of data
in Section 4 and hyperlink type recommendation in section 5. The

IJCKG ’23, December 08–09, 2023, Tokyo, Japan

Nhu Nguyen et al.

Table 1: The number of pages in three languages

3

Figure 1: The number of editors in local communities.
(https:// stats.wikimedia.org/ #/ all-projects)

experiments are presented in Section 6, followed by concluding
statements in Section 7.

2

RELATED WORKS

Previous research has identified the information gap among different Wikipedia editions and attempted to bridge it [10], [4],[5],[12],[9].
Hyperlinks and cross-language links are essential data elements
leveraged in this task. [8] used hyperlinks for recommending related articles in Wikipedia. Jiawen et al used hyperlinks as bridge
model the relationship between two documents [7] . [3] identifies
the information gap between analyzing linked entities in a crosslingual knowledge graph. While many approaches exist to finding
missing cross-language links. Jong et al [17] proposes a languageindependent method for discovering missing cross-language links
between English and Japanese. .[19] also leverages cross-lingual
analysis to study topical biases across various language editionsof Wikipedia. However, hyperlinks and other relationships within
Wikipedia represent an exceptional resource that has yet to be fullycomprehended[20]. When it comes to language biases and disparities among different versions of Wikipedia, there are two maincategories of studies: the editing behaviors of editors and readers’behaviors[9]. [2] explores navigation patterns by using hyperlinks
to understand readers and address structural biases and knowledge
gaps. Another research project delivers personalized recommendations to editors by acquiring expertise in effectively representing
Wikipedia articles and offering relevant content suggestions[18].
Recommendation systems aimed at reducing the information
gap within Wikipedia have been implemented using numerous
methods, collaborative filtering is a common approach used [22],[6], [14].
In this work, we present the disparity of different languages byidentifying the distribution of hyperlinks and types. Then, using
these types for collaborative filtering to suggest useful topics toeditors. As far as we know, our work is the first study that focuses on
Wikidata-based types of hyperlinks in multiple language editionsof Wikipedia. We aim to emphasize the impact of multilingualismon the recommendation outcomes.

Language#Wikipedia pages

en
ja
vi

6,438,267
1,297,653
1,268,004

PROBLEM STATEMENT

Each Wikipedia article in each language has a distinct title (e.g.,“Tokyo”). Its content contains hyperlinks (or intra-language links)
directing readers to other correlated articles within the same language (e.g., “Japan”,“Edo”). Although hyperlinks can connect resources across languages, websites, and various media, we only
focus on links within the same language version of a resource. We
also consistently refer to them using one unique term, “hyperlink”
in this paper. Additionally, we introduce the concept of hyperlink
types. These types are extracted based on the relation in Wikidata,named “P31: instance of”. For example, the article “Tokyo” in
the English version has a hyperlink to the article “Japan”. Wikidata
Qid of “Japan” item is 𝑄17 and it has “instance of” values suchas sovereign state and country. These values serve as types
for the hyperlink “Japan”. This “P31: instance of” relation isused in knowledge graphs related to Wikidata to assign one orseveral type(s) to an entity. In this research, we use it to retrieve
Wikidata-based type of hyperlinks. We, as pioneers, consider thistype as distinct concept, deploying it to evaluate language-based
disparities to gain insight into the behaviors and preferences of
local editors. Consequently, we explore its role in recommendation
solution for supporting editors and bridging language gaps.
To do this, we leverage the cross-language link of Wikipedia
articles in the three languages mentioned. These articles in different
languages on the same topic are linked through cross-language
links (or interlanguage links). For example, the article “Tokyo” in
the English edition has a cross-language link to the article “東京都” in Japanese and “Tokyo” in Vietnamese. Thanks to Wikidata,
these three articles share the same unique identifier in Wikidata(𝑄1490). As a result, this feature provides a straightforward way
to retrieve information about an item in various language editions.

4 STATISTICAL ANALYSIS
4.1 Data Extraction
Data collection scope. As previously mentioned, our researchis conducted on a case study involving the English (en), Japanese(ja), and Vietnamese (vi) languages. Table 1 presents the number ofarticles for these languages, sourced from data dumps of January
20, 2022, published by the Wikimedia Foundation. Based on thisdata, we only retrieved the articles that have cross-language links
in all three languages.
Figure 2 provides an overview of creating the data in our study,
including Processing and Data Extraction. The process begins by
retrieving articles from a Wikipedia dump, followed by their processing using two essential tools: the Dump Parser[11] and Data
Indexing[13]. To elaborate, this process is responsible for handling specific files from Wikipedia dumps. These dumps encompass

Exploring Cross-Language Differences in Wikidata-based Hyperlink Types for Enhanced Editorial Support on Wikipedia

copies of all pages from three Wikipedia wikis and redirect files,
all of which are accessible at https:// dumps.wikimedia.org. For
each article, critical details such as the title and ID information are
meticulously extracted. After retrieving and parsing, we extract
the relevant hyperlinks and hyperlink types based on Wikidata-lite[16]. As a result of this step, we obtain a collection of JSON files,
each housing articles in three different languages, along with theircorresponding hyperlinks and hyperlink types. Consequently, we
found 130,000 shared Wikidata identifiers between the three languages. That is, we obtained 130,000 articles from each language toserve as a primary data source for our research.

IJCKG ’23, December 08–09, 2023, Tokyo, Japan

Table 2: Cross-Linguistic Analysis of Hyperlinks and Hyperlink Types#Hyperlinks#Hyperlink Types

en

ja

vi

1,827,775
35,004

739,784
21,690266,596
12,376and city in English edition. Conversely, in Japanese articles, hyperlinks concerning various facets of Japan, including cities, towns,
municipalities, and railway stations, along with subjects like film,
earthquake, and manga series, capture considerable attention. Meanwhile, within the Vietnamese context, local interest predominantly
centers around hyperlinks linked to essential medicine, sovereignstate, country, and calendar year. These observations underscore
the rich diversity of topics and interests within each language’s
community and provide valuable insights into content preferences
and tendencies of users in different linguistic contexts.

Figure 2: Pipeline for extracting hyperlinks and types inthree languages

4.2

Statistical Analysis

In this section, we analyze the collected data to extract valuable
insights. With the specified condition, there are 130,000 articles
extracted in each language. We retrieved information from thearticle, such as Wikidata identifier, title, hyperlinks, hyperlink types,
and their occurrences.
Table 2 presents a comparative analysis of the number of hyperlinks and hyperlink types across three languages on the same
number of articles for each language. English stands out with asignificant count of 1,827,775 hyperlinks, nearly 2.5 times that ofJapanese and almost seven times that of Vietnamese. Correspondingly, English also demonstrates a notably higher count of hyperlink types at 35,004, whereas Japanese and Vietnamese exhibit
comparatively lower counts of 21,690 and 12,376 hyperlink types,
respectively. This distribution is consistent with the prominenceof English, which holds the highest number of articles. Simultaneously, it also distinctly reflects the differences in content and the
topics covered within the articles.
Additionally, it’s important to note that various hyperlink types
exhibit distinct frequencies in articles, and these trends can varysignificantly across different languages. By analyzing the occurrences of these hyperlink types in our dataset, we identify uniquepatterns for each language. Specifically, we calculate the occurrence
rate of each hyperlink type in the articles and filter out types withmore than 50%.
In Figure 3, we provide a visual representation of these trends,
highlighting the diverse array of subjects within each linguistic
community. For instance, we observe a notable preference for hyperlinks related to topics: film, automobile model, album, single,

Figure 3: Local interest of hyperlink types in different languages

5

HYPERLINK TYPE RECOMMENDATIONIn this study, we explore hyperlink types and their occurrences as adataset for recommendation solutions to support editors with additional information. These types assist editors in taking preliminary
topics to adjust content according to the recommendations.
We construct a user-item matrix, treating articles as users and hyperlink types as items. The occurrences of hyperlink types become
the ratings within this matrix. This constructed matrix forms thebasis for predicting missing items using collaborative filtering (CF).
This approach constructs a model using the historical behavior ofusers. Although the occurrences of the hyperlink types in the article
are based on real-world events, it differs from explicit ratings thatare typically used to express preferences. However, these numericalvalues are definitely useful as they provide confidence in a specificobservation. Section 4 illustrates the differences in the occurrencesof hyperlink types across languages, leading us to believe that thesevalues may indicate the preference in local editor communities. Our
aim is to predict occurrence (rating 𝑟𝑢𝑖 ) of hyperlink type (item - 𝑖)for an article (user - 𝑢. To do this, we employ CF methods such as
matrix factorization and neighborhood-based methods.

IJCKG ’23, December 08–09, 2023, Tokyo, Japan

Nhu Nguyen et al.

Neighborhoods in CF can be categorized into two kinds: userbased approach and item-based approach. Over time, the latterbecome more popular. In item-based approach, a rating is predictedby leveraging known ratings provided by the same user for comparable items. Thus, a similarity measure between items is calculated.
We employ the Mean Squared Difference (MSD) to identify the 𝑘items rated by user 𝑢 that exhibit the highest similarity to item 𝑖.
The predicted rating of 𝑟𝑢𝑖 is a weighted average of the ratings forneighboring items:Í𝑗 ∈𝑁 (𝑢 ) sim(𝑖, 𝑗)𝑟𝑢𝑖𝑟ˆ𝑢𝑖 = ÍLanguage

en

ja

vi

Number of Ratings (Occurrences)
Number of Users (Articles)
Number of Items (Hyperlink types)
Average number of Ratings
per User
Average number of Ratings
per Item

210,384
1,000
16,019155,004
1,000
11,116

111,807
1,000
7,197

210.384

155.004111.807

16.01911.116

7.197(1)
𝑗 ∈𝑁 (𝑢 ) sim(𝑖, 𝑗)

Whereas, 𝑟ˆ𝑢𝑖 is the predicted rating for user u on item i. 𝑁 (𝑢)presents the set of items that user 𝑢 has rated. sim(𝑖, 𝑗) is the similarity between items 𝑖 and 𝑗 based on the MSD similarity measure.
In the matrix factorization approach, Singular Value Decomposition (SVD) is one of the most common and successful techniques incollaborative filtering. It captures latent patterns within user-item
interactions. In a recommendation system, the matrix representing
user-item interactions is often sparse because not all users interact
with all items. SVD is applied to decompose this matrix into threematrices: 𝑈 (user matrix),Σ (diagonal singular value matrix), and𝑉 ⊺ (item matrix transpose). Singular value decomposition assumes
a matrix 𝑀 (for example, a matrix 𝑚𝑥𝑛) is decomposed as:
M = 𝑈 Σ𝑉 ⊺(2)

SVD reduces the dimensions of the utility matrix A by extracting its latent factors, thereby mapping each user and item into an𝑟 −dimensional latent space. This mapping provides a clear representation of the relationships between users and items. That means
the dot product of these two vectors gives you the predicted rating
for the user-item pairs.
Data sparsity is a common challenge in recommendation systems. To overcome this, we selectively extracted data with crosslanguage links, which will be presented in the following Section 6.
Through collaborative filtering models, we aim to uncover hidden
connections between articles and hyperlink types so that missing
hyperlink types can be recommended.

6

Table 3: Language Interaction Statistics

articles across three languages and encompasses more than 18,000
hyperlink types, resulting in a total of over 533,000 interactions
between the acquired articles and hyperlink types.
Because nearly 90% of the data falls within the rating range of 1
to 10. Furthermore, hyperlink types that have the high occurrences(ratings) are often types such as taxon (𝑄16521) and Wikimedia list
article (𝑄13406463). They are abstract types or unrelated to topics.
Therefore, for the convenience of evaluating CF techniques, weremove them and only assess the dataset with ratings ranging from 1
to 10. As mentioned above, the ratings in this context are essentiallybased on real events. In this experiment, our goal is to suggest
whether types should appear, high occurrence is not necessarily themost important factor. Figure 4 shows the distributions of ratingsby groups. Rating of 3 has the highest rating count among ratings
1-10. Table 3 shows the statistic of the dataset used for CF methods.

EXPERIMENT

We conducted experiments on collected data in three languages:
English, Japanese, and Vietnamese.

6.1

Experimental Setting

Input Data.
From the data obtained in Section 4.1, we have 130,000 articles
in each language that satisfied the criterion of having cross-lingual
connections. This maybe improve the recognition rate of patterns
when more languages are integrated. Then, we filter the articles
with the number of hyperlink type exceeding 200, which served
as a sample data for our models. This ensures that the chosen
articles have high-quality content in all three languages, serving
the dual purpose of maintaining experiment fairness and observing
the results of language integration. After filtering, we retained
approximately 1,000 articles in each language that satisfied thespecified conditions. Consequently, our input data comprises 3,000

Figure 4: Distribution of Articles by Rating Group.

We use cross-validation technique to assess the performance ofpredictive models. The most common form of cross-validation is
k-fold cross-validation, where the dataset is divided into k subsets
or “folds”. The model is trained on k-1 of these folds and tested on
the remaining fold. The performance metrics used are the official
metrics of collaborative filtering: MAE and RMSE.
Mean Absolute Error (MAE) is a metric for calculating the average of all absolute differences between the algorithm’s predictions

Exploring Cross-Language Differences in Wikidata-based Hyperlink Types for Enhanced Editorial Support on Wikipedia

IJCKG ’23, December 08–09, 2023, Tokyo, Japanand the actual ratings. A lower MAE indicates higher accuracy.
𝑛
1 ∑︁
MAE =
|𝑦𝑖 − 𝑦ˆ𝑖 |
(3)
𝑛 𝑖=1
Where, 𝑦𝑖 is the actual rating, 𝑦ˆ𝑖 is the predicted rating and 𝑛 is
the amount of ratings.
Root Mean Squared Error (RMSE) computes the mean valueof all squared differences between the true and predicted ratings.
Subsequently, it calculates the square root of the result. RMSE is
most useful when large errors are particularly unwanted.
v
t
RMSE =𝑛1 ∑︁(𝑦𝑖 − 𝑦ˆ𝑖 ) 2
𝑛 𝑖=1

(4)

We performed 5-fold cross-validation by dividing the dataset into
five equal parts. In each fold, 80% of the data were used for training,
and the remaining 20% were used for testing. In addition, we also
compare performance of CF approaches with baseline methods thatpredict a random rating based on the distribution of the trainingset.
The data used in this paper is publicly accessible, and our code
has been made available to facilitate the replication of our experiments. 1

6.2

Figure 5: Comparison of the lowest MAE and RMSE values
among different models on datasets.

Experimental Results

We first report the performance of collaborative filtering algorithms
on all datasets, and then investigate how multilingual datasets
improve the recommendation performance.
Specifically, we conducted experiments on three different language configurations as follows:• Monolingual dataset• Bilingual dataset• Trilingual dataset
Table 4 shows MAE and RMSE scores of models on different
datasets. The lower these values, the higher the performance ofthe models. SVD represents for matrix factorization approach. KNearest Neighbor (KNN) approach have KNNBaseline, KNNWithMeans, KNNWithZscore and KNNBasic models. NormalPredictor
model represents Baseline method. Clearly, CF approaches havebetter performance than Baseline method. The reason is that NormalPredictor model only uses maximum likelihood estimation forprediction. CF approaches estimate another characters of items or
relationship of users and items. Considering CF approaches, SVD
and KNNBaseline model have competitive results in monolingual
dataset. But, KNN models are better than SVD in multiple language.
In detail, the lowest MAE and RMSE of KNNBasic 0.956 and 1.503,
respectively, are attained on the trilingual configuration.
Figure 5 displays the lowest MAE and RMSE values achievedby the KNN models. It is evident that these values are inversely
proportional to the number of aggregated languages, indicating a
positive impact of multiple languages on the experimental results.
After hyperparameter tuning, we generate recommendations on
the datasets. Figure 6 illustrates the relative increase or decreasein the number of recommendations when multiple languages areused. For instance, the number of recommendations for Japanese
1 https://github.com/nhunthp/Hlink_RS#readme

Figure 6: Multilingual recommendation impact chartarticles increases more 50% when combined with other languages(primarily due to English). Vietnamese articles experience a moresignificant increase, with recommendations reaching around 150%
when combined with all three languages. These results are notsurprising, as initial statistics indicated that Vietnamese articles
had the lowest number of hyperlink types. However, they suggestthat low-source languages can benefit in recommendation systems
when combined with stronger languages. This enrichment of recommendation content offers more choices and supplements missing
content, ultimately narrowing the gap between languages within
Wikipedia.

7

CONCLUSION

We explore hyperlink types, revealing patterns across various languages. Furthermore, we introduced a pipeline for retrieving thesetypes and performed a statistical analysis of their occurrences acrossthree languages. The statistical findings reveal notable distinctions
within the collected data, highlighting the potential for utilizing thisinformation in recommendations to support editors. We evaluate
across multiple experiments involving different language combinations. The experimental results show that multilingual aggregating

IJCKG ’23, December 08–09, 2023, Tokyo, Japan

Nhu Nguyen et al.

Table 4: MAE, RMSE values of models on the datasets

MAE

ja
RMSE

MAE

vi
RMSE

MAE

en
RMSE

SVD
1.047
KNNBaseline
1.041
KNNWithMeans 1.198
KNNWithZScore 1.244
KNNBasic
1.065
NormalPredictor 2.47

1.553
1.539
1.683
1.733
1.704
3.076

1.023
1.009
1.161
1.210
1.026
2.440

1.513
1.506
1.636
1.688
1.653
2.450

1.035
1.018
1.183
1.229
1.035
2.451

1.557
1.519
1.672
1.721
1.671
3.060

Model

allows us to overcome the limitations of data sparsity in individual
languages.
By leveraging the characteristics of cross-language links fromvarious editions of Wikipedia, we enrich the dataset. Consequently,
the recommendation system becomes more robust in collaborative
filtering models. These results hold promise for supporting lowresource languages and bridging the gap in Wikipedia.
In the future, we are considering exploring alternative approaches
for the recommendation system, including examining graph-based
data organization or incorporating additional text attributes.

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