Automatic Multilingual Hypernym–Hyponym
Relation Extraction Using a Link Prediction Model
Kohei Iwakuma
Graduate School of Science and Engineering
Chuo University
Tokyo, Japana19.bxw6@g.chuo-u.ac.jp

Yao Gong
Graduate School of Science and Engineering
Chuo University
Tokyo, Japan

Hidetsugu Nanba
Faculty of Science and Engineering
Chuo University
Tokyo, Japannanba@kc.chuo-u.ac.jp

Satoshi Fukuda
Faculty of Science and Engineering
Chuo University
Tokyo, Japan
fukuda.satoshi.3238@kc.chuo-u.ac.jp
Abstract—In natural language processing, hypernym–hyponym
relations are the core of the body of knowledge and are useful formany downstream tasks, for example, technical trend analysis andpatent examination. However, it is very costly to manually
maintain these relations between terms. In this paper, we extract
hypernym–hyponym relations from patent text data written in
Japanese, English, and Chinese, and automatically construct a
multilingual thesaurus. The proposed method consists of thefollowing two steps. First, we use a generative adversarial network(GAN) to identify the terms in a hypernym–hyponym relation.
Then, ConvE and GraphSAGE are combined to predict links on
the graph of hypernym–hyponym relations constructed in theprevious step, and to predict missing edges that should be in a
hypernym–hyponym relation. In experiments conducted todemonstrate the effectiveness of the proposed method, it was
found that our method outperformed previous methods in boththe identification of hypernym–hyponym relations using GAN andlink prediction using a combination of ConvE and GraphSAGE.
We constructed a classifier that can discriminate between
hypernym–hyponym relations using a trained Discriminator with
GAN. In our experiments, we achieved a recall rate of 0.936 in
English. And we propose a new method that can automaticallycomplement missing hypernym–hyponym relations. The proposedmodel achieved a score of 97.3 on the H@10 evaluation index withrespect to the prediction of Chinese hypernym–hyponym relations.
Keywords—hypernym–hyponym
relation,
multilingual,
generative adversarial network (GAN), link prediction, information
extraction, patent

I. INTRODUCTION
This paper proposes a method for automatically constructing
a multilingual thesaurus by extracting hypernym–hyponym
relations from patent text data written in Japanese, English, andChinese. A thesaurus including hypernym–hyponym relationsof terms is useful in various situations, such as text mining andinformation retrieval. However, because it is very costly tomanually maintain such a thesaurus, an automatic constructionof such a thesaurus is required.
Methods for acquiring hypernym–hyponym relations using
linguistic patterns have been explored. Hearst [1] extracted
hypernym–hyponym relations from English newspaper articles
using lexical syntactic patterns. As a first step in the constructionof a multilingual thesaurus, we also use multilingual linguistic

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patterns such as “A や B などの C” (C such as A and B) in
Japanese,“D such as E, F” in English, and “G 包括 H 和 I” (Gsuch as H and I) in Chinese. From these patterns, we obtain termpairs that are candidates for hypernym–hyponym relations.
We construct a multilingual thesaurus for candidate
hypernym–hyponym term pairs extracted using the patterns in
two steps: first, we use a generative adversarial network (GAN)[2] to build a discriminator that identifies terms in a hypernym–
hyponym relation; then, we apply link reduction techniques to
the graph of hypernym–hyponym relations constructed in thefirst step to predict missing edges that should normally be in thehypernym–hyponym relation.
Link prediction tasks predict missing relations between
nodes and are key to automatically understanding data in large
graph structures. Although previous work on link prediction has
focused on large knowledge graphs [3, 4], there has been little
work on predicting links of hypernym–hyponym relations. In
this study, we propose a method to predict missing hypernym–
hyponym relations. ConvE [5] is a convolutional neural network(CNN) model that has proven to be very useful in a variety ofprediction tasks. GraphSAGE [6] is one of the methods forcomputing embeddings related to graph neural networks(GNNs). It also provides an effective approach for solving link
prediction tasks. We combine ConvE and GraphSAGE to
predict missing hypernym–hyponym relations. In the link
prediction of hypernym–hyponym relations, we transform eachword into a node in a graph, and predict the existence of linkedges between nodes in the graph (Fig. 1).
We aim to increase consistency and accuracy acrosslanguages by using the multilingual thesaurus constructed in thisway. We also expect to discover new relations and knowledge
from technical documents and scientific literature.

設備设备(equipment)

equipment(equipment)
equipment such as
electronic equipment

電子機器(electronicequipment)

electronicequipment

electronic equipment
such as monitor

monitor设备包括电子设备(equipment such as
electronic equipment)

設備などの電⼦機器(equipment such as
electronic equipment)

電⼦機器などのモニター(electronic equipment
such as monitor)

電⼦機器などの携帯電話(electronic equipment
such as cell phone)モニター(monitor)
electronic equipment
such as cell phone电子设备(electronicequipment)

携帯電話(cell phone)电子设备包括显示器(electronic equipment
such as monitor)显示器(monitor)电子设备包括手机(electronic equipment
such as cell phone)

手机(cell phone)

cell
phone

Fig. 1: Graph of multilingual hypernym–hyponym relations

II. RELATED WORK
A. Extraction of hypernym–hyponym relations
A typical method for extracting hypernym–hyponym
relations is to use linguistic expressions. Nanba et al. [7]automatically constructed a Japanese–English thesaurus by
extracting candidate terms of hypernym–hyponym relations
from patents written in Japanese and English, and then byaligning the Japanese and English terms using statisticalmachine translation technology. Whereas their method excluded
all terms that cannot be matched by statistical translation, in thisstudy, we use GAN to keep correct hypernym–hyponym
relations in the multilingual thesaurus, even though they cannot
be matched by translation. Furthermore, we expect to constructa more comprehensive multilingual thesaurus by using a link
prediction model to detect hypernym–hyponym relations thathave not been extracted by the linguistic pattern method.

relations. The difference between the present study and previous
work is that the missing relations of each language areautomatically complemented using a link prediction model
based on a single graph of multilingual hypernym–hyponym
relations extracted using linguistic patterns. For example,
assume that “small dog – dog” and “small dog – animal” exist
as hypernym–hyponym relations.

Other studies of multilingual hypernym–hyponym relation
detection have focused primarily on detecting hypernym–
hyponym relations in high-resource languages such as English.
Yu et al. [8] addressed the task of detecting low-resource
hypernym–hyponym words. To solve the problem of lack ofresources, we propose a method to apply meta-learning. Yu et
al. compared three collaborative training paradigm methods:“cross-language training,” “multilingual training,” and “metalearning.” Experimental results showed that the meta-learning
method was superior to the other methods. Meta-learning
significantly improves the performance of languages with very
few resources by preventing overfitting of small datasets.

B. Construction of a hypernym–hyponym relation classifier
using GAN
Traditional research using GANs is aimed at data
augmentation, thereby generating more real images andsentences. Kober et al. [10] proposed a method to augment datausing GANs to detect hypernym–hyponym relations. Here, we
examine data augmentation methods that utilize existing training
data to generate new training examples. By combining the
linguistic principles of transitivity of hypernym–hyponym
relations and cross-modifier-noun constructions, hypernym–
hyponym relations can be assumed. If “animal” is a hypernym
relation of “dog” and “dog” is a hypernym relation of “smalldog,” “animal” is added to the hypernym relation of “small dog.”
To find appropriate candidates for words, nouns must be
combined with modifiers. Experimental results show that the
proposed data augmentation and dataset extension methods canbe used to significantly improve classifier performance. Using
discriminator as a classifier, Chavdarova and Fleuret [11]proposed a semi-supervised GAN called SGAN to perform
multiclass classification tasks. The present study differs in thatthe variance representation of the hypernym–hyponym words is
input to the GAN, and the learned discriminator is used as aclassifier to identify the hypernym–hyponym relations.

Upadhyay et al. [9] proposed BISPARSE-DEP, an
unsupervised approach for cross-language hypernym–hyponym
relation detection. Bilingual embedding and an unsupervisedentailment scorer were used to detect hypernym–hyponym

C. Link prediction
In recent years, deep learning has excelled in a number ofnatural language processing tasks. The reason for this is theevolution of models and algorithms related to neural networks,and there have been many studies on neural link prediction
models. Bordes et al. [12] proposed TransE, a translatingembeddings model that uses deep learning to embed knowledge
graphs into a low-dimensional space. Yang et al. [13] proposed
the algorithm DistMult (bilinear diagonal), which restricts therelation matrix to a diagonal matrix. DistMult shows that Stateof-the-ART (SOTA) is achieved using a simple bilinear
diagonal. Trouillon et al. [14] proposed the ComplEx (extensionof the complex space) model using complex embeddings tohandle a variety of binary relations, including symmetric andantisymmetric relations.
GNNs have been extensively studied since 2016. The main
downstream tasks handled by GNNs include link prediction,
graph classification, and node classification. For application tograph-structured data, Kipf and Welling [15] proposed a graphconvolutional network (GCN) model that can extend CNN input
to graph structures. They also proposed an extensible supervised
learning approach based on CNNs that can operate directly on
the graph. However, GCNs ignore the relations between nodes
without considering their relation to each other. To address the
problems of GCN, Schlichtkrull et al. [16] introduced therelational graph convolutional network (R-GCN) model toaddress the effects of different edge relations in the graphstructure on nodes. Jiang et al. [17] proposed a link prediction
model, ConvR, which builds a convolution filter from therelational representation and applies this filter to the entire entity
representation to generate convolution features. In our work, we
combine a link prediction model with a graph embedding
method to address multilingual autocompletion tasks.
III. CONSTRUCTION OF A HYPERNYM–HYPONYM RELATION
CLASSIFIER USING GANSIn this study, we propose a method for constructing aclassifier for filtering the extracted candidate hypernym–
hyponym relations. We create a dataset of WordNet-mapped
hypernym–hyponym relations extracted from patents and build
a classifier that determines the hypernym–hyponym relations
using GANs. We then use a classifier to remove inappropriate
nodes from the graph of hypernym–hyponym relations. Fig. 2
summarizes the architecture of the construction of hypernymlevel classifiers with GANs. GANs consist of two elements,
namely a generator and a discriminator. The role of the generator
is to generate spurious data from a random distribution; thediscriminator identifies whether data is real or fake. In this study,
the discriminator is populated with word embeddings generated
from DeepWalk, which is a graph embedding method proposedby Perozzi et al. [18]. We convert the hypernym–hyponym terms
to nodes in the graph and vectorize them in DeepWalk. The 200
dimensions of each word are combined to generate embeddingsof the hypernym and hyponym pairs of 400 dimensions. We thengenerate fake data from the Gaussian noise Z given by thegenerator and train the generator and the discriminator tocompete with each other. Finally, we use the learned
discriminator as a classifier to classify if it is a hypernym–
hyponym relation. GAN aims to optimize the value function(value function) using the following equation:
𝑚𝑖𝑛 𝑚𝑎𝑥 𝑉(𝐷, 𝐺) = 𝔼𝒙∼%data (𝒙)[log 𝐷(𝒙)]!

"+ 𝔼𝒛∼%𝒛(𝒛) 6log 71 − 𝐷:𝐺(𝒛)<=>where 𝐺 is a mapping from 𝑧 to 𝒙, and 𝐷 is a sigmoid functionthat inputs the teacher data 𝒙 to Discriminator and returns theprobability 𝑝. 𝐺(𝑧) represents the fake data generated from the
input noise 𝑧 to the Generator, and 𝔼 represents the expectedvalue.
We train a multilingual dataset mapped to a GAN, then input
candidate hypernym–hyponym relation pairs into a trainedclassifier for classification.
Z(noise)Generator

Test data

Generated
data

Discriminator

Real
data

TrainedDiscriminator(classifier)Classification
resultFig. 2: Construction of hypernym-level classifiers with GANs
IV. AUTOCOMPLETION OF MULTILINGUAL HYPERNYM–
HYPONYM RELATIONSIn this study, we propose a multilingual hypernym–
hyponym relation autocompletion method that combines ConvE
and GraphSAGE. Although the CNN model ConvE for the link
prediction task can be used for monolingual link prediction tasks,
it is not easy to use for multilingual datasets. To solve thisproblem, in this study, we introduce GraphSAGE, which is oneof the graph embedding methods in GNNs. GraphSAGE in agraph aggregates embeddings of neighboring nodes andcalculates and updates embeddings of its own nodes. ConvE
uses random initialization values as embedding of the node. We
use DeepWalk to process distributed representations of words.
The embedding of each node is obtained by DeepWalk. The
acquired embedding is used as the initialization feature of thenode. Each node is a 200-dimensional vector of features. We
validate the proposed method using the WN18 hypernym–
hyponym relation dataset.
Fig. 3 shows the construction of a multilingual hypernym–
hyponym complementation system based on a link prediction
model. GraphSAGE needs to specify the range by the sample
size S of the neighborhood and the depth K from the node. We
set K=1 and S=5. We convert the input triple (s, r, o) into theform (hypernym, hypernym–hyponym relation, hyponym) and
use the hypernym–hyponym relations as nodes in the graph.
Additionally, we collect information from the source node’s
neighbors by GraphSAGE. Finally, we use the ConvE model topredict the missing relation between source node 1 and target
node 7. When predicting the missing relation, the system not
only predicts the relation from the source node to the target node,but also considers the target node’s neighbors. If there is acorresponding relation between a source node 1 neighbor node
and a source node 7 neighbor node, it is likely to be determined
to be a superior-hyponym relation. In the experiments describedin the next section, we remove the hypernym–hyponym relationfrom each language and predict the missing relation from theother languages.

B. Construction of the hypernym–hyponym relational
discriminator with GAN
Experimental Data

Sourcenode

Missing
relationTargetnode

Fig. 3: Prediction of multilingual hypernym–hyponym links
using the proposed method

Experimental Methods
GAN is implemented by an all-associative neural network
using PyTorch. We use Precision, Recall, and F-measure as
evaluation indices. The threshold is set at 0.5.

V. EXPERIMENT
A. Extraction of hypernym–hyponym relations
Experimental Data

Comparative Method

The data for Japanese patents was obtained from the Japan
Patent Office (https://www.publication.jpo.go.jp/), Chinese
patents from J-Platpat (https://www.j-platpat.inpit.go.jp/c1000),and US patents from USPTO Bulk Data Storage(https://bulkdata.uspto.gov/). The candidate hypernym–
hyponym relations with a frequency of 3 or more were extracted
from 1,148,250,436 sentences included in Chinese patent
specifications, 1,244,905,476 sentences included in Japanese
patent specifications, and 1,560,706,140 sentences included in
English patent specifications. Table I shows the details of thedata.
TABLE I.

We use the data of 3,794 multilingual hypernym and lowerlevel relations extracted from Japanese, English, and Chinese
patents that have been mapped to the Multilingual WordNet. We
split the training and evaluation data at a ratio of 70% to 30%.
The data is divided into 2,656 training data and 1,138 evaluation
data for each language.

RESULTS OF HYPERNYM–HYPONYM RELATIONS EXTRACTEDFROM JAPANESE, ENGLISH, AND CHINESE PATENTS

Patent data

Year of release

Hypernym–hyponym
terminology

Japanese
EnglishChinese

1993–2018
1993–20182018, 2020, 2021

2,908,574
4,506,946
1,080,357

Experimental Methods
The target terms are only nouns (NOUN) and proper nouns(PROPN), using the part-of-speech analysis of the Python
library spaCy. We used the linguistic patterns of each language
to extract candidate hypernym–hyponym relations from thetarget sentences.
Experimental Results
The results of the experiment are shown in Table I. We
extracted a total of 8,495,877 top subpatents from Japanese, US,
and Chinese patents using the standardized expressions in eachlanguage.
Discussion
A common cause of terms being incorrectly extracted ineach language is the inclusion of extra words in the extracted
hypernym terms. For example, for the sentence “various
additives such as antioxidants,” extra words such as “various”
were extracted. For these issues, we consider it necessary to use
multiple parameters and extract only matched term pairs.

We built our binary classifier using only the discriminator,
without using the GAN framework. We also constructed abinary classifier based on logistic regression.
Experimental Results
The classification results of the proposed method and theresults classified by the comparison method are shown in Table
II. Experimental results confirmed the effectiveness of the
proposed method, producing higher accuracy, repeatability, andF-measure than the comparative method.
Fig. 4 shows the loss functions of the generator and thediscriminator of the GAN when learned in Japanese. There is asimilar trend in loss when learning in English and Chinese.
Although the process of training GANs proceeds withoscillating loss, we confirm that the learning process is unstable.
It can be seen that the loss of the discriminator is suppressed
while the loss of the generator is increased.
Discussion
The reason for the lower accuracy, recall, and F-values
obtained for Japanese than for English and Chinese can be
attributed to the larger number of terms included in theconstructed Japanese term list than for the other languages.
TABLE II.
IDENTIFICATION RESULTS OF HYPERNYM–HYPONYM
RELATIONS FOR EACH LANGUAGE IN THE MULTILINGUAL DATASET
MethodLogistic
Regression
CNN(Discriminator)
GAN(Generator +Discriminator)

Evaluation
Measures
Precision
RecallF-measure
Precision
RecallF-measure
Precision
RecallF-measureJapanese

ChineseEnglish

0.841
0.524
0.645
0.841
0.469
0.602
0.866
0.875
0.871

0.864
0.644
0.738
0.840
0.529
0.649
0.872
0.917
0.894

0.873
0.645
0.742
0.861
0.584
0.696
0.875
0.936
0.905

Experimental Results
Table III shows the results of the prediction of the hypernym–
hyponym relations using ConvE+GraphSAGE—the method
proposed in this study—and the results of the prediction by thecomparative method. The experimental results show that the
proposed method is superior in the H@1, H@10, and MRR
evaluation indices, confirming the effectiveness of the proposed
method. The results of the autocompletion experiment are
shown in Table IV, and show that English outperformed theother languages in the H@1 and MRR metrics. Chinese
outperformed the other languages in the H@3 and H@10
evaluation indicators.
Discussion

Fig. 4: Loss functions when learned in Japanese.
C. Construction of a multilingual autocompletion system
Experimental Data
In the comparison experiment shown in Table III, we used
74,442 data from the WN18 dataset (a total of 18 relations) with
only hypernym–hyponym relations.
In the experiment shown in Table IV, we used 6,231 Japanese,
English, and Chinese patent terms, which are also present in
WordNet, in terms of hypernym–hyponym relations. There arethree types of relations between terms (is a hypernym, is not a
hypernym, alignment). We split the dataset per language into
46,109 data for training, 9,970 for validation, and 9,242 forevaluation.

The reason for the lower MRRs obtained in Japanese than inEnglish and Chinese is likely the higher number of disconnected
nodes in the graph. When embedding nodes using GraphSAGE
were calculated, the number of nodes in the neighborhood wasinsufficient. It is expected that the MRR can be improved in the
future by reducing the number of disconnected nodes.
TABLE III.
H@K AND MRR RESULTS OF PREDICTING HYPERNYM–
HYPONYM RELATIONS IN THE WN18 HYPERNYM–HYPONYM RELATIONDATASET
Method

H@1

ConvR
ConvE
ConvE+GraphSAGE

|+|

1
1
MRR =
S
|𝑆|
rank )
),1
1
1
1
=
+
+ ⋯+
T
V
|𝑆| rank- rank .
rank |+||+|

1
HITS @𝑛 =S 𝕀( rank ) ⩽ 𝑛)|𝑆|),-

Comparative Method
We use ConvE as proposed by Dettmers et al. [5] and ConvR
as proposed by Jiang et al. [17] as our comparison method.

H@10

MRR

42.9

49.3

55.4

47.5

44.7

81.4

90.1

64.0

52.3

80.1

90.2

67.1

TABLE IV.
H@K AND MRR RESULTS FOR EACH LANGUAGE WHEN
PREDICTING HYPERNYM–HYPONYM RELATIONS USING THE MULTILINGUALDATASET
MethodExperimental Methods
We build the ConvE model in PyTorch Geometric andimplement GraphSAGE. We use Mean Reciprocal Rank (MRR),HITS@1, HITS@3 and HITS@10(H@1, H@3, and H@10) for
our evaluation indices. The following formulas are used to
calculate each rating scale. |𝑆| represents the number of
combinations of triples: two entities and one relationship. rank )represents the predicted link ranking of the i-th triple.

H@3

ConvE +
GraphSAGE

Evaluation
measure
H@1
H@3
H@10
MRR

Japanese

ChineseEnglish

29.4
62.3
92.0
49.4

25.2
71.4
97.3
50.1

35.0
65.5
90.4
53.3

VI. CONCLUSION
In this study, we extracted hypernym–hyponym relations
from Japanese, English, and Chinese patent data using
multilingual definite expressions and mapped them to theMultilingual WordNet to create a multilingual hypernym–
hyponym relation dataset. We also used a trained discriminatorwith GAN to build a classifier that can identify hypernym–
hyponym relations. The constructed classifier achieved a recall
rate of 0.936 in English.
Combining the CNN link prediction model ConvE and the
GNN algorithm GraphSAGE, we propose a new method that
can automatically complement missing hypernym–hyponym
relations. The proposed model achieved a score of 97.3 on the
H@10 evaluation index with respect to the prediction ofChinese hypernym–hyponym relations. In future work, we will
investigate the extent to which the proposed method can be
improved by incorporating embedding using multilingual
models such as Multilingual BERT and XLM-RoBERTa.

ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant
Numbers 22K12154 and 20H04210.
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