DEIM Forum 2019 C5-4

Metadata Similarity Calculation in Cross-Language Record Linkage based on
Cross-lingual Embedding Models
Yuting SONG† Biligsaikhan BATJARGAL‡ and Akira MAEDA†‡†Research Organization of Science and Technology, Ritsumeikan University
1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577 JAPAN‡Kinugasa Research Organization, Ritsumeikan University56-1 Toji-in Kitamachi, Kita-ku, Kyoto, 603-8577 JAPAN†‡ College of Information Science and Engineering, Ritsumeikan University
1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577 JAPANE-mail: †ytsong@gst.ritsumei.ac.jp, ‡biligee@fc.ritsumei.ac.jp, †‡amaeda@is.ritsumei.ac.jp
Abstract In many data mining applications, there is a need to compare or combine information from different data sources.
One of the main processes is to find identical records that refer to the same real-world entity across databases, which is known
as record linkage or record matching. In this paper, we focus on the task of cross-language record linkage, in which records arefrom the databases in different languages. The descriptive metadata of an entity (e.g. the title of a book, the title of an image)summarize the content and distinguish it from other entities. Thus, we propose a method of measuring descriptive metadatasimilarities in different languages for record linkage. Our method utilizes cross-lingual embedding models, in which words andmetadata in different languages can be represented in a shared embedding space. In this work, we learn the shared embedding
space using sentence-level bilingual parallel data. We evaluate the effectiveness of our proposed method on the real-world
databases in Japanese and English.
Keyword Cross-language record linkage, title matching, semantic similarities

1. Introduction
Record linkage [1][2][3] is a task of finding record

digital museums of foreign countries with metadata intheir native languages [5].

pairs that refer to the same entities across multiple data

To find the record pairs that refer to the same entity, thesources, which is an important step in many data mining

record pairs are compared based on their metadata

applications as information from multiple sources needs tosimilarities. The task of cross-language record linkage is

be integrated or combined in order to allow more detailed

challenging since the metadata are in different languages.

data analysis. It can be used to improve data quality and to

In this paper, we focus on cross-language record linkage

reduce costs and efforts in data acquisition [4].

by measuring descriptive metadata similarities, since theWith the World Wide Web becomes widely matured in

descriptive metadata given to an entity summarizes andmore and more countries, the information is being

distinguishes it from other entities. Our method utilizes

produced in a variety of languages. The identical entities

cross-lingual embedding models, in which words andcan exist in multiple data sources in different languages.

metadata in different languages can be represented in a

An example is shown in Figure 1. The identical ukiyo-eshared embedding space.

prints 1 are digitized not only in the Japanese digital

metadata similarities across languages, we learn a linear

museums with the metadata in Japanese, but also in the

mapping between vector spaces of languages to transform

Then, for calculating thethe metadata representations from the vector space of one
1

Ukiyo-e is a type of Japanese traditional woodblock printing,which is known as one of the popular arts of the Edo period(1603-1868).

language to the other.
The reminder of this paper is organized as follows.
Section 2 outlines some related work; Section 3 introducesFigure 1: An example of the same ukiyo-e prints that are exhibited in multiple
databases with metadata in different languages
our proposed method in detail; Section 4 presents ournumerical values. This paper focuses on the descriptive

experimental setup and evaluations. Section 5 concludes

metadata, such as title.

this work and outlines future work.

2.2 Cross-language tasks
Cross-language entity linking [10][11] is related to our

2. Related work

work to some extent, which aims to link the named entities

2.1 Record linkage

in the texts in one language to a knowledge base in another

Record linkage is the task of identifying records that

language. In this task, much contextual information ofrefer to the same entities from several data sources. Over

named entities in texts and content of articles in

the past decade, various research fields have developed

knowledge bases can be employed. However, our work

their own solutions to the problem of record linkage, andfocuses on the record linkage where only the metadata

as a result, this task is named by many different terms. In

values can be utilized, which are usually short texts, andthe database field, record linkage and identity resolution

sometimes in poor quality.

[6] are used to describe the process of identifying the

Cross-language knowledge linking [12][13] is anotherrecords that represent the same entities. When matching

related task. Most methods are proposed using therecords are found, identity resolution merges the identicalstructural information of data, such as inlink and outlink

records,

thein the articles [12], to find the identical articles between

correspondence. In the natural language processing field,

knowledge bases in different languages. BabelNet [13] is a

this problem is known by the name of coreference

large

resolution [7][8], which is to determine two entity

combiningmentions refer to the same entity within document; andapproach aims at linking the records in several databases

entity linking [9], which is to link the entities mentioned

in different languages that refer to the same real-worldin text to the entry in a knowledge base.

entity, not to find identical lexicons or articles.

while

recordlinkage

simplynotes

multilingual

lexical

Wikipedia

andknowledge

base

built

by

WordNet.

However,our

To identify the record pairs that refer to the same entity,

Our work is also related to cross-language ontology

the similarity between two records is calculated bymatching [14][15][16]. With the development of thecomparing their metadata. The metadata of records

Linked Data 2 , ontology matching is attracting the interests

contains different types of data, for example, the personalof some researchers. Cross-language ontology matching isnames, titles, and abstracts are string values, the financial

to find equivalent elements between two semantic data

data such as salaries and expenses are numerical values,
and the date, age and time, which are a special case of2

http://linkeddata.org/

sources. The difference between our goal and theirs is that

formulated in the following equation:

our work focuses on general relational databases.

&

𝑅(𝑀) = ' ∑'*+& 𝑤*(1)

3. Methodology
Our method utilizes cross-lingual embedding models, inwhere n is the number of words in metadata M; 𝑤* is thewhich words and metadata in different languages can bevector embeddings of words that compose the metadata M.
An example in Figure 2 illustrates the process ofrepresented in a shared embedding space.
In this section, first, we introduce two methods for the

metadata representation. First, the vector embeddings ofshared embedding space induction. Then, we explain inwords in the English title “Storm below Mount Fuji” aredetailobtained from the pre-trained English word embeddings.

themetadata

representations

andmetadata

Next, the metadata is represented as a vector by adding thesimilarity calculation of our proposed method.

obtained vector embeddings.

3.1 Cross-lingual embeddings models
Cross-lingual embeddings from bilingual word pairs.
This type of models [17][18][19] focuses on learning the
mappings between independently trained monolingual
embedding spaces using a set of bilingual word pairs.
Cross-lingual
paralleldata.

sentence-aligned

embeddings
This

type

parallelfromofbilingualmodelscorporatosentence

exploitssomeinducethecross-lingual embedding spaces. In our proposed method,
we use the cross-lingual embeddings space that is induced
by sentence-aligned parallel corpora.
3.2 Metadata similarity calculation

We use cross-lingual embeddings to transform themetadata vectors from one language vector space to thevector space of the other language. We learn a linear
mapping between the vector spaces in different languages
using bilingual sentence pairs. Suppose we have a set ofbilingual sentence pairs and their associated vector
representations {𝑥* , 𝑦* }'*+& , where 𝑥* is the vector ofsentence 𝑖 in the source language, and 𝑦* is the vector ofits corresponding sentence in the target language. Our goal
is

to

learn

a

mappingmatrix

𝑊

such

that

𝑊𝑥*

approximates 𝑦* .
At the time of similarity calculation, for any given new

With the induced cross-lingual spaces we can directly

metadata vector x, we transform it into the vector space ofmeasure the semantic similarity of words in two languages,

the other language by computing 𝑧 = 𝑊𝑥. Then, we canbut we still need to define how to represent metadata. To

calculate the similarity between metadata in different

this end, we outline the method that exploits the induced

languages by comparing the transformed vectors withcross-lingual embedding space for metadata similarity

other metadata vectors in the vector space of the othercalculation.

language.

We represent metadata as vectors by adding thecross-lingual embeddings of their constituent words. We

4. Experiments

opt for vector addition as composition since word

In this section, we show the experimental results of ourembedding spaces exhibit linear linguistic regularities

proposed method in the task of finding the identical[20]. Thus, our method for representing metadata can beukiyo-e prints across databases in Japanese and English.

Figure 2: An example of metadata representation

Table 1: Some examples of Japanese ukiyo-e metadata records
作 品 (Title)

シ リ ー ズ 名 (Series name)

作 者 (Artist)

制 作 年 (Date)

神奈川沖浪裏冨嶽三十六景

葛飾北斎

天保 2 年〜4 年

深川万年橋下冨嶽三十六景

葛飾北斎

天保 2 年〜4 年

女官洋服裁縫之図

橋本周延

明 治 20 年 8 月

日本橋 朝之景

歌川広重

天保中期

木場の雪

歌川国貞

文化 8 年〜天保末

葛飾北斎

天保 3 年

隅田

雪月花

Table 2: Some examples of English ukiyo-e metadata records
Title

Series name

ArtistDate

Under the Wave off Kanagawa

Thirty-six Views of Mount Fuji

Katsushika Hokusaica. 1830–32

Morning View of Nihonbashi

Utagawa Hiroshige I

ca. 1833–34

Court Ladies Sewing Western
Clothing

Hashimoto Chikanobu

August23rd, 1887

Katsushika Hokusaica. 1833

Utsu Hill at Okabe

Utagawa Hiroshige I

1834

Evening Glow at Koganei
BorderRyūryūkyo Shinsai

1797–1858

Snow on the Sumida River

Snow, Moon, and Flowers

identified by ukiyo-e.org 5 referred to the same ukiyo-e

4.1 Experimental dataset
We collected 203 Japanese ukiyo-e metadata records
from Edo-Tokyo Museum 3 and 3,398 English ukiyo-e
4

metadata records from the Metropolitan Museum of Art .

print.
4.2 Experimental setup
4.2.1 Word embeddings of different languages

The metadata that was used in the experiments includes

In the experiments, both English word embeddings andartist names, titles, series names and date of the ukiyo-e

Japanese word embeddings are trained using Word2vec

prints. In our dataset, every record has metadata of artist

toolkit. The skip-gram model of Word2vec is employed tonames and titles. Some metadata records have serieslearn word embeddings. To train the skip-gram model, thenames and date. Some examples of Japanese and English

hyper-parameters recommended in [21] are used, whichukiyo-e metadata records are shown in Table 1 and Table 2,

are shown in Table 3.

respectively.

Table 3: The parameters of Word2vec for training word

In this dataset, each Japanese ukiyo-e metadata record

embeddings

has at least one corresponding English ukiyo-e metadata

Modelrecord in the English dataset, which means they refer tothe same ukiyo-e print. For example, for the first Japanese
metadata record in Table 1, its corresponding English
metadata record is the first record in Table 2, since theyrefer to the same ukiyo-e print. To generate this groundtruth data, for each Japanese ukiyo-e record, first, we
utilized the ukiyo-e.org image similarity analysis engine
to find the most similar metadata records in the English
dataset. Then, we manually checked whether the Japaneserecord and its most similar English record that is
3
4

Skip-gram

Window size

10

Vector dimensionality

200

To train English word embeddings, English Wikipedia
articles are used, which are the data in English Wikipedia
dump 6 as of September 2018.
To

train

Japaneseword

embeddings,

Japanese

Wikipedia articles are used, which are the data in Japanese
Wikipedia dump as of September 2018.
4.2.2 Parallel sentence corpora
To learn the mapping between the vector spaces that

http://digitalmuseum.rekibun.or.jp/app/selected/edo-tokyo

5

https://ukiyo-e.org/

http://www.metmuseum.org/

6

https://dumps.wikimedia.org/

represent Japanese and English, 600 Japanese-English

Translator Text API 8 . As it provides two translation

parallelmodels: statistical machine translation (SMT) and neural

shortJapanese-English

sentence

pairs

are

parallelshort

sentence

used.

These

pairs

are

network translation (NNT), we experimented with both

extracted from Tanaka corpus . The lengths of thesetranslation models to translate metadata. The translation

parallel short sentence pairs range from 6 to 12 words,of metadata of our experimental dataset was made on

which are equivalent to the length of ukiyo-e titles. Some

January 22, 2019.

examples of Japanese-English parallel short sentence pairs

4.2.3 Record pair comparison

7

are shown in Table 4.

Besides the metadata of the title and series name, we
also utilized the artist name and date of the ukiyo-e prints.

Table 4: Some examples of Japanese-English parallel short

Artist names. Since artist names are not the target

sentence pairs

metadata of our proposed method, we translated theJapanese artist names by using a Japanese–English

Japanese sentence

English sentence

私はテニス部員です

I'm in the tennis club

manually compiled using the authority data in the Web

多くの動物が人間によって滅ぼされた
私達は国際人になりたいと思います
彼の小説は１つも読んでいない
私は音楽が好きではありません
申告する物は何もありません

Many animals have been
destroyed by men

NDL Authorities 9 , which is a web service provided by the

We want to be international

Date. Since the dates metadata of ukiyo-e prints arebilingual list of ukiyo-e artist names. This list was

I haven't read any of his
novelsNational Diet Library (NDL) of Japan.
numeric values, they are also not the target metadata of
our proposed method. In the Japanese ukiyo-e datasets, thedates are represented in the Japanese calendar, such as the

I do not like music

examples that are shown in Table 1. However, in theI have nothing to declare

父は今家にいるだろう

My father may be at homenow

近くで火事が起こった

A fire broke out nearby

スケートの方が好きです

I like skating better

学生全員が出席した

All of the students were
present

English ukiyo-e datasets, the dates are represented in thewestern calendar, such as the examples that are shown in
Table 2. To compare the dates between the Japanesedatasets and English datasets, we use HuTime Web API 1 0
to convert the dates in Japanese calendar to westerncalendar.
In the task of record linkage, the similarity between two
records is calculated by comparing several metadatasimilarities. Here, the similarity between two ukiyo-e

In order to make the learned mapping more accurate torecords ( 𝑆5 ) is determined by combining the title

transform the metadata of ukiyo-e prints, the experiments

similarity (𝑆7*789 ), series name similarity ( 𝑆:9;*9: ), artist

further use several pairs of Japanese and English ukiyo-ename similarity (𝑆<;7*:7 ) and date similarity (𝑆=<79 ), which

titles to optimize the learned mapping, in which each

is defined in the Equation (2).

Japanese and English title pair refers to the same ukiyo-e

𝑆5 = 𝑆<;7*:7 (𝛼 ∙ 𝑆7*789 + b ∙ 𝑆:9;*9: + g ∙ 𝑆=<79 )prints.

Here, 𝛼 + b + g = 1. 𝛼 is the weight of title similarity; b

4.2.3 Baseline methods
We compare our proposed method with the performanceofcross-language(2)

recordlinkagewith

theis the weight of series name similarity; g is the weight ofdate similarity.

translation-based method. In the translation-based method,

𝑆<;7*:7 uses the exact string matching. It means 𝑆<;7*:7

we translated the titles and series names of ukiyo-e

is set as 1 if the translation of the Japanese artist name isrecords from Japanese to English by using Microsoft

the same with the English artist name. Otherwise, 𝑆<;7*:7
is set as 0.

7

𝑆=<79 is defined as follows:

Tanaka corpus consist of Japanese-English parallel sentencepairs, which are collected from Japanese−English bilingual
newspaper articles and broadcast media news reports published8

https://www.microsoft.com/en-us/translator/translatorapi.aspx

on the WWW.

9

http://id.ndl.go.jp/auth/ndla

http://www.edrdg.org/wiki/index.php/Tanaka_Corpus

10

http://ap.hutime.org/cal/index.html

|= F= |1 − =E G
𝑆=<79 = B
HIJ , 𝑖𝑓 P𝑑R − 𝑑9 P < 𝑑T<U
0, 𝑒𝑙𝑠𝑒(3)

We consider cross-language record linkage as a ranking
problem in our experiments. For each Japanese metadata
record, we ranked candidate English metadata records

Here, 𝑑R is Japanese date; 𝑑9 is English date.
Since title and series name are the target metadata of
this work, 𝑆7*789 and 𝑆:9;*9: are calculated using our
proposed method that is introduced in Section 3. In thecomparison experiments, 𝑆7*789 and 𝑆:9;*9: are calculated

according to the similarity score between them. Thus, weevaluated the ranking results in terms of Precison@n(P@n) and Recall@n (R@n).
4.3 Experimental results

using the baseline methods that are introduced in Section
4.2.3.

The experimental results are shown in Table 5 and Table
6.

4.2.4 Evaluation
Table 5: The experimental results: P@n
P@1(%)

P@2(%)

P@3(%)

P@4(%)

P@5(%)

Translation-based method (STM)

51.72

35.71

25.12

19.46

16.16

Translation-based method (NNT)

56.15

35.47

26.11

20.32

16.45

Our proposed method

55.29

34.12

21.54

18.22

14.53

Table 6: The experimental results: R@n
R@1(%)

R@2(%)

R@3(%)

R@4(%)

R@5(%)

Translation-based method (STM)

47.62

61.66

65.35

66.75

68.88

Translation-based method (NNT)

51.72

61.01

66.17

67.89

68.88

Our proposed method

51.19

52.54

54.19

58.32

61.22

Comparing two baseline methods, it can be seen that theIn the future, we plan to apply our method to otherresults of using NNT are better than STM. It indicates thatdatasets, such as book and film datasets. We also plan tothe performance of cross-language record linkage isvalidate the effectiveness of our method on the dataset in

affected by the translation quality.

other languages.

Although the P@1 and R@1 of our proposed method area bit lower than the translation-based method (NNT), themethodwithouttranslation

has

less

bilingualdata

requirements, which could possibly be applied to otherlow-resourced languages.

5. Conclusion
In this paper, we presented a method to measure thesimilarity between metadata in different languages forcross-language record linkage, which does not use anytranslation methods. This method only uses a small set ofbilingual parallel data to learn a linear mapping between
the vector space of the source language and vector spaceof the target language, which is used to transform thevector representations of metadata from the source
language to the target language.

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