=Paper=
{{Paper
|id=Vol-1690/paper90
|storemode=property
|title=Cross-Language Record Linkage using Word Embedding driven Metadata Similarity Measurement
|pdfUrl=https://ceur-ws.org/Vol-1690/paper90.pdf
|volume=Vol-1690
|authors=Yuting Song,Taisuke Kimura,Biligsaikhan Batjargal,Akira Maeda
|dblpUrl=https://dblp.org/rec/conf/semweb/SongKBM16
}}
==Cross-Language Record Linkage using Word Embedding driven Metadata Similarity Measurement==
https://ceur-ws.org/Vol-1690/paper90.pdf
Cross-Language Record Linkage using Word Embedding
driven Metadata Similarity Measurement
Yuting Song1, Taisuke Kimura1, Biligsaikhan Batjargal2, Akira Maeda3
1
Graduate School of Information Science and Engineering, Ritsumeikan University, Japan
{gr0260ff, is0013hh}@ed.ritsumei.ac.jp
2
Research Organization of Science and Engineering, Ritsumeikan University, Japan
biligee@fc.ritsumei.ac.jp
3
College of Information Science and Engineering, Ritsumeikan University, Japan
amaeda@is.ritsumei.ac.jp
Abstract. Aiming to link the records that refer to the same entity across multi-
ple databases in different languages, we address the mismatches of wordings
between literal translations of metadata in source language and metadata in tar-
get language, which cannot be calculated by string-based measures. In this pa-
per, we propose a method based on word embedding, which can capture the
semantic similarity relationships among words. The effectiveness of this meth-
od is confirmed in linking the same records between Ukiyo-e (Japanese wood-
block printing) databases in Japanese and English. This method could be ap-
plied to other languages since it makes little assumption about languages.
Keywords: Cross-language record linkage ·Similarity measurement ·Word
embedding ·Semantic matching
1 Introduction
Cross-language record linkage is a task of finding pairs of records that refer to the
same entity across multiple databases in different languages. It is crucial to various
fields, such as federated search and data integration. Furthermore, the metadata of
identical records in different languages are helpful to build multilingual Linked Data.
Cross-language record linkage consists of two steps. First, the metadata of a record,
e.g. title, author, publisher, in the source language are translated into the target lan-
guage based on bilingual dictionaries. Next, identical records are determined by cal-
culating the similarities between metadata within one language, which is similar to the
monolingual record linkage [1].
In monolingual record linkage, the mismatches are mainly due to the typographical
variations of string data, which can be measured by string-based comparison. Never-
theless, when it comes to cross-language record linkage, the mismatches of wordings
between literal translations and metadata in target language cannot be measured by
simple metrics. Figure 1 gives an example of this type of mismatch. The word “白雨”
in Japanese is translated into “rainfall” by a Japanese-English bilingual dictionary.
However, the corresponding word in English title is “storm”, which is translated by a
human expert translator. Such a mismatch is due to the use of different wordings to
express the same meaning, which cannot be measured by string-based similarity.
Some approaches exploit the network structure of records deeply in knowledge bases
to determine the identical records [2]. However, in most databases, unlike Wikipedia
or WordNet, the network structure of records cannot be obtained easily.
Japanese database English database
作品名(Title): 山下白雨 Title: Storm below Mount Fuji
作家(Artist): 葛飾北斎 Artist: Katsushika Hokusai
Step1: Translating
Step2: Matching
mount, under, rainfall Storm below Mount Fuji
Fig. 1. An example of mismatches of wordings between literal translations of metadata in
source language and metadata in target language
In this paper, we propose a method for cross-language record linkage that can
measure the similarities between metadata with the same meaning but in different
wordings. Our method is based on distributed representations of words [3] (a.k.a.
word embedding), in which semantically similar words are closer in vector space. The
effectiveness of this approach is evaluated in the record linkage across Ukiyo-e data-
bases in Japanese and English.
2 Methodology
As mentioned above, cross-language record linkage can be divided into two steps:
translating and matching. We focus on the second step, especially the matching
among non-proper nouns in metadata. The reason is that non-proper nouns are more
likely to be translated into different words than proper nouns. Proper nouns can usual-
ly be transliterated, which have a one-to-one mapping.
2.1 Learning Distributed Representations of Words
Distributed representations for words are dense, low-dimensional and real-valued
vectors, which were firstly proposed by Rumelhart et al. [4]. Recently, the distributed
skip-gram model for learning word representations was introduced by Mikolov et al.
[3]. This model employs simple neural network architecture, which can be trained on
a large amount of unstructured text data in a short time (billions of words in hours).
Besides, the distributed representations of words learnt by this model can capture
semantic similarity relationships. Considering the advantages above, we utilize the
skip-gram model of Mikolov et al. for learning word representations in our method.
2.2 Similarity Measurement between Metadata
In the proposed method, the similarity metric between the literal translations of
metadata in source language (𝑀𝑙𝑡 ) and metadata in target language (𝑀𝑡 ) is defined in
Formula 1. 𝑁𝑃(𝑀𝑙𝑡 ), 𝑁𝑃(𝑀𝑡 ) are the number of non-proper nouns in 𝑀𝑙𝑡 and 𝑀𝑡
respectively. 𝑛𝑝𝑖 is a non-proper noun in 𝑀𝑙𝑡 . 𝐶(𝑛𝑝𝑖 ) is the number of candidate
translations of 𝑛𝑝𝑖 . 𝑣𝑖𝑗 is the distributed representation of a candidate translation of
𝑛𝑝𝑖 . Similarly, 𝑣𝑞 is the distributed representation of a non-proper noun in 𝑀𝑡 .
𝑠𝑐𝑜𝑟𝑒(𝑛𝑝𝑖 ) is the matching degree of 𝑛𝑝𝑖 , which is the maximal value of similarity
between candidate translations of 𝑛𝑝𝑖 and non-proper nouns in 𝑀𝑡 . 𝑐𝑜𝑠𝑖𝑛𝑒(𝑣𝑖𝑗 , 𝑣𝑞 ) is
the cosine similarity between 𝑣𝑖𝑗 and 𝑣𝑞 . 𝑁𝑝 means the number of matched proper
nouns. 𝑤𝑝 and 𝑤𝑛𝑝 are weights of proper nouns and non-proper nouns respectively. L
is the total number of words in 𝑀𝑙𝑡 .
𝑁𝑃(𝑀𝑙𝑡 )
S(𝑀𝑙𝑡 , 𝑀𝑡 ) = [ 𝑤𝑝 ∙ 𝑁𝑝 + 𝑤𝑛𝑝 ∙ ∑𝑖=1 𝑠𝑐𝑜𝑟𝑒(𝑛𝑝𝑖 ) ]⁄𝐿 (1)
𝐶(𝑛𝑝 ) 𝑁𝑃(𝑀 )
where 𝑠𝑐𝑜𝑟𝑒(𝑛𝑝𝑖 ) = max [ ∑𝑗=1 𝑖 ∑𝑞=1 𝑡 𝑐𝑜𝑠𝑖𝑛𝑒(𝑣𝑖𝑗 , 𝑣𝑞 ) ]
3 Experiments
In this section, we evaluate the effectiveness of our proposed method in linking the
same Ukiyo-e prints between the databases in Japanese and English.
3.1 Experimental Setup
The titles of Ukiyo-e prints are used to identify the same records. The experimental
data set consists of 243 Japanese titles of Ukiyo-e prints in the Edo-Tokyo Museum1
and 3,293 English titles in the Metropolitan Museum of Art2, in which each Japanese
title has at least one corresponding English title. Among the 243 Japanese titles, 143
titles are descriptive titles that contain at least one non-proper noun.
Here we translate non-proper nouns of Japanese titles into English by using EDR
Japanese-English bilingual dictionary3. The proper nouns are transliterated by Hep-
burn Romanization system4. Distributed representations of words are learnt from the
text data in English Wikipedia dump that contains more than 3 billion words. The
similarities between the literal translations of Japanese titles and English titles are
calculated by our proposed method (Formula 1). Besides, we use a baseline for com-
1
http://digitalmuseum.rekibun.or.jp/app/selected/edo-tokyo
2
http://www.metmuseum.org/
3
http://www2.nict.go.jp/out-promotion/techtransfer/EDR/index.html
4
https://en.wikipedia.org/wiki/Hepburn_romanization
parison experiments. It is using string matching to measure the similarities among
words in titles [5], which is shown in Formula 2. 𝑁𝑝 and 𝑁𝑛𝑝 are the number of
matched proper nouns and non-proper nouns in literal translations of Japanese titles
respectively. 𝑤𝑝 and 𝑤𝑛𝑝 are their weights. L is the total number of words in a Japa-
nese title. We set 𝑤𝑝 , 𝑤𝑛𝑝 equal to 2 and 1 respectively, which is the same as [5].
Here, proper nouns are given a higher weight than non-proper nouns, because proper
nouns are representative features for calculating similarity in our proposed method.
Similarity metric = (𝑤𝑝 ∙ 𝑁𝑝 + 𝑤𝑛𝑝 ∙ 𝑁𝑛𝑝 )⁄𝐿 (2)
3.2 Experimental Results
Table 1 shows the performance of baseline and our proposed method for cross-
language record linkage using descriptive titles and all titles. From the results, it can
be seen that our proposed method is better than the baseline method, especially for
descriptive titles that contain one or more non-proper nouns.
Table 1. Results of cross-language record linkage.
The precision of descriptive titles The precision of all titles
Baseline 0.31 0.27
Our method 0.43 0.34
4 Conclusion
In this paper, we proposed a method that employs the distributed representations of
words to measure metadata similarities for cross-language record linkage. Experi-
mental results have shown that this approach improves the precision of cross-
language record linkage between Ukiyo-e databases in Japanese and English. In the
future, we plan to improve the similarity metric by measuring the degree of similarity
between word embedding.
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