=Paper=
{{Paper
|id=Vol-2293/paos2018-passcr2018_paper4
|storemode=property
|title=
Identifying Transmedia Works from User-Generated Knowledge Bases : Japanese Pop Culture Study Case
|pdfUrl=https://ceur-ws.org/Vol-2293/paos2018-passcr2018_paper4.pdf
|volume=Vol-2293
|authors=Stella Zevio,Tetsuya Mihara,Shigeo Sugimoto
|dblpUrl=https://dblp.org/rec/conf/jist/ZevioMS18
}}
==
Identifying Transmedia Works from User-Generated Knowledge Bases : Japanese Pop Culture Study Case
==
https://ceur-ws.org/Vol-2293/paos2018-passcr2018_paper4.pdf
Identifying Transmedia Works from
User-Generated Knowledge Bases : Japanese
Pop Culture Study Case
Stella Zevio1 , Tetsuya Mihara2 , and Shigeo Sugimoto2
1
LIPN - CNRS UMR 7030, Université Paris 13, France
zevio@lipn.univ-paris13.fr
2
Faculty of Library, Information and Media Science, University of Tsukuba, Japan
mihara@slis.tsukuba.ac.jp
sugimoto@slis.tsukuba.ac.jp
Abstract. As Japanese pop culture spreads worldwide, digital libraries
compiling information about works through representative media (manga,
anime, video games) emerge. Some of these works may share the same
story, characters or universe, thus being part of a conceptual instance
which we call a transmedia work in this paper. Transmedia works are ab-
stract entities composed of works through several media linked together
by semantic relationships. Identifying works belonging to the same trans-
media work is still a challenge to enhance access, retrieval and organi-
zation of media in digital libraries. To overcome this challenge, semantic
relationships between works should be identified. As no authority data
yet describes semantic relationships between works, we need to find this
information in knowledge bases generated by users such as Wikipedia.
More precisely, we exploit DBpedia, Wikipedia’s Linked Data counter-
part, to respect the semantic web standards. In this paper, we present
our method and experiment in building work entity datasets of Japanese
pop culture (manga, anime and video games) and extracting relation-
ships between these works in order to ease identification of transmedia
works from the semantic data structure used in DBpedia. We also ex-
tract pertinent information to link works to bibliographic data in the
future. We propose an evaluation of our contribution and demonstrate
that we can easily and relevantly identify works belonging to the same
transmedia work from user-generated knowledge bases.
Keywords: Transmedia work · Semantic Web · Digital Libraries · Linked
data · Domain-dependent semantic data analysis
1 Introduction
Several works through different media can sometimes express the same story,
take place in the same universe or exploit the same characters. In this case,
these works are part of the same transmedia work [17].
2 S. Zevio et al.
Example 1. ”Dragon Ball Z: Budokai” video game and ”Dragon Ball” anime
take place in the same universe and share some common characters, thus they
are both part of the same transmedia work.
Identifying transmedia works would be useful for a better access, retrieval
and organization of media, in particular for digital libraries. Indeed, identifying
semantically linked works is still a challenge and a key issue for recommandation
within digital libraries supporting various media formats.
As Japanese pop culture has long been considered as a subculture unworthy
of interest, there is no sufficient authorized data of representative media nor reli-
able knowledge bases describing relations between works. Still, there are emerg-
ing digital libraries and databases of Japanese pop culture media, as interest
in Japanese pop culture grows worldwide. Media Art Database[6] (MADB) is a
database of manga, animation and video games published in Japan, produced by
Agency for Cultural Affairs in Japan as the national authority of works through
these media. However, MADB compiles information about works through dif-
ferent media but lacks information about relationships between these works. On
the other hand, the information is available from knowledge bases generated by
users, such as Wikipedia[7].
In this research, our aim is to identify transmedia works of Japanese pop cul-
ture, as no authority data describes them. To achieve this goal, we first extract
work entity datasets of manga, anime and video games from user-based knowl-
edge bases, then exploit semantic relationships described by users between these
works to find works belonging to the same transmedia work. We use DBpedia[1],
which is Wikipedia’s Linked Open Data dataset, in order to extract relations be-
tween works according to the semantic web standards, in an interpretable and
interoperable way. Using DBpedia enables us to take advantage of the simplicity,
interoperability and interpretability brought by semantic web technologies. We
choose to exploit English resources as they are known to be richer than Japanese
ones. This method is of course heavily dependent on the data structure used in
DBpedia thus we’re discussing this issue in section 4. Our contribution lies at
the interface between domain-dependent semantic data analysis and knowledge
extraction from Linked Data.
This paper presents our method and experiment in building work entity
datasets of manga, anime and video games and extracting semantic relation-
ships between them in order to identify works belonging to the same transmedia
work. In section 2, we present related work. In section 3 we present our experi-
ment, results we obtained as well as an evaluation. In section 4 we present our
conclusions and we discuss about further work.
2 Related Work
Bibliographic information describing semantic relationships between works is
useful when it comes to transmedia publications like adaptations for example.
The Functional Requirements for Bibliographic Records (FRBR)[13] model, de-
veloped by the International Federation of Library Associations and Institutions
Semantic Relationships from User-Generated Knowledge Bases 3
(IFLA)[16], defines entities and their relationships for advanced functions of bib-
liographic records. In the FRBR model, work entity is defined as an abstract one
to express distinct intellectual or artistic creation. Different editions or transla-
tions of the same creation are semantically connected to each other. In addition,
works belonging to the same creation group also have semantic relationships
between each other, for example, William Shakespeare’s Romeo and Juliet and
its namesake film adaptation from Franco Zeffirelli.
The FRBR model is commonly used as a conceptual data model for biblio-
graphic records [16], cataloging rules (Resource Description and Access (RDA)[8]
being the most representative one) and even pop-culture databases. McDonough[12]
evaluates the usability of the FRBR model to describe relationships between var-
ious editions, translations, and adaptations of video games. Jett[10] developed a
conceptual model reflecting FRBR for video games and interactive media.
On the other hand, if the FRBR model intends describing relationships be-
tween entities, there is actually a lack of datasets or records describing such
entity relationships, especially for Japanese pop-culture[11]. OCLC WorldCat
Fiction Finder[3] provides data about relationships between different editions of
the same work. Unfortunately, records for animation and video game are not
well covered by this database.
A method for creating FRBR dataset from existing datasets and conventional
bibliographic records is FRBRization[15]. For example, WorldCat Fiction Finder
is populated from MARC[5] bibliographic and authority records by using OCLC
FRBR Work-Set Algorithm[4]. He et al.[9] proposed a method for identifying
FRBR Works using Wikipedia, through DBpedia articles for manga. DBpedia
is used as a reference authority in order to identify Work level entities of manga
in the catalog records of Kyoto Manga Museum which is the largest library for
manga in Japan. Takhirov[14] proposed a method for linking a FRBR entity
to its corresponding LOD entity and an evaluation using DBpedia and Amazon
bookstore’s Web API. Although He and Takhirov focus on the information about
books and do not show interest about transmedia works, they suggest that using
DBpedia as a source of work entities and their relationships is a viable solution.
As DBpedia has many resources about transmedia works including manga, anime
and video games, our contribution aims at measuring the quantity and quality of
transmedia works and semantic relationships between them that we can extract
from DBpedia with simple SPARQL queries.
3 Experiment
3.1 Overview
In order to identify transmedia works, we conduct an experiment consisting in
two steps. As few authority datasets are available, our first step described in
section 3.2 consists in building our own work entity datasets of manga, anime
and video games from DBpedia. The second step described in section 3.3 consists
in exploiting semantic relationships described by users to link works through
several media together.
4 S. Zevio et al.
We harvest DBpedia SPARQL endpoint as well as DBpedia Live[2] SPARQL
endpoint and compare the results obtained with both. DBpedia Live SPARQL
endpoint is the most up-to-date one as it is continuously synchronized with
Wikipedia, while DBpedia SPARQL endpoint is only updated periodically. In a
theoretical setting, we should expect more accurate results with DBpedia Live,
assuming that knowledge available on DBpedia is growing larger and more ac-
curate with the Wikipedia users’ contributions.
3.2 Datasets
In DBpedia, a concept is described by an article. An article is defined as a RDF
resource and additional information such as links to other articles are described
as properties of the RDF resource. To determine than an article describes a
manga, an anime or a video game, we exploit its rdf:type property. Indeed, an
article describing a manga would have rdf:type property dbo:Manga. An arti-
cle about an anime or a video game would have rdf:type property dbo:Anime or
dbo:VideoGame respectively. We are building the manga, anime and video games
datasets harvesting DBpedia and DBpedia Live with the SPARQL queries stuc-
ture described in query 1.1. In table 1 we present the number of results obtained.
1 SELECT DISTINCT ? Concept WHERE {
2 ? Concept rdf : type dbo : Manga }
Listing 1.1. SPARQL query : Manga
Media Number of results (DBpedia) Number of results (DBpedia Live)
Manga 3783 3928
Anime 4271 5014
Video games 28869 20807
Table 1. Datasets of manga, anime and video games obtained by harvesting DBpedia
and DBpedia Live on 12-20-2017
3.3 Identification of works belonging to the same transmedia work
In order to identify semantic relationships between works through several media,
we exploit semantic links between articles describing works. For an example, an
article describing an anime may have a dct:subject property which would apply
to an article describing a manga. If that so, it would mean that this anime and
this manga belong to the same transmedia work. We exploit any direct semantic
relationship between articles about works through several media as well as some
indirect ones. Queries are all derived from query structure shown in query 1.2.
In table 2 we present the number of results obtained.
1 SELECT DISTINCT ? Manga ? Anime WHERE {
Semantic Relationships from User-Generated Knowledge Bases 5
2 {? Manga rdf : type dbo : Manga .
3 ? Anime rdf : type dbo : Anime .
4 ? Anime ? p ? Manga }
5 UNION
6 {? Anime rdf : type dbo : Anime .
7 ? Manga rdf : type dbo : Manga .
8 ? Manga ? p ? Anime }
9 UNION
10 {? Anime rdf : type dbo : Anime .
11 ? Anime dct : subject ? Category .
12 ? Category skos : broader dbc :
Wikipedia_categories_named_after_anime_and_manga_series
.
13 ? Manga rdf : type dbo : Manga .
14 ? Manga dct : subject ? Category }}
Listing 1.2. SPARQL query : Manga-Anime belonging to the same transmedia work
Transmedia works Number of results (DBpedia) Number of results (DBpedia Live)
Manga - Anime 764 696
Manga - Video games 864 191
Anime - Video games 411 135
Table 2. Couples of works through different media belonging to the same transmedia
work obtained by harvesting DBpedia and DBpedia Live on 12-20-2017
3.4 Extraction of links between works and bibliographic data
Digital libraries may compile informations about works through bibliographic
data according to the FRBR model [3]. Therefore, it is a key issue to reconcile
works to bibliographic data. In order to ease this reconciliation, we exploit se-
mantic links between articles describing manga and list of chapters as well as
anime and list of episodes according to the query structure shown in query 1.3.
In table 3 we present the number of results obtained.
1 SELECT DISTINCT ? Manga ? List WHERE {
2 {? List dct : subject
3 dbc : L i s t s _ o f _ m a n g a _ v o l u m e s _ a n d _ c h a p t e r s .
4 ? Manga rdf : type dbo : Manga .
5 ? Manga ? p ? List }
6 UNION
7 {? Manga rdf : type dbo : Manga .
8 ? List dct : subject
9 dbc : L i s t s _ o f _ m a n g a _ v o l u m e s _ a n d _ c h a p t e r s .
10 ? List ? p ? Manga }
6 S. Zevio et al.
11 UNION
12 {? List dct : subject
13 dbc : L i s t s _ o f _ m a n g a _ v o l u m e s _ a n d _ c h a p t e r s .
14 ? List dct : subject ? Category .
15 ? Category skos : broader
16 dbc :
Wikipedia_categories_named_after_anime_and_manga_series
.
17 ? Manga rdf : type dbo : Manga .
18 ? Manga dct : subject ? Category }}
Listing 1.3. SPARQL query : Manga - List of chapters
Work - Bibliographic data Number of results (DBpedia) Number of results (DBpedia Live)
Manga - List of chapters 244 247
Anime - List of episodes 266 275
Table 3. Couples of works and bibliographic data obtained by harvesting DBpedia
and DBpedia Live on 12-20-2017
3.5 Evaluation
As no gold standard is available for data about manga, anime nor video games
and semantic links between them as far as we know from the literature, calculate
a recall is impossible. It is difficult to judge whether or not our queries have a
good coverage of the domain. A possible solution would be to manually collect
all works related through several media for a certain number of known works
then evaluate the recall of our method according to this restricted gold standard.
However, even building a restricted gold standard requires a very high level of
expertise and most experts would rely on user-generated knowledge bases at
some point. Thus, we don’t propose a recall measure.
Still, we can evaluate the accuracy of the queries and detect the relevance of
the results returned. To estimate the relevance of our results, we conducted an
evaluation consisting in randomly selecting 100 results for each query, asking two
external experts of the domain to test the exactitude of each result. In the end,
we obtain an accuracy as well as all errors raised. This information is available
in tables 4, 5 and 6, along with error types encountered.
We managed to obtain overall precise results. As expected, results obtained
with DBpedia Live SPARQL endpoint are more precise than with DBpedia
SPARQL endpoint, but with a surprisingly huge gap between them. From the
error types and the precision drop with DBpedia SPARQL endpoint concerning
the construction of the video games dataset and the identification of transmedia
works, we can assert that the results are heavily dependant on the semantic data
structure described by the users, which may potentially be inconsistent, as it’s
human-generated data.
Semantic Relationships from User-Generated Knowledge Bases 7
Query DBpedia Error types DBpedia Error types
Live
1 Manga 94 % Something else than a 94 % Something else than a
manga (Manga genre, novel, manga (Novels)
film, wafer silicon, anime)
2 Anime 91 % Something else than an 94 % Something else than an
anime (Drama, live action, anime (Drama, live action)
director, studio, magazine,
method of animation)
3 Video games 26 % Something else than a video 100 %
game (Card game, board
game, Superbowl, gaming
platform)
Table 4. Evaluation on query results obtained on 12-20-2017 (Work entity datasets of
manga, anime and video games)
Query DBpedia Error types DBpedia Error types
Live
4 Manga - List of 91 % A list of chapters is associ- 85 %
• A list of chapters is asso-
chapters ated to something that is not
ciated to something that
a manga (Volume, mangaka
is not a manga (Volume)
(person))
• The list of chapters as-
sociated does not cor-
respond to the manga
(Chapters of a manga
from the same series)
5 Anime - List of 80 % 90 %
• Something else than • Something else than an
episodes
an anime (DBpedia anime (Visual novel)
page about ”Anime” in • The list of episodes does
general) not correspond to the
• The list of episodes does anime (episodes of an
not correspond to the anime from the same or
anime (episodes of an from different series)
anime from the same se-
ries)
Table 5. Evaluation on query results obtained on 12-20-2017
8 S. Zevio et al.
Query DBpedia Error types DBpedia Error types
Live
6 Manga - Anime 8 % 89 % The manga and the anime
• Something else than a
do not belong to the same
manga (Manga genre,
transmedia work
company, magazine,
mangaka (person))
• Something else than
an anime (DBpedia
page about ”Anime” in
general, list of episodes)
• The manga and the anime
do not belong to the same
transmedia work
7 Manga - Video 41 % 77 %
• Something else than a • Something else than a
games
manga (Manga series, manga (Novels)
manga genre, company, • The manga and the video
magazine, mangaka game do not belong to the
(person)) same transmedia work
• The manga and the video
game do not belong to the
same transmedia work
8 Anime - Video 32 % 95 %
• Something else than an • Something else than an
games
anime (DBpedia page anime (Drama)
about ”Anime” in gen- • The anime and the video
eral, company, visual game do not belong to the
novel) same transmedia work
• Something else than a
video game (Gaming
hardware, video game
genre, gaming platform)
Table 6. Evaluation on query results obtained on 12-20-2017
Semantic Relationships from User-Generated Knowledge Bases 9
4 Discussion and conclusion
With the help of very simple SPARQL queries, we managed to build work entity
datasets of manga, anime and video games, which is hard to create manually
by simple computational method without authority datasets. We prepared a fu-
ture linkage between works and bibliographic data, by linking manga to their
list of chapters and anime to their list of episodes. We also identified seman-
tic relationships between manga, anime and video games, creating a semantic
network that enables us to easily identify transmedia works. Although it is dif-
ficult to estimate the coverage of the domain as no gold standard is available,
we managed to obtain satisfying results in terms of accuracy as well as a solid
number of results. As expected, we obtained better results harvesting DBpedia
Live SPARQL endpoint, which is the most up-to-date one.
We identified several limitations on this work. First, we use knowledge bases
with user-generated content, which are not always exhaustive. Indeed, informa-
tion may not be available in Wikipedia, or may be available in Wikipedia but not
semantically described with accurateness in DBpedia. This limitation is closely
related to the lack of authority data in this field, so it is a compromise that
has to be made. Then, we obtained disparate results according to the SPARQL
endpoint used. Therefore, using an up-to-date endpoint is a key feature. Indeed,
consistency of user-generated data is not ensured.
To pursue this work, an interesting research question would be to determine
how to link data to records of publications in different countries. A comparison
between English and Japanese resources would help us determine if multilingual
processes would help us expand our results.
Acknowledgements
This work was supported by JSPS KAKENHI Grant Number 16H01754.
References
1. DBpedia. https://dbpedia.org, [Online; accessed 26-July-2018]
2. DBpedia Live. http://live.dbpedia.org/sparql, [Online; accessed 26-July-2018]
3. FictionFinder: A FRBR-based Prototype for Fiction in WorldCat. https://
www.oclc.org/research/activities/fictionfinder.html, [Online; accessed 15-
August-2018]
4. FRBR Work-Set Algorithm. https://www.oclc.org/research/activities/
frbralgorithm.html, [Online; accessed 15-August-2018]
5. MARC. http://www.loc.gov/marc/umb/um01to06.html, [Online; accessed 15-
August-2018]
6. Media Art Database. https://mediaarts-db.bunka.go.jp/, [Online; accessed 26-
July-2018]
7. Wikipedia. https://en.wikipedia.org, [Online; accessed 26-July-2018]
8. Steering Committee, T.R.: About RDA. http://rda-rsc.org/content/
about-rda, [Online; accessed 15-August-2018]
10 S. Zevio et al.
9. He, W., Mihara, T., Nagamori, M., Sugimoto, S.: Identification of Works of Manga
Using LOD Resources: An Experimental FRBRization of Bibliographic Data of
Comic Books. In: Proceedings of the 13th ACM/IEEE-CS Joint Conference on
Digital Libraries. pp. 253–256. JCDL ’13 (2013)
10. Jett, J., Sacchi, S., Lee, J.H., Clarke, R.I.: A Conceptual Model for Video Games
and Interactive Media. J. Assoc. Inf. Sci. Technol. 67(3), 505–517 (2016)
11. Kiryakos, S., Sugimoto, S., Nagamori, M., Mihara, T.: Aggregating Metadata from
Heterogeneous Pop Culture Resources on the Web. In: International Conference
on Dublin Core and Metadata Applications. pp. 65–74 (2017)
12. McDonough, J., Kirschenbaum, M., Reside, D., Fraistat, N., Jerz, D.: Twisty Little
Passages Almost All Alike: Applying the FRBR Model to a Classic Computer
Game. Digital Humanities Quarterly 4(2) (2010)
13. IFLA Study Group on the Functional Requirements for Bibliographic Records:
Functional Requirements for Bibliographic Records. https://www.ifla.org/
publications/functional-requirements-for-bibliographic-records, [On-
line; accessed 15-August-2018]
14. Takhirov N., Duchateau F., A.T.: Linking FRBR Entities to LOD through Se-
mantic Matching. Research and Advanced Technology for Digital Libraries. TPDL
2011. Lecture Notes in Computer Science 6966, 69–76 (2011)
15. Takhirov N., Duchateau F., A.T.: Supporting FRBRization of Web Product De-
scriptions. Research and Advanced Technology for Digital Libraries. TPDL 2011.
Lecture Notes in Computer Science 6966, 284–295 (2011)
16. Tillett, B.: What is FRBR? A Conceptual Model for the Bibliographic Universe.
Australian Library Journal 54(1), 24–30 (2005)
17. Vukadin, A.: Bits and Pieces of Information: Bibliographic Modeling of Transme-
dia. Cataloging & Classification Quarterly 52(3), 285–302 (2014)