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 abstract entities composed of works through several media linked together by semantic relationships. Identifying works belonging to the same transmedia work is still a challenge to enhance access, retrieval and organization of media in digital libraries. To overcome this challenge, semantic relationships between works should be identi ed. As no authority data yet describes semantic relationships between works, we need to nd this information in knowledge bases generated by users such as Wikipedia. More precisely, we exploit DBpedia, Wikipedia's Linked Data counterpart, 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 relationships between these works in order to ease identi cation of transmedia works from the semantic data structure used in DBpedia. We also extract 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.
Several works through di erent 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 [
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 su cient authorized data of representative media nor
reliable knowledge bases describing relations between works. Still, there are
emerging digital libraries and databases of Japanese pop culture media, as interest
in Japanese pop culture grows worldwide. Media Art Database[
In this research, our aim is to identify transmedia works of Japanese pop
culture, as no authority data describes them. To achieve this goal, we rst extract
work entity datasets of manga, anime and video games from user-based
knowledge bases, then exploit semantic relationships described by users between these
works to nd works belonging to the same transmedia work. We use DBpedia[
This paper presents our method and experiment in building work entity datasets of manga, anime and video games and extracting semantic relationships 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 experiment, results we obtained as well as an evaluation. In section 4 we present our conclusions and we discuss about further work. 2
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)[
The FRBR model is commonly used as a conceptual data model for
bibliographic records [
On the other hand, if the FRBR model intends describing relationships
between entities, there is actually a lack of datasets or records describing such
entity relationships, especially for Japanese pop-culture[
A method for creating FRBR dataset from existing datasets and conventional
bibliographic records is FRBRization[
In order to identify transmedia works, we conduct an experiment consisting in two steps. As few authority datasets are available, our rst 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.
We harvest DBpedia SPARQL endpoint as well as DBpedia Live[
In DBpedia, a concept is described by an article. An article is de ned 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 article 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 stucture described in query 1.1. In table 1 we present the number of results obtained. 1 S E L E C T D I S T I N C T ? C o n c e p t W H E R E { 2 ? C o n c e p t rdf : type dbo : M a n g a }
Table 1. Datasets of manga, anime and video games obtained by harvesting DBpedia and DBpedia Live on 12-20-2017 3.3
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. 2 3 4 5 6 7 8 9 10 11 12 13 14 {? Manga rdf : type dbo : Manga . ? Anime rdf : type dbo : Anime .
? Anime ?p ? Manga } UNION {? Anime rdf : type dbo : Anime . ? Manga rdf : type dbo : Manga .
? Manga ?p ? Anime } UNION {? Anime rdf : type dbo : Anime . ? Anime dct : subject ? Category . ? Category skos : broader dbc :
Wikipedia_categories_named_after_anime_and_manga_series . ? Manga rdf : type dbo : Manga .
? Manga dct : subject ? Category }}
Digital libraries may compile informations about works through bibliographic
data according to the FRBR model [
In table 3 we present the number of results obtained. 2 3 4 5 6 7 8 9 10 1 SELECT DISTINCT ? Manga ? List WHERE { {? List dct : subject dbc : Lists_of_manga_volumes_and_chapters . ? Manga rdf : type dbo : Manga .
? Manga ?p ? List } UNION {? Manga rdf : type dbo : Manga . ? List dct : subject dbc : Lists_of_manga_volumes_and_chapters .
? List ?p ? Manga }
U N I O N {? List dct : s u b j e c t 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 . ? List dct : s u b j e c t ? C a t e g o r y . ? C a t e g o r y skos : b r o a d e r dbc :
W i k i p e d i a _ c a t e g o r i e s _ n a m e d _ a f t e r _ a n i m e _ a n d _ m a n g a _ s e r i e s . ? M a n g a rdf : type dbo : M a n g a . ? M a n g a dct : s u b j e c t ? C a t e g o r y }}
3.5
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 di cult 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 identi cation 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.
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 future linkage between works and bibliographic data, by linking manga to their list of chapters and anime to their list of episodes. We also identi ed semantic relationships between manga, anime and video games, creating a semantic network that enables us to easily identify transmedia works. Although it is difcult 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 identi ed several limitations on this work. First, we use knowledge bases with user-generated content, which are not always exhaustive. Indeed, information 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 eld, 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 di erent countries. A comparison between English and Japanese resources would help us determine if multilingual processes would help us expand our results.
This work was supported by JSPS KAKENHI Grant Number 16H01754.