SUGISAKAMASANORI

Journal of Robotics, Networking and Artificial Life
Vol. 9(4); March (2023), pp. 379–384
ISSN (Online):2352-6386; ISSN (Print):2405-9021
https://alife-robotics.org/jrnal.html

Jumpei Ono1, Motoki Kumagai2, Takashi Ogata3
1

Faculty of Software and Information Technology, Aomori University, 2-3-1 Kohbata, Aomori-shi, Aomori, 030-0943, Japan
Graduate School of Software and Information Science, Iwate Prefectural University, 152-52 Sugo, Takizawa, Iwate, 020-0693, Japan
Faculty of Software and Information Science, Iwate Prefectural University, 152-52 Sugo, Takizawa, Iwate, 020-0693, Japan

2
3

Received 02 December 2021
Accepted 03 April 2023

Narrative generation

The story units in this study are the units described in Common Lisp based on the types offolktales analyzed by Seki et al. We have been developing story units for our integrated narrative
generation system (INGS) as a type of narrative technique that generates a new narrative basedon the synthesis, transformation, expansion, and so on, of a narrative structure. The story units
function in the INGS with conceptual dictionaries. In this paper, we combine these story units
with a verb conceptual dictionary and a noun conceptual dictionary. The constitutional elementsof each story unit are verb and noun concepts; therefore, combining them with a noun conceptual
dictionary enables the substantial function of story units based on the types of Japanese folktales
as a group of narrative techniques in the INGS.

Noun conceptual dictionary
Story generation© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

1. Introduction
There are various approaches to narrative generation. For
instance, Ogata divided the majority of past narrative
generation systems into a problem-solving or planningbased approach, a structural approach, and a synthetic
approach [1]. Planning-based models view the sequence of
events that make up a story as a causal sequence toward an
end goal. For example, the TALE-SPIN system [2]generates a story by taking a character’s goal as the story’s
goal and using an inference engine to connect the events
that lead from the initial to the final state. In contrast,
structural models involve a narrative generation process
based on narrative structural formalizations, such as story
grammar and story schema. Two systems, GESTER [3]and JOSEPH [4], consist of an interpreter with both a story
grammar and a world model. The BRUTUS system [5]employs a blended approach by using various problem-

solving methods, such as goal-based planning, andstructural techniques, such as story grammar and theme
structure. Such synthetic systems can be grouped into athird approach.
The comprehensive survey of narrative generation
systems by Alhussain and Azmi [6] covers machinelearning and neural network approaches as narrative
generation techniques. For example, the Buncho system [7]uses GPT2, a version of a generative pre-trainedtransformer (GPT), to perform machine learning using a
corpus of novels on the Web.
Moreover, the introduction of conceptual and literaryknowledge is effective for more interesting narrative
generation. These two themes were positioned at the centerof our narrative generation research. Ogata developed
several conceptual dictionaries to flexibly deal withconceptual and linguistic knowledge for various aspects ofnarrative generation [8] and presented a method tocomputationally treat the knowledge of types of folktales

Corresponding author’s E-mail: j.ono@aomori-u.ac.jp, g231u020@s.iwate-pu.ac.jp, t-ogata@iwate-pu.ac.jp379

[9]. Concerning the literary knowledge, Ogata publishedthree books that systematically describe from thecomprehensive survey of literary knowledge to theimplementation of several systems combined to literary
theories [10], [11], [12].
Alhussain and Azm also state that the literaryknowledge based on narrative theories is critical for
narrative interest in story generation. They cited our papers
related to Genette’s narrative discourse theory [13], [14],which showed a systematic classification of narrative
discourse techniques in a novel [15].
In the above research context, we have developed an
integrated narrative generation system (INGS) for story
generation that introduces six types of knowledge [10],[11]. The INGS organically combines event generation
using a conceptual dictionary [8] with narratology-based
generation through a system [16], [17] based on Propp’s
theory [18] and Genette’s narrative discourse [15].
Narratology has been used as a framework for sociologicalanalysis in recent years; however, traditional narrative
theory focuses on texts such as folktales and novels.
Propp’s theory is based on folktales, whereas Genette’s
narrative discourse is based on novel texts. We have been
incorporating findings from narrative theory into oursystem, and this study extends the research that introduces
the structural knowledge of stories in Japanese folktales.
This study focuses on “Nihon Mukashi-Banashi Taisei”(“The Complete Collection of Japanese Folktales”) [19].
“The Complete Collection of Japanese Folktales”
comprises the categories of folktales (types of folktales)
based on the classification of European folktales by Aarne[20] and the collection of folktales by Seki et al. We used
the types of folktales included in Volume 11 (Note:
Volumes 1 to 10 contain the folktales Seki collected, andVolume 12 is a collection of research papers on folktales)
for this study.
We created folktale programs based on these types offolktales [21], [22]. The structure, termed as “story units,”
was created from 825 folktales. Story units are knowledge
used in story generation to create a story structure. The
INGS synthesizes them based on conceptual dictionaries[8]. Therefore, we need to combine the story units andconceptual dictionaries for matched the case structure of
events in the former with concepts stored in the latter. At
first, we combined verbs in story units with verb concepts[9]. The remaining targets are elements (mostly nouns)
stored in cases of the verbs of the story units.

In this paper, we aim to combine nouns in the story units
with the noun conceptual dictionary to generate stories
using the story units in various techniques shown in Section
4. This paper is closely associated with both conceptual andliterary knowledge types for narrative generation, which are
the most important topics in our narrative generation
research.
2. Overview of Story Units and the Noun
Conceptual Dictionary
This section describes the two elements that must be
combined: the story units and the noun conceptual
dictionary.
2.1. Overview of story units and an example
A story unit is the knowledge used to generate a story, andis a record of the story’s structure. It is based on the
structure of a Japanese folktale, and its content expresses
the structure of the folktale compiled by Seki et al. A story
unit contains two or more events. Events have a casestructure consisting of a verb and its accompanying case,each of which is associated with the conceptual dictionaries
described in Section 2.2. The INGS generates stories byediting story units or combining story units based on
conceptual dictionaries.
Fig. 1 shows an example of the story unit. To make thestory units available for story generation, we combined
verbs and verb concepts [9]. However, the story units do
not combine with noun concepts. The story shown in Fig.1
is “A woman wears a demon-mask in a mountain. A
monster sees the mask and runs away from her. The woman
takes a treasure and goes home or becomes the owner of themonster’s house.”
The details of Fig. 1 are described below. “motif0669” isthe ID for the story unit. The “鬼の面[demon-mask]” is thestory unit’s name. The list of verbs that follow this namesummarizes the structure of the story unit. It is followed bya list of the story unit’s case structures. The verbs in thecase structure are described using verb concepts. A verb
concept has the descriptive form of a verb and a serial
number. It indicates any one of the several meanings thatthe verb has, depending on this number. “Agent,” “object,”“counter-agent,” “location,” “to” are cases. Cases mainly
store nouns, noun phrases, and adverbs and may store
events. “(event 被る 1[wear] (agent (&sc 女[woman]))(object (&sc 鬼 の 面 [demon-mask])) (location (&sc 山[mountain])”shows “A woman wears a demon-mask in a
mountain.”380

(motif0669 (鬼の面[demon-mask](被る 1[wear] 見る 1[look] 逃げる 1[run-away](or (取る 1[take] 帰る 1[go-home])
なる 1[become])))((1 (event 被 る 1[wear] (agent (&sc 女[woman]))(object (&sc 鬼の面[demon-mask]))(location (&sc 山[mountain]))(2 (event 見る 1[look](agent (&sc 化け物[monster]))(counter-agent (&sc 女[woman]))))(3 (event 逃げる 1[run-away](agent (&sc 化け物[monster]))))(or(4a (event 取る 1[take](agent (&sc 女[woman]))(object (&sc 宝物[treasure])))(event 帰る 1[go-home](agent (&sc 女[woman]))))(4b (event なる 1[become](agent (&sc 女[woman]))(to(&sc 化 け 物 屋 敷 の 主 人 [host-of-monsterhouse])))))))“Depth” indicates the position in the noun conceptual
dictionary’s hierarchical structure, which can containvalues from 1 to 13.
“Hype” indicates the intermediate concept of parent.
“Hypo” refers to the intermediate concepts ofchildren.
“Terminal” shows intermediate concepts ofchildren.
“Frame” does not currently signify anything.
([Noun](hierarchy(depth [Number])(hype [Intermediate noun concept])(hypo [Intermediate noun concept])(terminal [Terminal noun concepts]))(frame nil))Fig. 2. The structure of an intermediate noun concept.

3.

Combining Story Units with the Noun
Conceptual Dictionary

Fig. 1. Example for a story unit.

This section presents the results in a previous researchand solutions to the possible issues that can be raised in
response to these results.

2.2. Overview of the noun conceptual dictionary
The INGS has conceptual dictionaries [8]. Conceptual
dictionaries are primarily referenced to generate events
that comprise a story. There are nouns, verbs, andmodifier conceptual dictionaries (adjectives, adjective
verbs, and adverbs). This section describes the noun
conceptual dictionary.
The noun conceptual dictionary contains 5,809
intermediate concepts and 115,769 terminal concepts.
Intermediate concepts indicate the classification ofnoun concepts, and terminal concepts indicate thenouns that appear in the story. The hierarchicalstructure of the intermediate concepts had 13 levels.
The subordinate noun concepts under an individualintermediate noun concept are (1) intermediate noun
concepts only, (2) terminal noun concepts only, or (3)both intermediate and terminal noun concepts.
Furthermore, general and proper nouns have different
hierarchical structures. However, the description
format remains the same.
Fig. 2 shows the structure of an intermediate noun
concept. We combine noun concepts corresponding to“terminal” with the story unit nouns (Section 3). Each
item is described as follows:

3.1. Results from previous research and problems
In the process of creating the story unit shown in Fig. 1,
we divided the folktale events according to the casestructure. Consequently, the event components weredivided into (A) verb components and (B) othercomponents. Thus, based on [21], (A) has beencombined with the verb conceptual dictionary and there
are 3,695 elements in (B) and 766 are nested structuresof events with verbs. Therefore, 766 elements werecombined into a verb conceptual dictionary. The
remaining 2,929 elements in (B), were classified
according to the results of the morphological analyzer
MeCab (used IPA corpus) [23]. Consequently, 1,888
elements are nouns. The remaining 1,041 werecombined for a separate study [24]. They include
modifiers and character utterances.
3.2. Method and results
Among the 1,888 nouns, 1,062 can be combined with thenoun conceptual dictionary. However, 826 cannot be
combined. After describing the merging procedure, wesummarize the details of each case. (1) First, for each of
the 1,888 nouns, the noun concept was compared to that
stored in the noun conceptual dictionary. (2) If thenotation used to register the concept matched the nounin the story unit, it was considered a joinable noun. (3)381

Then, each joinable noun was given an intermediateconcept in the noun conceptual dictionary.
In the above procedure, we combine 1,062 nouns
with the noun conceptual dictionary. Fig. 3 shows amotif combined with noun concept in the noun
conceptual dictionary. In this figure, the description
form of “@X” means that it shows an intermediatenoun concept and the part of “X” indicates a particularintermediate noun concept. Each intermediate noun
concept in the noun conceptual dictionary contains one
or more concrete values. For instance, ”@女” in the
figure has the concrete noun concepts, such as “ 女[woman],” “妻[wife],” and “母[mother].” In this figure,“女@女” corresponds to a concrete “女[woman]” in
the intermediate noun concept, “@ 女 .” Thismechanism enables to generate diverse noun concepts
in story generation according to the above manner.
There are two types of 1,062 nouns: single nouns,
such as “snake” and “bull,” and nouns with modifying
words, such as “delicious dish.” The former could be
examined directly in conjunction with noun concepts.
For the latter, we omitted elements that modified theword and examined noun concept binding. 826 nouns
cannot be combined. There are two reasons for thisfinding: no corresponding noun concepts and inability

to combine proper nouns. In any case, if a corresponding
noun concept did not exist in the noun conceptual
dictionary, we considered adding a new noun concept.
798 nouns have no corresponding noun concepts. The
Japanese language uses several kinds of characters towrite sentences; therefore, there are several ways towrite a word. Noun concepts are not checked using alanguage notation dictionary for multiple notation
methods. Nouns comprising two or more morphemes are
not supported. For example, “radish” and “field” havecorresponding noun concepts in the noun conceptual
dictionary. However, “radish field” has no noun concept.
There are 28 proper nouns. The noun conceptual
dictionary contains general noun and proper noun
concepts. However, there are various proper nouns.
Therefore, the noun conceptual dictionary not store allproper noun concepts that corresponded to them. Proper
nouns that cannot be addressed are names currently
uncommon and unique in the story.
4.

Techniques for Using Story Units in the
INGS

The INGS can attempt to generate a variety of stories
using story units combined with noun concepts. Fig. 4
summarizes the possibilities of story generation: (A) As
a simple approach, the INGS can generate a story
according to the structure of a story unit. (B) The system
can generate different story structures by editing thestory unit. In particular, as described in Section 2.1, the
INGS generates stories by editing story units based on
conceptual dictionaries and by composing story units.
For example, the INGS can generate different stories bychanging noun concepts that appear in the story unit
based on the hierarchical structure of the noun
conceptual dictionary.
Moreover, as shown in Fig. 4 (C), new and longer
stories can be generated by mixing and synthesizing two
or more story units. In particular, the INGS can
synthesize various story structures and story units. The
INGS has knowledge bases for story generation, called“story content knowledge base.” Story content
knowledge represents a story structure of various
granularities. Story content knowledge bases are also
associated with conceptual dictionaries, and story units
can be synthesized with story content knowledge(motif0669 (鬼の面[demon-mask](被る 1[wear] 見る 1[look] 逃げる 1[run-away](or (取る 1[take] 帰る 1[go-home])
なる 1[become])))((1 (event 被る 1[wear] (agent (&sc 女@女[woman]))(object (&sc 鬼の面[demon-mask]))(location(&sc
山 @ 山 { 本体}[mountain]))(2 (event 見る 1[look](agent (&sc 化 け 物 @ 魔 物 ・ 化 け 物[monster]))(counter-agent
(&sc
女 @ 女
[woman]))))(3 (event 逃げる 1[run-away](agent (&sc 化 け 物 @ 魔 物 ・ 化 け 物[monster]))))(or(4a (event 取る 1[take](agent (&sc 女@女[woman]))(object (&sc 宝物@宝物[treasure])))(event 帰る 1[go-home](agent (&sc 女@女[woman]))))(4b (event なる 1[become](agent (&sc 女@女[woman]))(to(&sc 化 け 物 屋 敷 の 主 人 [host-of-monsterhouse])))))))

Fig. 3. Example of a motif combined with noun
concepts.

382

(motif0669 (鬼の面[demon-mask] )((1 (event 被る1[put-on] (agent (&sc 女[woman]))(object (&sc 鬼の面[demon-mask]))(location (&sc 山[mountain]))))(2 (event 見る1[look] (agent (&sc 化け物[monster]))(counter-agent (&sc 女[woman]))))(3 (event 逃げる1[run-away] (agent (&sc 化け物[monster]))))…Omitted… ))(A) Simple generation(B) Story generation byediting story units

女 が 鬼 の 面 を 被 る 。 [A woman
puts on a demon mask.] 化 物が女を見る。[A monster looks
the woman.]化物が逃げる。[Themonster run away.]

母 が 鬼 の 面 を 被 る 。 [A motherputs on a demon mask.] 化 物
が母を見る。[A monster looks
the mother.] 化 物 が 逃 げ る 。
[The monster run away.](C) Story generation
by mixing story units
and using the otherstory structureknowledge.

Fig. 4. Possibilities of story generation using story units combined with noun concepts.

through conceptual dictionaries. Consequently, the
INGS can incorporate the story structure of Japanesefolktales into multiple types of stories.
5.

Conclusion

In this paper, we summarized the issues that arise story
units created from types of folktales are combined with
the noun conceptual dictionary. We then combined
1,062 nouns from 1,888 nouns with the noun
conceptual dictionary. Nouns were combined withnoun conceptual dictionary without considering theirmodifiers. The remaining 826 nouns had nocorresponding concepts. Therefore, new noun concepts
must be registered in the noun conceptual dictionary. In
the future, we will address elements of story units thathave not yet been combined with conceptual
dictionaries.

Acknowledgement
This research was supported by the Japan Society for
the Promotion of Science (JSPS KAKENHI) under
grant no. 21K17870.
References
1. T. Ogata. A Computational, Cognitive, and NarratologicalApproach to Narrative Generation, Post-Narratology
Through Computational and Cognitive Approaches. PA:
IGI Global, 2019, pp. 1-84.
2. J. R. Meehan. Tale-Spin, an Interactive Program thatWrites Stories. Proceedings of the Fifth International Joint
Conference on Artificial Intelligence. 1977, pp. 91–98.

3. L. Pemberton. A Modular Approach to Story Generation.
Proceedings of the Fourth Conference on European Chapterof the Association for Computational Linguistics. 1989, pp.
217-224.
4. R. R. Lang. A Declarative Model for Simple Narratives.
Narrative Intelligence: Papers from the 1999 AAAI Fall
Symposium, Technical Report. FS-99-01, 1999, pp. 134141.
5. S. Bringsjord, D. A. Ferrucci. Artificial Intelligence andLiterary Creativity Inside the Mind of BRUTUS, a
Storytelling Machine. Routledge, London, 1999.
6. A. I. Alhussain, A. M. Azmi. Automatic Story Generation:
A Survey of Approaches. ACM Computing Surveys, 2021,
Volume 54(5), 1-38.
7. H. Osone, J. L. Lu, Y. Ochiai. BunCho: AI Supported Story
Co-Creation via Unsupervised Multitask Learning toIncrease Writers’ Creativity in Japanese. Extended
Abstracts of the 2021 CHI Conference on Human Factors in
Computing Systems. 2021, pp. 1–10.
8. T. Ogata. Building Conceptual Dictionaries for an
Integrated Narrative Generation System. Journal of
Robotics, Networking and Artificial Life, Volume 1(4),2015, 270-284.
9. T. Ogata, J. Ono. Implementing the Story Units of Japanese
Folktales by Using a Verb Conceptual Dictionary. Journalof Robotics, Networking Artificial Life, 2022, Volume 9(2),99-114.
10. T. Ogata. Toward an Integrated Approach to Narrative
Generation: Emerging Research and Opportunities. IGI
Global, Hershey (PA), 2020.
11. T. Ogata. Internal and External Narrative Generation Basedon Post-Narratology: Emerging Research and Opportunities.
IGI Global, Hershey (PA), 2020.
12. T. Ogata. Monogatari Seisei no Post-Narratology: Jinko
Chino no Jidai no Narratology ni Mukete 2 (The PostNarratology of Narrative Generation: Toward the383

Naratology in the Age of Artificial Intelligence 2).
Shin’yosha, Tokyo, 2022.
13. T. Ogata, S. Yamakage. A Computational Mechanism of
the “Distance” Narrative: A Trial in the Expansion of
Literary Theory. Proceedings of the 8th World
Multiconference on Systemics, Cybernetics, andInformatics. 2004, Volume 14, 179–184.
14. T. Ogata. Computational and Cognitive Approaches toNarratology from the Perspective of Narrative Generation.
Computational and Cognitive Approaches to Narratology.
IGI-Global, Hershey (PA), 2016, pp.1-74.
15. G. Genette. Discours du Récit, Essai de Méthode, Figures
III. Seuil, Paris, 1972.
16. S. Imabuchi, T. Ogata. A Story Generation System Basedon Propp Theory: As a Mechanism in an Integrated
Narrative Generation System. Lecture Note of Artificial
Intelligence. Springer, Berlin, 2012, Volume 7614, pp.
312-321.
17. T. Akimoto, T. Ogata. Evaluation of a Narrative Discourse
Generation System Based on the Concept of “Norm and
Deviation”. Journal of Robotics, Networking andArtificial Life, 2015, Volume 2(1), 50-53.
18. V. Propp. Morphology of the Folktale. University ofTexas Press, Austin (TX), 1968.
19. K. Seki, J. Nomura, H. Ōshima (Eds.). Nihon MukashiBanashi Taisei 11: Shiryō-Hen (The Complete Collectionof Japanese Folktales, Vol. 11: Data). Kadokawa Shoten,
Tokyo, 1980.
20. A, Aarne. The Types of the Folktale: A Classification andBibliography (S. Thompson, trans.). FF Communications.
1961, 184.
21. J. Ono, T. Ito, T. Ogata. Mukahi-Banashi no Motif noProgram ka to Motif Kozo no Hikaku (Programs of the
Motives of Folktales and the Comparison Among Motif
Structures). Proceedings of the 61th Special Interest
Group on Language Sense Processing Engineering. 2019,
pp. 51-62.
22. T. Ito, J. Ono, T. Ogata. Using Motifs of Folktales for
Narrative Generation. Proceedings of the 59th Special
Interest Group on Language Sense Processing
Engineering. 2018, pp. 8-11. (SMC2018 Workshop on
Informational and Cultural Narratology and Cognitive
Content Generation.).
23. MeCab. MeCab: Yet Another Part-of-Speech andMorphological
Analyzer.
https://taku910.github.io/mecab/, 2006.
24. M. Kumagai, J. Ono, T. Ogata. Implementation of
Combining Story Units Based on the Types of Japanese
Folktales with a Noun Conceptual Dictionary.
Proceedings of the 68th Special Interest Group on
Language Sense Processing Engineering. 2022, pp. 67-76.

Authors’ Introduction
Dr. Jumpei Ono
Jumepi Ono received his bachelor’s
degree from the Faculty of Softwareand Information Science, Iwate
Prefectural University, in 2010. He
received his MS and PhD from the
Graduate School of Software andInformation
Science,
Iwate
Prefectural University in 2014 and2018, respectively. He worked as an
Information and Communication Technology instructor at
the Vocational School of Digital Arts Sendai from 2018 to2020. He is currently an Assistant Professor at the Facultyof Software and Information Technology, Aomori
University.
Mr. Motoki Kumagai
Motoki Kumagai has been anundergraduate student at the Facultyof Software and Information Science,
Iwate Prefectural University, since
2019.

Dr. Takashi Ogata
Takashi Ogata received his BSS fromWaseda University in 1983, his MS
from Tsukuba University in 1992,
and his PhD from the University ofTokyo in 1995. He has garneredindustrial experience by working insoftware development companies
since 1983. He was an Associate
Professor in the Faculty ofEngineering at Yamanashi University since 1997 and iscurrently a Professor in the Faculty of Software andInformation Science at Iwate Prefectural University, where
he has worked since 2005.

384