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KC in order to examine our method. As a result, KC demonstrates significant
potentials for study of regional culture, based on spatiotemporal information.
Reviewing similar researches in Cultural Computing, we evaluate our original
method to tackle extracted culture.
As the next step of our research, we have to work on three major chal-
lenges. First, we have to enhance our user dictionary, pattern-phrase dictionary,
and domain-topic repository to handle the wide range of stories. Since these
databases are reusable for researchers interested in similar areas and times,
collaborative development and sharing of such databases could contribute to
enhance a variety of socio-cultural research activities. Secondly, we still have
trouble dealing with ambiguous and fuzzy spatiotemporal information. As GIS
researchers are experiencing similar problems, we should collaborate with each
other to develop techniques or corpus to handle such ill-defined spatiotemporal
information. Finally, we need to apply our method and platform to more cases
for enhancing generalizability of our way on mining and visualizing collective
culture.
Acknowledgments.
The authors would like to thank Prof. Mika Tomita and
her colleagues who provided the narrative data for our research. This research
has been partially supported by Digital Humanities Center for Japanese Arts
and Cultures, Ritsumeikan University.
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