SOCIA: Linked Open Data of Context behind Local Concerns for Supporting Public Participation

To address public concerns that threat the sustain-ability of local societies, supporting public participation by shar-ing the background context behind these concerns is essentially important. We designed a SOCIA ontology, which was a linked data model, for sharing context behind local concerns with two approaches: (1) structuring Web news articles and microblogs about local concerns on the basis of geographical regions and events that were referred to by content, and (2) structuring public issues and their solutions as public goals. We moreover built a SOCIA dataset, which was a linked open dataset, on the basis of the SOCIA ontology. Web news articles and microblogs related to local concerns were semi-automatically gathered and structured. Public issues and goals were manually extracted from Web content related to revitalization from the Great East Japan Earthquake. Towards more accurate extraction of public concerns, we investigated feature expressions for extracting public concerns from microblogs written in Japanese. To address a technical issue about sample selection bias in our microblog corpus, we formulated a metric in mining feature expressions, i.e., bias-penalized information gain (BPIG). Furthermore, we developed a prototype of a public debate support system that utilized the SOCIA dataset and formulated the similarity between public goals for a goal matching service to facilitate collaboration.

@article{Shiramatsu2015,
  title     = {SOCIA: Linked Open Data of Context behind Local Concerns for Supporting Public Participation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {2},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Shun Shiramatsu and Tadachika Ozono and Toramatsu Shintani},
  doi       = {10.14569/IJACSA.2015.060238},
  url       = {https://doi.org/10.14569/IJACSA.2015.060238}
}