MAGES: A Multilingual Angle-integrated Grouping-based Entity Summarization System

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 123
  • Download : 0
This demo presents MAGES (multilingual angle-integrated grouping-based entity summarization), an entity summarization system for a large knowledge base such as DBpedia based on a entity-group-bound ranking in a single integrated entity space across multiple language-specific editions. MAGES offers a multilingual angle-integrated space model, which has the advantage of overcoming missing semantic tags (i.e., categories) caused by biases in different language communities, and can contribute to the creation of entity groups that are well-formed and more stable than the monolingual condition within it. MAGES can help people quickly identify the essential points of the entities when they search or browse a large volume of entity-centric data. Evaluation results on the same experimental data demonstrate that our system produces a better summary compared with other representative DBpedia entity summarization methods.
Publisher
International Committee on Computational Linguistics (ICCL)
Issue Date
2016-12-11
Citation

26th International Conference on Computational Linguistics, COLING 2016, pp.203 - 207

URI
http://hdl.handle.net/10203/276344
Appears in Collection
CS-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0