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Association Rules to Help Populating a Never-Ending Growing Knowledge Base

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8864)

Abstract

Large and continuous growing knowledge bases (KBs) have been widely studied in recent years. A major challenge in this field is how to develop techniques to help populating such KBs and improve their coverage. In this context, this work proposes an “association rules”-base approach. We applied an association rule mining algorithm to discover new relations between the instances and categories, to populate a KB. Considering that automatically constructed KBs are often incomplete, we modified traditional support criteria, creating the MSC measure, to deal with missing values. Experiments showed that an association rule mining algorithm, with and without the modified support calculation, brings relevant rules and can play an interesting role in the process of increasing a large growing knowledge base.

Keywords

  • Association rules
  • Missing values
  • Large knowledge base
  • Knowledge base extension
  • Never-ending learning

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Correspondence to Rafael Garcia Leonel Miani .

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Miani, R.G.L., de S. Pedro, S.D., Hruschla, E.R. (2014). Association Rules to Help Populating a Never-Ending Growing Knowledge Base. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-12027-0_14

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