Primal dual based ontology sparse vector learning for similarity measuring and ontology mapping

Shu Gong, Liwei Tian, Muhammad Imran, Wei Gao

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


From the mathematical point of view, the goal of ontology learning is to obtain the dimensionality function f: p → and the p-dimensional vector corresponding to the ontology vertex is mapped into one-dimensional real number. In the background of big data applications, the ontology concept corresponds to the high complexity of information, and thus sparse tricks are used in ontology learning algorithm. Through the ontology sparse vector learning, the ontology function f is obtained via ontology sparse vector β, and then applied to ontology similarity computation and ontology mapping. In this paper, the ontology optimization strategy is obtained by coordinate descent and dual optimization, and the optimal solution is obtained by iterative procedure. Furthermore, the greedy method and active sets are applied in the iterative process. Two experiments are presented where we will apply our algorithm to plant science for ontology similarity measuring and to mathematics ontologies for ontology mapping, respectively. The experimental data show that our primal dual based ontology sparse vector learning algorithm has high efficiency.

Original languageEnglish
Pages (from-to)4525-4531
Number of pages7
JournalJournal of Intelligent and Fuzzy Systems
Issue number4
Publication statusPublished - 2018


  • Ontology
  • iterative algorithm
  • machine learning
  • ontology mapping
  • similarity measure

ASJC Scopus subject areas

  • Statistics and Probability
  • General Engineering
  • Artificial Intelligence


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