A Generalized QSQR Evaluation Method for Horn Knowledge Bases

  • Authors:
  • Ewa Madalińska-Bugaj;Linh Anh Nguyen

  • Affiliations:
  • University of Warsaw;University of Warsaw

  • Venue:
  • ACM Transactions on Computational Logic (TOCL)
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

We generalize the QSQR evaluation method to give the first set-oriented depth-first evaluation method for Horn knowledge bases. The resulting procedure closely simulates SLD-resolution (to take advantages of the goal-directed approach) and highly exploits set-at-a-time tabling. Our generalized QSQR evaluation procedure is sound and complete. It does not use adornments and annotations. To deal with function symbols, our procedure uses iterative deepening search, which iteratively increases term-depth bound for atoms and substitutions occurring in the computation. When the term-depth bound is fixed, our evaluation procedure runs in polynomial time in the size of extensional relations.