Monotonic qualitative logic programs: Computation and applications

Rachel Ben-Eliyahu-Zohary, Tal Grinshpoun, Elena Churkin, Ehud Gudes

Research output: Contribution to journalArticlepeer-review


A qualitative logic program (QLP) is a logic program where a dominance is associated with each rule in the program. The intuition is that rules with higher dominance are more plausible or more reliable. Literals in the answer sets of QLPs are also annotated with weights, with the intuition that a literal with a higher weight is more likely to be true. We present three different applications of QLPs: Ontology Matching, Ranking of search results and Inheritance Networks. We also address the problem of computing answer sets of QLPs. We define a property of QLPs called "monotonicity" and show that answer sets of monotonic QLPs can be computed in an 'anytime' fashion, such that the literals are produced by a descending order of their dominance. We then present an application of QLP called LPmatch, which is a tool for ontology matching. LPmatch is a simple matcher composed of only nineteen logic programming rules. The most significant advantage of LPmatch is its flexibility as it can handle new domains for ontology matching instantly just by adding new rules. Comparisons with other existing tools show that LPmatch is a top performer.

Original languageEnglish
Pages (from-to)213-228
Number of pages16
JournalAI Communications
Issue number3
StatePublished - 2014


  • Qualitative logic programs
  • answer set programming
  • inheritance networks
  • logic programs
  • ontology matching
  • stable model semantics


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