2002
DOI: 10.1016/s0304-3975(01)00188-8
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Approximate reasoning by similarity-based SLD resolution

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Cited by 105 publications
(97 citation statements)
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References 32 publications
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“…Nevertheless, for the first case, we can find in [17] some (theoretical) analysis establishing nice correspondences between both languages. In particular, it can be proved that the effects of the similarity-based unification/resolution methods of [33] can be somehow replicated (at a theoretical level) by applying the procedural mechanism seen here on multi-adjoint logic programs augmented with special weighted rules which simulate similarity equations. On the other hand, for the annotated logic programs of [34] and specially, for the more recent (and much easier in its formulation) version of [35], we need to make a more detained analysis.…”
Section: Final Discussionmentioning
confidence: 96%
See 1 more Smart Citation
“…Nevertheless, for the first case, we can find in [17] some (theoretical) analysis establishing nice correspondences between both languages. In particular, it can be proved that the effects of the similarity-based unification/resolution methods of [33] can be somehow replicated (at a theoretical level) by applying the procedural mechanism seen here on multi-adjoint logic programs augmented with special weighted rules which simulate similarity equations. On the other hand, for the annotated logic programs of [34] and specially, for the more recent (and much easier in its formulation) version of [35], we need to make a more detained analysis.…”
Section: Final Discussionmentioning
confidence: 96%
“…Consequently, there exist some cases not fully covered by the multi-adjoint logic approach. These are, for instance, the cases of similarity-based [33] and annotated [34] logic programming. Nevertheless, for the first case, we can find in [17] some (theoretical) analysis establishing nice correspondences between both languages.…”
Section: Final Discussionmentioning
confidence: 99%
“…It is important to note that this last property is not required when considering proximity relations. In order to simplify our developments, as in [9], we assume that x ∧ y is the minimum between the two elements x, y ∈ [0, 1].…”
Section: Similarity Relations and Fuzzy Logic Programmingmentioning
confidence: 99%
“…Most of these languages implement (extended versions of) the resolution principle introduced by Lee [4], such as Elf-Prolog [5], Fril [6], F-Prolog [7] and MALP [8]. There exists also a family of fuzzy languages based on sophisticated unification methods [9] to cope with similarity/proximity relations, as occurs with LIKELOG [10], SQLP [11] and BOUSI∼PROLOG [12], [13] (some related approaches based on probabilistic logic programming can be found in [14], [15]). …”
Section: Introductionmentioning
confidence: 99%
“…In this paper we ignore this important issue of similarity and ranking, and possible extensions, which should be incorporated in a fullfledged Fuzzy RuleML framework in particular to represent and reason with fuzzy data. Some proposals already support these notions, like the ones described in [2,28,5]. More interesting for our objectives is the element <degree>, a child of element <Atom> and <Equal> in RuleML 0.9.…”
Section: A General Uncertainty Extensionmentioning
confidence: 99%