Geodesic
Fuzzy non-horn knowledge bases: calculi, models, inference
Zapiski Nauchnykh Seminarov POMI, Representation theory, dynamical systems, combinatorial methods. Part XXXIV, Tome 517 (2022), pp. 176-190

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This paper investigates inference in knowledge bases with fuzzy fuzzy non-Horn facts and rules. Sequent calculi with one structural, one logical rule, and non-logical axioms representing knowledge base rules and facts serve as a proof theory for these knowledge bases. These knowledge bases are also characterized by constrained real-valued models which are applicable to a variety of truth functions. Inference for fuzzy non-Horn knowledge bases is done by applying a variant of ordered resolution, transforming resolution refuations into sequent calculus derivations, building symbolic expressions from the derivations, and evaluating the symbolic expressions. 
@article{ZNSL_2022_517_a10,
     author = {A. Sakharov},
     title = {Fuzzy non-horn knowledge bases: calculi, models, inference},
     journal = {Zapiski Nauchnykh Seminarov POMI},
     pages = {176--190},
     publisher = {mathdoc},
     volume = {517},
     year = {2022},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/ZNSL_2022_517_a10/}
}
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A. Sakharov. Fuzzy non-horn knowledge bases: calculi, models, inference. Zapiski Nauchnykh Seminarov POMI, Representation theory, dynamical systems, combinatorial methods. Part XXXIV, Tome 517 (2022), pp. 176-190. http://geodesic.mathdoc.fr/item/ZNSL_2022_517_a10/