Formal ontologies play an increasingly important role in demanding knowledge representation applications like the Semantic Web. Regarding automated reasoning support, the mainstream of research focusses on ontology languages that are also Description Logics, such as OWL-DL. However, many existing ontologies go beyond Description Logics and use full first-order logic. We propose a novel transformation technique that allows to apply existing model computation systems in such situations. We describe the transformation and some variants, its properties and intended applications to ontological reasoning.

The Model Evolution (ME) Calculus is a proper lifting to first-order logic of the DPLL procedure, a backtracking search procedure for propositional satisfiability. Like DPLL, the ME calculus is based on the idea of incrementally building a model of the input formula by alternating constraint propagation steps with non-deterministic decision steps. One of the major conceptual improvements over basic DPLL is lemma learning, a mechanism for generating new formulae that prevent later in the search combinations of decision steps guaranteed to lead to failure. We introduce three lemma generation methods for ME proof procedures, with various degrees of power, effectiveness in reducing search, and computational overhead. Even if formally correct, each of these methods presents complications that do not exist at the propositional level but need to be addressed for learning to be effective in practice for ME. We discuss some of these issues and present initial experimental results on the performance of an implementation within Darwin of the three learning procedures.

Recent years have seen considerable interest in procedures for computing finite models of first-order logic specifications. One of the major paradigms, MACE-style model building, is based on reducing model search to a sequence of propositional satisfiability problems and applying (efficient) SAT solvers to them. A problem with this method is that it does not scale well, as the propositional formulas to be considered may become very large.

We propose instead to reduce model search to a sequence of satisfiability problems made of function-free first-order clause sets, and to apply (efficient) theorem provers capable of deciding such problems. The main appeal of this method is that first-order clause sets grow more slowly than their propositional counterparts, thus allowing for more space efficient reasoning.

In the paper we describe the method in detail and show how it is integrated into one such prover, Darwin, our implementation of the Model Evolution calculus. The results are general, however, as our approach can used in principle with any system that decides the satisfiability of function-free first-order clause sets.

To demonstrate its practical feasibility, we tested our approach on all satisfiable problems from the TPTP library. Our methods can solve a significant subset of these problems, which overlaps but is not included in the subset of problems solvable by state-of-the-art finite model builders such as Paradox and Mace4.

In this paper we introduce several new improvements to the bottom-up model generation (BUMG) paradigm. Our techniques are based on non-trivial transformations of first-order problems into a certain implicational form, namely range-restricted clauses. These refine existing transformations to range-restricted form by extending the domain of interpretation with new Skolem terms in a more careful and deliberate way. Our transformations also extend BUMG with a blocking technique for detecting recurrence in models. Blocking is based on a conceptually rather simple encoding together with standard equality theorem proving and redundancy elimination techniques. This provides a general-purpose method for finding small models. The presented techniques are implemented and have been successfully tested with existing theorem provers on the satisfiable problems from the TPTP library.