Reasoning Support for Expressive Ontology Languages Using a Theorem Prover

Automated theorem provers for first-order logic usually operate on sets of first-order clauses. It is well-known that the translation of a formula in full first-order logic to a clausal normal form (CNF) can crucially affect performance of a theorem prover. In our recent work we introduced a modification of first-order logic extended by the first class boolean sort and syntactical constructs that mirror features of programming languages. We called this logic FOOL. Formulas in FOOL can be translated to ordinary first-order formulas and checked by first-order theorem provers. While this translation is straightforward, it does not result in a CNF that can be efficiently handled by state-of-the-art theorem provers which use superposition calculus. In this paper we present a new CNF translation algorithm for FOOL that is friendly and efficient for superposition-based first-order provers. We implemented the algorithm in the Vampire theorem prover and evaluated it on a large number of problems coming from formalisation of mathematics and program analysis. Our experimental results show an increase of performance of the prover with our CNF translation compared to the naive translation.

Scalability of reasoning systems is one of the main criteria which will determine the success of Semantic Web systems in the future. The focus of recent work is either on (a) systems which rely on in-memory structures or (b) not so expressive ontology languages, which can be dealt with by using database technologies.In this paper we introduce a method to perform query answering for semi-expressive ontologies without the limit of in-memory structures. Our main idea is to compute small and characteristic representations of the assertional part of the input ontology. Query answering is then more efficiently performed over a reduced set of these small represenations. We show that query answering can be distributed in a network of description logic reasoning systems to scale for reasoning. Our initial results are encouraging.

We introduce a heuristic for automatically checking the validity of first-order formulas of the form forallalpham existbetan middot Psi(alpham,betan) that are encountered in inductive proofs of hardware correctness. The heuristic introduced in this paper is used to automatically check the validity of k-step induction formulas needed to verify hardware designs. The heuristic works on word-level designs that can have data and address buses of arbitrary widths. Our refinement heuristic relies on the idea of predicate instantiation introduced in (H. Abu-Haimed et al., 2003). The heuristic proves quantified formulas by the use of a validity checker, CVC (A. Stump et al., 2002), and a first-order theorem prover, Otter (W.W. McCune, 1994). Our heuristic can be used as a stand-alone technique to verify word-level designs or as a component in an interactive theorem prover. We show the effectiveness of this heuristic for hardware verification by verifying a number of hardware designs completely automatically. The large size of the quantified formulas encountered in these examples shows the effectiveness of our heuristic as a component of a theorem prover

This paper introduces a new technique for reasoning with quantifiers and theories. Traditionally, first-order theorem provers (ATPs) are well suited to reasoning with first-order problems containing many quantifiers and satisfiability modulo theories (SMT) solvers are well suited to reasoning with first-order problems in ground theories such as arithmetic. A recent development in first-order theorem proving has been the AVATAR architecture which uses a SAT solver to guide proof search based on a propositional abstraction of the first-order clause space. The approach turns a single proof search into a sequence of proof searches on (much) smaller sub-problems. This work extends the AVATAR approach to use a SMT solver in place of the SAT solver, with the effect that the first-order solver only needs to consider ground-theory-consistent sub-problems. The new architecture has been implemented using the Vampire theorem prover and Z3 SMT solver. Our experimental results, and the results of recent competitions, show that such a combination can be highly effective.

Although there is an increasing amount of experimental research on learning concepts expressed in first-order logic, there are still relatively few formal results on the polynomial learnability of first-order representations from examples. Most previous analyses in the pac-model have focused on subsets of Prolog, and only a few highly restricted subsets have been shown to be learnable. In this paper, we will study instead the learnability of the restricted first-order logics known as “description logics”, also sometimes called “terminological logics” or “KL-ONE-type languages”. Description logics are also subsets of predicate calculus, but are expressed using a different syntax, allowing a different set of syntactic restrictions to be explored. We first define a simple description logic, summarize some results on its expressive power, and then analyze its learnability. It is shown that the full logic cannot be tractably learned. However, syntactic restrictions exist that enable tractable learning from positive examples alone, independent of the size of the vocabulary used to describe examples. The learnable sublanguage appears to be incomparable in expressive power to any subset of first-order logic previously known to be learnable.

Higher-order aspects and context in SUMO

Encoding TLA+ into unsorted and many-sorted first-order logic

Description logics provide powerful languages for representing and reasoning about knowledge of static application domains. The main strength of description logics is that they offer considerable expressive power going far beyond propositional logic, while reasoning is still decidable. There is a demand to bring the power and character of description logics into the description and reasoning of dynamic application domains which are characterized by actions. In this paper, based on a combination of the propositional dynamic logic PDL, a family of description logics and an action formalism constructed over description logics, we propose a family of dynamic description logics DDL(X @) for representing and reasoning about actions, where X represents well-studied description logics ranging from the to the , and X @ denotes the extension of X with the @ constructor. The representation power of DDL(X @) is reflected in four aspects. Firstly, the static knowledge of application domains is represented as RBoxes and acyclic TBoxes of the description logic X. Secondly, the states of the world and the pre-conditions of atomic actions are described by ABox assertions of the description logic X @, and the post-conditions of atomic actions are described by primitive literals of X @. Thirdly, starting with atomic actions and ABox assertions of X @, complex actions are constructed with regular program constructors of PDL, so that various control structures on actions such as the "Sequence", "Choice", "Any-Order", "Iterate", "If-Then-Else", "Repeat-While" and "Repeat-Until" can be represented. Finally, both atomic actions and complex actions are used as modal operators for the construction of formulas, so that many properties on actions can be explicitly stated by formulas. A tableau-algorithm is provided for deciding the satisfiability of DDL(X @)-formulas; based on this algorithm, reasoning tasks such as the realizability, executability and projection of actions can be effectively carried out. As a result, DDL(X @) not only offers considerable expressive power going beyond many action formalisms which are propositional, but also provides decidable reasoning services for actions described by it.

Though many kinds of multi-agent systems based on argumentation have been proposed where only rule-based knowledge is taken into account, they have been unable to handle the ontological knowledge so far. In our daily life, however, there are a lot of human argumentation where both ontological and rule knowledges are used. For example, in e-commerce, a seller and a buyer usually use ontologies about products along with their respective strategic rules for buying and selling. Recent progress of the Semantic Web technology provides expressive ontology languages. In this paper, we demonstrate integration of the Semantic Web reasoning and argument-based reasoning. We have implemented the integrated system such that Logic of Multiple-valued Argumentation-based agent system (specialized to two values {f, t }) can be accessible to the Semantic Web reasoning established as the description logic reasoning system, given ontologies expressed by OWL DL or its notational variant the DL SHOIN(D). An interesting argumentation result using both ontologies and rules about the university curriculum is shown as an example executed by our system.

The Replacement Rule Theorem Prover (RRTP) is an instance-based, refutational, first-order clausal theorem prover. The prover is motivated by the idea of selectively replacing predicates by their definitions, and operates by selecting relevant instances of the input clauses. The relevant instances are grounded, if necessary, and tested for unsatisfiability by using a fast propositional calculus decision procedure.

Efficient proof techniques are vital for the success of formal verification of hardware. First-Order Logic theorem provers satisfy this requirement. This paper deals with experiences in applying such a tool, OTTER, to the verification of correctness of combinational logic. Several proof methodologies are here discussed: a rewrite rules and a resolution based approach are compared.

While separation logic is acknowledged as an enabling technology for large-scale program verification, most of the existing verification tools use only a fragment of separation logic that excludes separating implication. As the first step towards a verification tool using full separation logic, we develop a nested sequent calculus for Boolean BI (Bunched Implications), the underlying theory of separation logic, as well as a theorem prover based on it. A salient feature of our nested sequent calculus is that its sequent may have not only smaller child sequents but also multiple parent sequents, thus producing a graph structure of sequents instead of a tree structure. Our theorem prover is based on backward search in a refinement of the nested sequent calculus in which weakening and contraction are built into all the inference rules. We explain the details of designing our theorem prover and provide empirical evidence of its practicality.

Generally, part-whole relations are modeled using fragments of first-order logic(FOL) and difficulties arise when meta-reasoning is done over their properties, leading to reason outside the logic. Alternatively, classical languages for ontological reasoning such as Description Logics + Logic Programming lack of expressive formal foundations resulting in ambiguous interpretations of relations. Moreover, they show some difficulties to prove that a given meta property is logically correct. In order to address these problems, we suggest a formal framework using a dependent (higher-order) type theory such as those used in program checking and theorem provers (e.g., Coq). All properties of part-whole relations are formalized through abstract constructs called parameterized specifications (p-specifications). We detail their content and explain how they are suitable to build an ontology of formal properties that can be further used for reasoning about higher-order properties.

Incorporating Generalized Quantifiers into Description Logic to Improve Source Selection

A key issue in semantic reasoning is the computational complexity of inference tasks on expressive ontology languages such as OWL DL and OWL 2 DL. Theoretical works have established worst-case complexity results for reasoning tasks for these languages. However, hardness of reasoning about individual ontologies has not been adequately characterised. In this paper, we conduct a systematic study to tackle this problem using machine learning techniques, covering over 350 real-world ontologies and four state-of-the-art, widely-used OWL 2 reasoners. Our main contributions are two-fold. Firstly, we learn various classifiers that accurately predict classification time for an ontology based on its metric values. Secondly, we identify a number of metrics that can be used to effectively predict reasoning performance. Our prediction models have been shown to be highly effective, achieving an accuracy of over 80%.KeywordsDescription LogicReasoning PerformanceReasoning TaskFeature SelectorOntology EngineeringThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.