In this paper we study the problem of obtaining meaningful answers to queries posed over inconsistent DL-Lite ontologies. We consider different variants of inconsistency-tolerant semantics and show that for some of such variants answering unions of conjunctive queries (UCQs) is first-order (FOL) rewritable, i.e., it can be reduced to standard evaluation of a FOL/SQL query over a database. Since...

Semantic Web (SW), commonly regarded as the next generation of the Web, is an emerging vision of the new Web from the Knowledge Representation and the Web communities. To realize this vision, a series of techniques has been proposed. Semantic Web Ontology Langauge (OWL) and its extension Semantic Web rule Language (SWRL) and Semantic Web Logic Language (SWRL-FOL) are some of the most important ...

We describe a novel fragment library termed fragments of life (FOL) for structure-based drug discovery. The FOL library includes natural small molecules of life, derivatives thereof, and biaryl protein architecture mimetics. The choice of fragments facilitates the interrogation of protein active sites, allosteric binding sites, and protein-protein interaction surfaces for fragment binding. We s...

Abstract We present an Isabelle/HOL formalization of Simple Clause Learning for first-order logic without equality: SCL(FOL). The main results are formal proofs soundness, non-redundancy learned clauses, termination, and refutational completeness. Compared to the unformalized version, formalized calculus is simpler more general, some such as stronger non-subsumption new. found one bug in a prev...

There are still very few systems performing a Similarity Based Learning and using a First Order Logic (FOL) representation. This limitation comes from the intrinsic complexity of the learning processes in FOL and from the difficulty to deal with numerical knowledge in this representation. In this paper, we show that major learning processes, namely generalization and clustering, can be solved i...

In this paper, we present the FOLtoNL system, which converts first order logic (FOL) sentences into natural language (NL) ones. The motivation comes from an intelligent tutoring system teaching logic as a knowledge representation language, where we use it as a means for feedback to the users. FOL to NL conversion is achieved by using a rule-based approach, where we exploit the pattern matching ...

A number of Inductive Logic Programming (ILP) systems have addressed the problem of learning First Order Logic (FOL) discriminant definitions by first reformulating the FOL learning problem into an attribute-value one and then applying efficient learning techniques dedicated to this simpler formalism. The complexity of such propositionalisation methods is now in the size of the reformulated pro...

There are still very few systems performing a Similarity Based Learning and using a First Order Logic (FOL) representation. This limitation comes from the intrinsic complexity of the learning processes in FOL and from the difficulty to deal with numerical knowledge in this representation. In this paper, we show that major learning processes, namely generalization and clustering, can be solved i...