This paper describes principles and structure for a software system that implements a dialect of natural logic for knowledge bases. Natural logics are formal logics that resemble stylized natural language fragments, and whose reasoning rules reflect common-sense reasoning. Natural logics may be seen as forms of extended syllogistic logic. The paper proposes and describes realization of deductive querying functionalities using a previously specified natural logic dialect called Natura-Log. In focus here is the engineering of an inference engine employing as a key feature relational database operations. Thereby the inference steps are subjected to computation in bulk for scaling-up to large knowledge bases. Accordingly, the system eventually is to be realized as a general-purpose database application package with the database being turned logical knowledge base.

This paper describes the principles of a system applying natural logic as a knowledge base language. Natural logics are regimented fragments of natural language employing high level inference rules. We advocate the use of natural logic for knowledge bases dealing with querying of classes in ontologies and class-relationships such as are common in life-science descriptions. The paper adopts a version of natural logic with recursive restrictive clauses such as relative clauses and adnominal prepositional phrases. It includes passive as well as active voice sentences. We outline a prototype for partial translation of natural language into natural logic, featuring further querying and conceptual path finding in natural logic knowledge bases.

This paper makes a case for adopting appropriate forms of natural logic as target language for computational reasoning with descriptive natural language. Natural logics are stylized fragments of natural language where reasoning can be conducted directly by natural reasoning rules reflecting intuitive reasoning in natural language. The approach taken in this paper is to extend natural logic stepwise with a view to covering successively larger parts of natural language. We envisage applications for computational querying and reasoning, in particular within the life-sciences.

A summary is a concise description that reflects the essence of a subject. A text, a collection of text documents, or a query answer can be summarized by simple means such as an automatically generated list of the most frequent words or “advanced” by a meaningful natural language description of the subject. In between these two extremes, conceptual summaries encompass selected concepts derived using background knowledge. We address in this paper an approach where conceptual summaries are provided through a conceptualization as given by an ontology. The ontology guiding the summarization can be a simple taxonomy or a generative domain ontology. A domain ontology can be provided by a preanalysis of a domain corpus and can be used to condense improved summaries that better reflects the conceptualization of a given domain.

In this article, we introduce principles for ontology-based querying of information bases. We consider a framework in which a basis ontology over atomic concepts in combination with a concept language defines a generative ontology. Concepts are assumed to be the basis for an index of the information base, in the sense that these concepts are attached to objects in the information base. Concepts are thus applied to obtain a means for descriptions that generalize classical word-based information base indexing. We discuss how the ontology influences the matching of values, especially how the different relations of the ontology may contribute to overall similarity between concepts. Further, we discuss a set of major properties to improve a given similarity measure's accordance with the semantics of the ontology, and use these properties to guide the choice of function. Finally we implement a prototype search system to evaluate the chosen approach. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 739–761, 2007.