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.

We describe a logical calculus for process synthesis applying reaction rules for consuming, transforming and producing products at costs. Unlike common logical calculi, which deal with propositions supported by truth semantics, the process synthesis calculus deals with products (in a concrete or abstract sense) and their costs, and creation of products by the composing of appropriate reaction assemblies from a database of reactions or transactions. As such the calculus may be understood as a resource logic akin to linear logics. The calculus appeals to means-end backwards reasoning using a derivative of definite clause logic. Although rather general in scope, as an interesting case in this paper the calculus is applied to chemical retro-synthesis – in particular with the notoriously difficult Solvay cluster designs.

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.

We argue in favour of adopting a form of natural logic for ontology-structured knowledge bases as an alternative to description logic and rule based languages. Natural logic is a form of logic resembling natural language assertions, unlike description logic. This is essential e.g. in life sciences, where the large and evolving knowledge specifications should be directly accessible to domain experts. Moreover, natural logic comes with intuitive inference rules. The considered version of natural logic leans toward the closed world assumption (CWA) unlike the open world assumption with classical negation in description logic. We embed the natural logic in DATALOG clauses which is to take care of the computational inference in connection with querying

We reconsider the nature and formal properties of the class inclusion relation, IS-A, from the point of view of information modeling and engineering of formal ontologies. In particular we review approaches to the elusive notion of intensionality. We then conduct an analysis adopting a metalogic setup where classes and properties are reified. This approach affords choices along the extensionality/intensionality spectrum. Our analysis concludes that the distinction between epistemic modes for distinguishing definitions, norms, hypotheses, and observational evidence is more important the extensionality/intensionality dichotomy in ontological engineering.

In this paper we discuss models of formal argumentation games. We argue that the tactics and strategies of board-games like chess provide a useful analogy for adversarial argumentation games. The objective of this study is to elaborate on a common model for board games and argumentation dialogues. In particular we strive at making analogies between tactical and strategic chess game notions and notions in adversarial argumentation games.

This paper discusses whether relationships can preferably be reduced to properties in conceptual modelling and formal ontology engineering. The discussion takes as point of departure the subject-predicate form of logic supporting a monadistic view.

In legal settings the admissibility of any speech act could be contested (cf. rule-scepticism, a view of legal positivism [5]). Thus all arguments, including rules, should initially be considered unascertained as to their acceptability and meaning for a given legal dispute. Only after considering the circumstances of the particular dispute, either explicitly or implicitly inferred from the utterances of the parties or lack of such, the acceptability of a rule and its impact are to be determined. Using metalogic, we construct a theory of legally acceptable and meaningful jurisprudence by requiring all rules of a resulting theory at level i to be assessed by a theory of legally acceptable metarules at the adjacent higher level i+1.

We outline an approach to logical analysis and formalization of legal argumentation and dispute as game trees, wellknown in AI, using metalogic programming. The argument/counter-argument dialectic is facilitated through defeasible reasoning, and the applied principles are sought demonstrated by unravelling of a legal case within statutory law.

This paper introduces to the main objectives of the OntoQuery project, with the agenda of developing theories and methodologies for content-based retrieval from text databases. Content-based retrieval is obtained through descriptions of database objects and queries derived from natural language analysis and shaped with respect to a domain specific ontology, which in turn is expressed in a formal description language. Querying is performed by comparing descriptions and, during this, reasoning with the domain knowledge expressed in the ontology. These 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.