Reasoning with inconsistency involves some compromise on classical logic. There is a range of proposals for logics (called paraconsistent logics) for reasoning with inconsistency each with pros and cons. Selecting an appropriate paraconsistent logic for an application depends upon the requirements of the application. Here we review paraconsistent logics for the potentially significant application area of technology for structured text. Structured text is a general concept that is implicit in a variety of approaches to handling information. Syntactically, an item of structured text is a number of grammatically simple phrases together with a semantic label for each phrase. Items of structured text may be nested within larger items of structured text. The semantic labels in a structured text are meant to parameterize a stereotypical situation, and so a particular item of structured text is an instance of that stereotypical situation. Much information is potentially available as structured text, including tagged text in XML, text in relational and object-oriented databases, and the output from information extraction systems in the form of instantiated templates. In this review paper, we formalize the concept of structured text, and then focus on how we can identify inconsistency in items of structured text, and reason with these inconsistencies. Then we review key approaches to paraconsistent reasoning, and discuss the application of them to reasoning with inconsistency in structured text.

This paper surveys the most representative approaches of knowledge-base revision. After a description of the revision characterization according to the AGM paradigm, the paper reviews different revision methods. In each case, the same example is used, as a reference example, to illustrate the different approaches. Closely connected with revision, some other non-monotonic approaches, like update, are briefly presented.

The retrieval of relevant cases plays a crucial role in case-based reasoning. There are three major methods for the retrieval of relevant cases: computational approaches (based upon measures of similarity), representational approaches (based upon indexing structures) and hybrid approaches. This paper looks at recent successful implementations of case retrieval with regard to this classification framework. Similarly it emphasises computational and representational models applied to feature-vector case representations.

In this study we present a review of the emerging field of meta-knowledge components as practised over the past decade among a variety of practitioners. We use the artificially defined term “meta-knowledge” to encompass all those different but overlapping notions used by the artificial intelligence and software engineering communities to represent reusable modelling frameworks: ontologies, problem-solving methods, patterns and experience factories and bases, to name but a few. We then elaborate on how meta-knowledge is deployed in the context of system's design to improve its reliability by consistency-checking, enhance its reuse potential and manage its knowledge-sharing. We speculate on its usefulness and explore technologies for supporting deployment of meta-knowledge. We argue that, despite the different approaches being followed in systems design by divergent communities, meta-knowledge is present in all cases, in a tacit or explicit form, and its utilisation depends on pragmatic aspects which we try to identify and critically review on criteria of effectiveness.

To an ever-growing degree, information technology is being based on interactive visual media, and visualisation techniques are emerging as the primary support to decision-making tasks. In this paper I briefly survey the most recent results in the field of advanced visual interfaces, focusing on visualisation techniques along with their design and their applications.