Specifications of conceptualisations (ontologies) are often employed for representing reality, both in knowledge representation and software engineering. While languages offer sophisticated constructs and rigorous semantics for building conceptual entities, no attention is paid to the relationship between such entities and the world they intend to represent. This paper studies such a relationship and provides empirical evidences in favour of two main hypotheses: (1) conceptualisations are insufficient to fully represent the specifics of reality; (2) languages (both representation and design-oriented) are general representations of (classes of) systems in the world, and they can be characterised as scientific theories. The first hypothesis establishes a problem for which we propose a solution based on the explicit elaboration of statements claiming the similarity (in some respects and to certain degrees of accuracy) between conceptual entities and real-world systems of interest. The second hypothesis constitutes a new perspective for understanding languages, whose advantages to representation and design are discussed in detail.

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Abstract. We present and validate a theoretical model of methodological works in Software Engineering that, without claiming for completeness, allows us to investigate the role of ontologies in the problem solving process related with the development of software. Our main conclusion is the potential of ontologies as resources for an individual to think during problem solving. We argument that suitable ontologies can support solving strategies as well as motivate their invention. We also conclude the importance of accompany an ontology with knowledge that guides the engineer in reasoning with its concepts.The model regards a methodological work as an heterogeneous theory about a class of problems and about a number of conceptual elements. Some of the elements are ontologies, which play the role of identifying and relating aspects of the knowledge about the class of problems, making up novel perspectives on the problems that may promote solving strategies.For illustration purposes, we take Jackson's "Problem Frames" as a case study. We analyse this work through the former model, identifying the ontologies, guides, and promoted strategies. Then we propose an alternative ontology, based on that used in the KAOS approach; we reformulate some parts of Jackson's work through this ontology and propose a strategy as well as some guides.

This paper presents the ideas, experiments and specifications related to the Supervised TextRank (STR) technique, a word tagging method based on the TextRank algorithm. The main innovation of STR technique is the use of a graph-based ranking algorithm similar to PageRank in a supervised fashion, gathering the information needed to build the graph representations of the text from a tagged corpus. We also propose a flexible graph specification language that allows to easily experiment with multiple configurations for the topology of the graph and for the information associated to the nodes and the edges. We have carried experiments in the Part-Of-Speech task, a common tagging problem in Natural Language Processing. In our best result we have achieved a precision of 96.16%, at the same level of the best tagging tools.

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