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Data modelling is a complex process that depends on the knowledge and experience of the designers who carry it out. The quality of created models has a significant impact on the quality of successive phases of information systems development. This paper, in short, reviews the data modelling process, the entity-relationship method (ERM) and actors in the data modelling process. Further, in more detail it presents systems, methods, and tools for the data modelling process and identifies problems that occur during the development phase of an information system. These problems also represent the authors' motivation for conducting research that aims to develop a knowledge-based system (KBS) in order to support the data modelling process by applying formal language theory (particularly translation) during the process of conceptual modelling. The paper describes the main identified characteristics of the authors' new KB system that are derived from the analysis of existing systems, methods, and tools for the data modelling process. This represents the focus of the research.

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Enterprise Resource Planning (ERP) represents a general business application software solution which is applicable in different business operations. Therefore, it covers areas which are common to a certain group of business operations (e.g. manufacturing, services, financial services, public services, etc.). However, business operations, no matter if they belong to the same or different group, have specific areas which are not supported in general ERP solutions. This paper attempts to reveal which business operations are supported by specialized ERP solutions, how to measure the size of specialized ERP producers, how many business operations are supported by ERP producers on average, and whether there are any classifications of ERP producers based on the number of supported business operations.

The paper describes our current research activities and results related to developing knowledge-based systems to support the creation of entity-relationship (ER) models. The authors based obtaining an ER model in textual form on translation from one language into another, that is, from an English controlled natural language into the formalized language of an ER data model. Our translation method consisted of creating translation rules of sentential form parts into ER model constructs based on the textual and character patterns detected in the business descriptions. To enable the computer analyses necessary for creating translation mechanisms, we created a linguistic corpus that contains lists of the business descriptions and the texts of other business materials. From the corpus, we then created a specific dictionary and linguistic rules to automate the business descriptions' translation into the ER data model language. Before that, however, the corpus was enriched by adding annotations to the words related to ER data model constructs. In this paper, we also present the main issues uncovered during the translation process and offer a possible solution with utility evaluation: applying information-extraction performance measures to a set of sentences from the corpus.

Knowledge representation is one of the areas covered by artificial intelligence. One of the methods for graphical representation of text expressed knowledge is the method NOK (Nodes Of Knowledge). NOK method enables transformation of text expressed knowledge into a graphical network of words and group of words. In this paper application of NOK method is presented. This application is based on sentences from an Aesop's Fable in Croatian ("Golden eggs in the chicken") and English ("The Goose with the golden eggs") version. In this way the applicability of this method on two natural human languages is presented, and similarities and differences that are partially conditioned by freedom of translators, and not only by differences in the syntax of the two languages, are observed.

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