0019/2009 - A Survey on Conceptual Modeling

Conceptual modeling remains a relevant research topic, even though more than thirty years have passed since Peter Chen enunciated his Entity-Relationship Model. Methods and methodologies for the creation of conceptual models have been the subject of studies and projects which goal is to produce clearer, complete and easier-to-read models. Several methods, modeling languages and tools, have been proposed over the years, some of which aim at creating and/or reading such models automatically, which can imply a simplification that might oppose the idea of semantic accuracy and completeness. The common denominator among all proposals is that, for a conceptual model to be effective and useful, a designer must learn the language used in the Universe of Discourse to be modeled, along with its underlying concepts, and then represent such concepts in a modeling language. Also, no matter the source of information, the knowledge about the scenario to be modeled is always passed to the designer in a natural language. For the resulting model to be both detailed and unambiguous, the modeling language must convey the semantics of such environment, in a way that anyone who is literate in this language can, from reading the model, get the same understanding as from the description in a natural language. In other words, the modeling language must be as rich and generative as the natural language in which the Universe of Discourse concepts are described. Several projects that focus on conceptual modeling have turned to linguistics as a support for the modeling process itself, relating natural language constructs to those of the adopted modeling language; what they all have in common is that their work is done from the perspective of the (meta)model itself. This report presents some of the aforementioned studies and proposes that the linguistic approach, invaluable as it is, should actually be applied to the modeling process from the natural language perspective

Semi-automatic conceptual data modeling using entity and relationship instance repositories

Conceptual modeling is the foundation of analysis and design methodologies for the development of information systems. It is challenging because it requires a clear understanding of an application domain and an ability to translate the requirement specifications into a data model. However, novice designers frequently lack experience and have incomplete knowledge about the application being designed. We propose new types of reusable artifacts called Entity Instance Repository (EIR) and Relationship Instance Repository (RIR), which contain ER (Entity-Relationship) modeling patterns from prior designs and serve as knowledge-based repositories for conceptual modeling. We also select six data modeling rules to check whether they are comprehensive enough in creating quality conceptual models. This research aims to develop effective knowledge-based systems (KBSs) with EIR and RIR. Our proposed artifacts are likely to be useful for conceptual designs in the following aspects: (1) they contain knowledge about a domain; (2) automatic generation of EIR and RIR overcomes a major problem ofinefficient manual approaches that depend on experienced modeling designers and domain experts; and (3) they are domain-specific and therefore easier to understand and reuse. Two KBSs were developed in this study: Heuristic-Based Technique (HBT) and Entity Instance Pattern WordNet (EIPW). The goals of this study are (1) to find effective approaches that can improve the novice designers’ performance in developing conceptual models by integrating pattern-based technique and various modeling techniques, (2) to evaluate whether those selected six modeling rules are effective in HBT, and (3) to validate whether the proposed KBSs are effective in creating quality conceptual models. To assess the potential of the KBSs to benefit practice, empirical testing was conductedon tasks of different sizes. The empirical results indicate that novice designers’ overall performance increased by 30.9~46.0 % when using EIPW, and increased by 33.5~34.9% when using HBT, compared with the cases of no tools.Ph.D., Information Studies -- Drexel University, 201

Semi-Automated Development of Conceptual Models from Natural Language Text

The process of converting natural language specifications into conceptual models requires detailed analysis of natural language text, and designers frequently make mistakes when undertaking this transformation manually. Although many approaches have been used to help designers translate natural language text into conceptual models, each approach has its limitations. One of the main limitations is the lack of a domain-independent ontology that can be used as a repository for entities and relationships, thus guiding the transition from natural language processing into a conceptual model. Such an ontology is not currently available because it would be very difficult and time consuming to produce. In this thesis, a semi-automated system for mapping natural language text into conceptual models is proposed. The model, which is called SACMES, combines a linguistic approach with an ontological approach and human intervention to achieve the task. The model learns from the natural language specifications that it processes, and stores the information that is learnt in a conceptual model ontology and a user history knowledge database. It then uses the stored information to improve performance and reduce the need for human intervention. The evaluation conducted on SACMES demonstrates that (1) designers’ creation of conceptual models is improved when using the system comparing with not using any system, and that (2) the performance of the system is improved by processing more natural language requirements, and thus, the need for human intervention has decreased. However, these advantages may be improved further through development of the learning and retrieval techniques used by the system