The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges

Conceptual Modeling: the Linguistic Approach

After more than thirty years of its first introduction, conceptual modeling remains an important research field, which has been recently addressed by the literature on semantic interoperability in its various forms (model integration, service interoperability, knowledge harmonization, taxonomy alignment), domain engineering and the creation of conceptual models through Natural Language Processing (NLP), to name a few. In the database conceptual design, the designer must learn the language used in the Universe of Discourse (UoD) to be modeled, along with its underlying concepts, and then represent such concepts in a modeling language. Thus, the conceptual modeling process can be seen as a translation. For the resulting model to be both detailed and unambiguous, the designer must represent the UoD in a generative language which constructs can convey the same concepts represented in the respective natural language. For the whole process to be effective, we argue that the adoption of modeling languages and methodologies that are based on well-founded ontological theories is required. We propose the use of a linguistic approach for conceptual modeling from natural language texts, and illustrate how it may be applied using the well-founded modeling language OntoUML

Generating natural language specifications from UML class diagrams

Early phases of software development are known to be problematic, difficult to manage and errors occurring during these phases are expensive to correct. Many systems have been developed to aid the transition from informal Natural Language requirements to semistructured or formal specifications. Furthermore, consistency checking is seen by many software engineers as the solution to reduce the number of errors occurring during the software development life cycle and allow early verification and validation of software systems. However, this is confined to the models developed during analysis and design and fails to include the early Natural Language requirements. This excludes proper user involvement and creates a gap between the original requirements and the updated and modified models and implementations of the system. To improve this process, we propose a system that generates Natural Language specifications from UML class diagrams. We first investigate the variation of the input language used in naming the components of a class diagram based on the study of a large number of examples from the literature and then develop rules for removing ambiguities in the subset of Natural Language used within UML. We use WordNet,a linguistic ontology, to disambiguate the lexical structures of the UML string names and generate semantically sound sentences. Our system is developed in Java and is tested on an independent though academic case study

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