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Abstract

In the context of Knowledge Organization (KO) the ontological focus is sometimes overlooked in studies related to the nature of the concept. This study presents an analysis with this purpose, questioning possible modes of existence of concepts (such as mental representations, cognitive abilities or abstract objects), framed in four different readings: a linguistic one, the psychological one, the epistemological one, and the ontological one; and focuses on the two first ones. The suitability of using the concept as an elementary unit of Knowledge Organization Systems (KOS) is analyzed according to the different perspectives. From a mental entity, passing to another one that exists in a non-mental realm, although also non-physical, moving on to another one with an objective linguistic existence.

Type

a

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Abstract

Several instruments of knowledge organization will reflect different possibilities for information retrieval. In this context, ontologies have a different potential because they allow knowledge discovery, which can be used to retrieve information in a more flexible way. However, this potential can be affected by the theoretical principles adopted in ontology building. The aim of this paper is to discuss, in an introductory way, how a (not exhaustive) set of theoretical principles can influence an aspect of ontologies: their use to obtain inferences. In this context, the role of Ingetraut Dahlberg's Theory of Concept is discussed. The methodology is exploratory, qualitative, and from the technical point of view it uses bibliographic research supported by the content analysis method. It also presents a small example of application as a proof of concept. As results, a discussion about the influence of conceptual definition on subsumption inferences is presented, theoretical contributions are suggested that should be used to guide the formation of hierarchical structures on which such inferences are supported, and examples are provided of how the absence of such contributions can lead to erroneous inferences

Type

a

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Abstract

Nos últimos 30 anos, o tema das ontologias tem sido um dos terrenos mais férteis de investigação na área da Organização do Conhecimento. É um tema complexo e revestido de polémica, pela dificuldade na definição do próprio conceito e pelas apropriações que diferentes campos científicos têm exercido sobre ele. Com origem no domínio da filosofia, a ontologia é hoje um território partilhado pelas Ciências da Computação, com destaque para a Ciência dos Dados (Data Science), e pela Ciência da Informação, particularmente pela Organização do Conhecimento. São raros os autores desta área que não escreveram sobre o tema, abordando as suas fronteiras conceptuais ou discutindo a relação das ontologias com outros sistemas de organização do conhecimento, como as taxonomias, os tesauros ou as classificações.

Type

a

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Abstract

Some of the fundamental activities of the software development process are related to the discipline of Requirements Engineering, whose objective is the discovery, analysis, documentation and verification of the requirements that will be part of the system. Requirements are the conditions or capabilities that software must have or perform to meet the users needs. The present study is being developed to propose a model of cooperation between Information Science and Requirements Engineering. Aims to present the analysis results on the possibilities of using the knowledge organization systems: taxonomies, thesauri and ontologies during the activities of Requirements Engineering: design, survey, elaboration, negotiation, specification, validation and requirements management. From the results obtained it was possible to identify in which stage of the Requirements Engineering process, each type of knowledge organization system could be used. We expect that this study put in evidence the need for new researchs and proposals to strengt the exchange between Information Science, as a science that has information as object of study, and the Requirements Engineering which has in the information the raw material to identify the informational needs of software users.

Type

a

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Abstract

Information management in corporate environments is a growing problem as companies' information assets grow and their need to use them in their operations. Several management models have been practiced with application on the most diverse fronts, practices that integrate the so-called Enterprise Content Management. This study proposes a conceptual model of semantic corporate information ecosystem, based on the Universal Document Model proposed by Dagobert Soergel. It focuses on unstructured information objects, especially multimedia, increasingly used in corporate environments, adding semantics and expanding their recovery potential in the composition and reuse of dynamic documents on demand. The proposed model considers stable elements in the organizational environment, such as actors, processes, business metadata and information objects, as well as some basic infrastructures of the corporate information environment. The main objective is to establish a conceptual model that adds semantic intelligence to information assets, leveraging pre-existing infrastructure in organizations, integrating and relating objects to other objects, actors and business processes. The approach methodology considered the state of the art of Information Organization, Representation and Retrieval, Organizational Content Management and Semantic Web technologies, in the scientific literature, as bases for the establishment of an integrative conceptual model. Therefore, the research will be qualitative and exploratory. The predicted steps of the model are: Environment, Data Type and Source Definition, Data Distillation, Metadata Enrichment, and Storage. As a result, in theoretical terms the extended model allows to process heterogeneous and unstructured data according to the established cut-outs and through the processes listed above, allowing value creation in the composition of dynamic information objects, with semantic aggregations to metadata.