Knowledge discovery from RDF data stored in NoSQL databases

Currently, the existence of large amounts of data suggests the use of tools capable of processing them and facilitate the process of finding new knowledge. The discovery of new facts that were not previously explicit in data can be crucial to decision-making processes. In this article, we present a survey on Semantic Web standards, stores of RDF data (Resource Description Framework) and inference mechanisms available in RDF stores. The main goal is to report how inference can be applied and derive new facts from existing data. For this purpose, we demonstrate inferences obtained from a set of predefined rules over data about scientific publications stored in a NoSQL database designated of MarkLogic.Atualmente, a existência de grandes volumes de dados sugere a utilização de ferramentas capazes de os processar e de facilitar o processo de descoberta de novo conhecimento. A descoberta de novos factos, que não estavam explícitos anteriormente, pode ser crucial para os processos de tomada de decisão. Nesse contexto, este artigo apresenta uma revisão da literatura relevante relativa a normas da Web Semântica, repositórios de dados RDF (Resource Description Framework) e mecanismos de inferência disponíveis em repositórios RDF. O objetivo principal é o de relatar como é que a inferência pode ser aplicada e derivar novos factos a partir dos dados já existentes. Para esse efeito, são apresentadas inferências obtidas a partir de um conjunto de regras pré-definidas sobre dados de publicações científicas armazenados numa base de dados NoSQL designada de MarkLogicFCT - Fundação para a Ciência e a Tecnologia(PTDC/COM-INF/28284/2017 e UID/CEC/00319/2019

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

Bias in Artificial Intelligence (AI) is a critical and timely issue due to its sociological, economic and legal impact, as decisions made by biased algorithms could lead to unfair treatment of specific individuals or groups. Multiple surveys have emerged to provide a multidisciplinary view of bias or to review bias in specific areas such as social sciences, business research, criminal justice, or data mining. Given the ability of Semantic Web (SW) technologies to support multiple AI systems, we review the extent to which semantics can be a “tool” to address bias in different algorithmic scenarios. We provide an in-depth categorisation and analysis of bias assessment, representation, and mitigation approaches that use SW technologies. We discuss their potential in dealing with issues such as representing disparities of specific demographics or reducing data drifts, sparsity, and missing values. We find research works on AI bias that apply semantics mainly in information retrieval, recommendation and natural language processing applications and argue through multiple use cases that semantics can help deal with technical, sociological, and psychological challenges