Pesquisa de Doutorado da Veronica

 A Comprehensive Approach to Assess Trustworthiness and Completeness of Knowledge Graphs  International Journal of Knowledge Engineering, Vol. 10, No. 1, 2024    Abstract Completeness and trustworthiness are two dimensions that are used to assess the quality of KGs. Estimation of the completeness and trustworthiness of a largescale knowledge graph often requires humans to annotate samples from the graph. Estimativa de métricas de qualidade que dependem de interferência humana Nowadays, to reduce the costs of the manual construction of knowledge graphs, many KGs have been constructed automatically from sources with varying degrees of trustworthiness. Therefore, possible noises and conflicts are inevitably introduced in the process of construction, which severely interferes with the quality of constructed KGs.  O processo de construção automática para aumentar a completude pode degradar a confiabilidade. Introduz "fatos" incorretos ou conflitantes. we propose a new approach to

Vídeo https://youtu.be/jq9o4mYEFLo?si=6WCtMd8TbFUWziTq Fluxo https://drive.google.com/file/d/1t0EH2nGZNZX6cYBqktmuvrGl2iDpkJpY/view  Curadoria das Fontes: selecionar fontes confiáveis Técnicas de integração de dados Step 1: Data, Content, Source Audit (Estruturadas ou não estruturadas) Step 2: Source Tier Logic Step 3: Statement Similarity Assesment Step 4: Identification of duplicates, clusters or similarity statements Step 5: Weighting Assessment Step 6: Temporal data Flow Step 7: Final score calculation Step 8: Statement router Step 9: Human Verification Step 10: Adding verification tags and verified statements to the graph hey everybody so today we are going to be going over how do you do statement validation for your knowledge graph there's a lot of reasons that you might want to do this if you are one of the folks that thinks that you know because you're sourcing data for your knowledge graph from internal sources or licensed sources licensed data sets it should all be co

 https://nesslabs.com/decision-making We make thousands of choices everyday, often automatically, using mental models we have created over years of experience. Modelos mentais: estão modelados na ROT Decision-making is the process we use to identify and choose alternatives, producing a final choice, which may or may not result in an action. It can be more or less rational based on the decision maker’s values, beliefs, and (perceived) knowledge. Decidir não agir é uma decisão também.  What’s interesting is that these areas are not only involved in the formation of a decision, but also signal the degree of confidence associated with the decision. Grau de Confiança e Grau de Risco das decisões In fact, poor decision-making rarely has to do with a lack of intelligence or information. Instead, we it’s often due to the ‘mental baggage’ we bring to the table when considering our options. Pobres em que sentido? Analysis paralysis. You may know this one under the term “overthinking.”