A probabilistic algorithm to predict missing facts from knowledge graphs

• Ano de defesa: 2019
• Autor(a) principal: André Lopes Gonzaga
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO; Programa de Pós-Graduação em Ciência da Computação; UFMG
• Palavras-chave em Português: Knowledge; Graph; Probabilistic; Learning; Computação – Teses; Web semântica – Teses; Aprendizado do computador – Teses; Base de conhecimento – Teses; Predição de falhas – Teses
• Link de acesso: http://hdl.handle.net/1843/34314

Knowledge Graph, as the name says, is a way to represent knowledge using a directed graph structure (nodes and edges). However, such graphs are often incomplete or contain a considerable amount of wrong facts. This work presents ProA: a probabilistic algorithm to predict missing facts from Knowledge Graphs based on the probability distribution over paths between entities. Compared to current state-of-the-art approaches, ProA has the following advantages: simplicity as it considers only the topological structure of a knowledge graph, good performance as it does not require any complex calculations, and readiness as it has no other requirement but the graph itself.