A probabilistic algorithm to predict missing facts from knowledge graphs
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Universidade Federal de Minas Gerais
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Dissertação de mestrado
Título alternativo
Um algoritmo probabilístico para predição de fatos em grafos de conhecimento
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Membros da banca
Luiz Chaimowicz
Denilson Barbosa
Denilson Barbosa
Resumo
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.
Abstract
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Knowledge, Graph, Probabilistic, Learning, Computação – Teses, Web semântica – Teses, Aprendizado do computador – Teses, Base de conhecimento – Teses, Predição de falhas – Teses