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http://hdl.handle.net/1843/34314
Type: | Dissertação |
Title: | A probabilistic algorithm to predict missing facts from knowledge graphs |
Other Titles: | Um algoritmo probabilístico para predição de fatos em grafos de conhecimento |
Authors: | André Lopes Gonzaga |
First Advisor: | Mirella Mouro Moro |
First Co-advisor: | Mario Sérgio Ferreira Alvim Júnior |
First Referee: | Luiz Chaimowicz |
Second Referee: | Denilson Barbosa |
Abstract: | 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. |
language: | eng |
metadata.dc.publisher.country: | Brasil |
Publisher: | Universidade Federal de Minas Gerais |
Publisher Initials: | UFMG |
metadata.dc.publisher.department: | ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO |
metadata.dc.publisher.program: | Programa de Pós-Graduação em Ciência da Computação |
Rights: | Acesso Aberto |
URI: | http://hdl.handle.net/1843/34314 |
Issue Date: | 31-Jan-2019 |
Appears in Collections: | Dissertações de Mestrado |
Files in This Item:
File | Description | Size | Format | |
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Dissertação André Gonzaga - Versão Final.pdf | 4.26 MB | Adobe PDF | View/Open |
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