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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 | |
|---|---|---|---|---|
| Dissertação André Gonzaga - Versão Final.pdf | 4.26 MB | Adobe PDF | View/Open |
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