Pesquisa de Doutorado da Veronica

Fonte: https://plato.stanford.edu/entries/abduction/ In the philosophical literature, the term “ abduction ” is used in two related but different senses. In both senses, the term refers to some form of explanatory reasoning . However, in the historically first sense, it refers to the place of explanatory reasoning in generating hypotheses, while in the sense in which it is used most frequently in the modern literature it refers to the place of explanatory reasoning in justifying hypotheses. In the latter sense, abduction is also often called “ Inference to the Best Explanation. ” [Definição moderna: justificar hipóteses. Inferir a Melhor Explicação] Most philosophers agree that abduction (in the sense of Inference to the Best Explanation) is a type of inference that is frequently employed, in some form or other, both in everyday and in scientific reasoning. [Usada tanto no dia a dia quanto na própria ciência] The type of inference exhibited here is called abduction or, somewhat more c...

Sobre Do Raphaël Troncy, meu colega no projeto europeu: Thanks for this pointer! Interesting work! I skim through it and one of the related work I can think of but not cited is 1) the work of Fabian Suchanek with AMIE and rule mining in KG to detect automatically outliers / false values / inconsistent values / incongruences / etc. and 2) to some extent, the body of literature working on detecting pseudo-keys for doing entity matching, and again relying on mining property-values that are generally true for 90%+ of the entities but not for a few ones suggesting errors. Did you look into this too? Acho que vale dar uma olhada (me parece que seriam trabalhos complementares ao que a gente fez) para a tese. []s Veronica: Do item 1, olhando as publicações do Fabian Suchanek no DBLP,  eu separei 3 artigos sobre o AMIE (2013, 2015, 2020) para ler. Dois são citados no survey da ACM sobre KG na seção 4.4.1 Rule Mining Daniel: Lembrando que é um trabalho que poderia complementar ...

 Abstract ... While ILP is a mature field, mining logical rules from KBs is difficult, because KBs make an open-world assumption. This means that absent information cannot be taken as counterexamples. ... 1 Introduction In recent years, however, the KBs have become so large that they can themselves be mined for information. It is possible to find rules in the KBs that describe common correlations in the data. [As regras se baseiam nas instâncias para extrair estes padrões SE-ENTÃO] Since RDF has only positive inference rules, these KBs contain only positive statements and no negations. Furthermore, they operate under the open-world assumption (OWA). Under the OWA, a statement that is not contained in the KB is not necessarily false; it is just unknown. This is a crucial difference to many standard database settings that operate under the closed-world assumption(CWA). Consider an example KB that does not contain the information that a particular person is married. Under the CWA, we ...

ABSTRACT: Inductive Logic Programming (ILP) can be used to mine logical rules from the KB. These rules can help deduce and add missing knowledge to the KB. While ILP is a mature field, mining logical rules from KBs is different in two aspects: First, current rule mining systems are easily overwhelmed by the amount of data .... Second, ILP usually requires counterexamples. KBs, however, implement the open world assumption (OWA), meaning that absent data cannot be used as counterexamples.    [Mesmo problema de algortimos de ML para Link Prediction] 1. INTRODUCTION   Making these KBs complete requires great effort to extract facts, check them for correctness, and add them to the KB. However, KBs themselves often already contain enough information to derive and add new facts. ... As for any rule, there can be exceptions, but in the vast majority of cases, the rule will hold. [Mesmo que as regras não prevejam todos os fatos corretamente pois há exceções ainda é válido para a m...