Defeasible argumentation over relational databases

FALAPPA, MARCELO ALEJANDRO; CHESÑEVAR, CARLOS IVÁN; DEAGUSTINI, CRISTHIAN ARIEL DAVID; FULLADOZA DALIBÓN, SANTIAGO EMANUEL; FALAPPA, MARCELO ALEJANDRO; CHESÑEVAR, CARLOS IVÁN; DEAGUSTINI, CRISTHIAN ARIEL DAVID; FULLADOZA DALIBÓN, SANTIAGO EMANUEL; SIMARI, GUILLERMO RICARDO; GOTTIFREDI, SEBASTIÁN; SIMARI, GUILLERMO RICARDO; GOTTIFREDI, SEBASTIÁN

Argument & Computation

IOS Press

Año: 2017 vol. 8 p. 35 - 59

1946-2166

 Defeasible argumentation has been applied successfully in several real-world domains in which it is necessary to handle incomplete and contradictory information. In recent years, there have been interesting attempts to carry out argumentation processes supported by massive repositories developing argumentative reasoning applications. One of such efforts builds arguments by retrieving information from relational databases using the DBI-DeLP framework; this article presents eDBI-DeLP, which extends the original DBI-DeLP framework by providing two novel aspects which refine the interaction between DeLPprograms and relational databases. First, we expand the expressiveness of dbi-delp programs by providing ways of controlling how the information in databases is recovered; this is done by introducing filters that enable an improved fine-grained control on the argumentation processes which become useful in applications, providing the semantics and the implementation of such filters. Second, we introduce an argument comparison criterion which can be adjusted at the level of literals to model particular features such as credibility and topic expertise, among others. These new tools can be particularly useful in  environments such as medical diagnosis expert systems, decision support systems, or recommender systems based on argumentation, where datasets are often provided in the form of relational databases.