Using Redescriptions and Formal Concept Analysis for Mining Definitions in Linked Data

Autor:	Yannick Toussaint, Justine Reynaud, Amedeo Napoli
Rok vydání:	2019
Předmět:	Set (abstract data type) Information retrieval Point (typography) Association rule learning Computer science Formal concept analysis Linked data Reuse Class (biology)
Zdroj:	Formal Concept Analysis ISBN: 9783030214616 ICFCA
DOI:	10.1007/978-3-030-21462-3_16
Popis:	In this article, we compare the use of Redescription Mining (RM) and Association Rule Mining (ARM) for discovering class definitions in Linked Open Data (LOD). RM is aimed at mining alternate descriptions from two datasets related to the same set of individuals. We reuse RM for providing category definitions in DBpedia in terms of necessary and sufficient conditions (NSC). Implications and AR can be jointly used for mining category definitions still in terms of NSC. In this paper, we firstly, recall the basics of redescription mining and make precise the principles of definition discovery. Then we detail a series of experiments carried out on datasets extracted from DBpedia. We analyze the different outputs related to RM and ARM applications, and we discuss the strengths and limitations of both approaches. Finally, we point out possible improvements of the approaches.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::23ee41b26e343315a63920b3401c718b https://doi.org/10.1007/978-3-030-21462-3_16 Zobrazit plný text záznamu