Scalable association rule mining with predication on semantic representations of data

Autor:	Li-Shiang Tsay, Sreenivas R. Sukumar, Larry W. Roberts
Rok vydání:	2015
Předmět:	Computer science business.industry computer.file_format computer.software_genre Social Semantic Web Semantic grid Named graph Semantic equivalence Semantic similarity Semantic computing Graph (abstract data type) SPARQL Data mining business computer
Zdroj:	TAAI
Popis:	Finding semantic associations from a vast amount of heterogeneous data is an important and useful task in various applications. We present a framework to extract semantic association patterns directly from a very large graph dataset without the extra step of converting graph data into transaction data. The proposed algorithm SAG (Semantic Association Generator) utilizes the principle of minimum description length to unearth general relevant associations and demonstrates that SPARQL commands can be used to perform data mining tasks.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c497f0eb046d99896f0e0c6e8a42c4c2 https://doi.org/10.1109/taai.2015.7407080 Zobrazit plný text záznamu