Titlebook: Knowledge Discovery, Knowledge Engineering and Knowledge Management; 10th International J Ana Fred,Ana Salgado,Joaquim Filipe Conference pr

INEPT

书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management影响因子(影响力)




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management影响因子(影响力)学科排名




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management网络公开度




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management网络公开度学科排名




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management被引频次




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management被引频次学科排名




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management年度引用




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management年度引用学科排名




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management读者反馈




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management读者反馈学科排名

BOOM

DHPs: Dependency Hearst’s Patterns for Hypernym Relation Extractionormance of Dependency Hearst’s patterns to lexico-syntactic patterns: Hearst’s patterns and an extended set of Hearst’s patterns applied on two corpora: Music and English. Dependency Hearst’s patterns yield to a considerable improve in term of recall and a slight decrease in term of precision.

Peak-Bone-Mass

交响乐

Spina-Bifida

1865-0929 eering and Knowledge Management, IC3K 2018, held in Seville, Spain, in September 2018..The 12 full papers presented were carefully reviewed and selected from 167 submissions. The papers are organized in topical sections on knowledge discovery and information retrieval; knowledge engineering and onto

calorie

Conference proceedings 20207 submissions. The papers are organized in topical sections on knowledge discovery and information retrieval; knowledge engineering and ontology development; and knowledge management and information sharing..

招惹

ADORE

著名

Analysis and Detection of Unreliable Users in Twitter: Two Case Studiesssify a twitter account according to its reliability. The experiments conducted using two different data sets show performance above 94% using Decision Trees as the learning algorithm. These experiments, although with some limitations, provide some encouraging results for future research on detectin

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