Titlebook: Knowledge Discovery, Knowledge Engineering and Knowledge Management; 9th International Jo Ana Fred,David Aveiro,Joaquim Filipe Conference p

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書(shū)目名稱(chēng)Knowledge Discovery, Knowledge Engineering and Knowledge Management影響因子(影響力)




書(shū)目名稱(chēng)Knowledge Discovery, Knowledge Engineering and Knowledge Management影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Knowledge Discovery, Knowledge Engineering and Knowledge Management網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Knowledge Discovery, Knowledge Engineering and Knowledge Management網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Knowledge Discovery, Knowledge Engineering and Knowledge Management被引頻次




書(shū)目名稱(chēng)Knowledge Discovery, Knowledge Engineering and Knowledge Management被引頻次學(xué)科排名




書(shū)目名稱(chēng)Knowledge Discovery, Knowledge Engineering and Knowledge Management年度引用




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書(shū)目名稱(chēng)Knowledge Discovery, Knowledge Engineering and Knowledge Management讀者反饋




書(shū)目名稱(chēng)Knowledge Discovery, Knowledge Engineering and Knowledge Management讀者反饋學(xué)科排名

countenance

佛刊

Ontology in Holonic Cooperative Manufacturing: A Solution to Share and Exchange the Knowledgeing knowledge based on an ontology model. Ultimately, the research illustrates and implements the proposed solution over a cooperative assembly scenario, which involves two workers and one collaborative robot, whom cooperate together to assemble a customized product.

GROVE

一瞥

kidney

Conference proceedings 2019ed from 157 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..

換話(huà)題

1865-0929 and selected from 157 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..978-3-030-15639-8978-3-030-15640-4Series ISSN 1865-0929 Series E-ISSN 1865-0937

Seizure

Identification of Relevant Hashtags for Planned Events Using Learning to Rankanked list of top hashtags for the event. The aggregation weights of the features are obtained using a learning to rank algorithm. We establish the superiority of our method by performing detailed experiments on a large dataset containing multiple categories of events and related tweets.

TRACE

Investigation of Passage Based Ranking Models to Improve Document Retrievale, the worst-performing queries are damaged slightly and the those that perform well are boosted for the WebAp collection. However, our rank-based similarity function boosted the performance of the difficult queries in the Ohsumed collection.

抱負(fù)

Knowledge Discovery, Knowledge Engineering and Knowledge Management9th International Jo