Social Semantic Search: A Case Study on Web 2.0 for Science
作者： Laurens De Vocht , Selver Softic , Ruben Verborgh , Erik Mannens , Martin Ebner
DOI: 10.4018/IJSWIS.2017100108
关键词:
摘要: When researchers formulate search queries to find relevant content on the Web, those typically consist of keywords that can only be matched in or its metadata. The Web Data extends this functionality by bringing structure and giving well-defined meaning it enables humans machines work together using controlled vocabularies. Due high degree mismatches between vocabularies different sources, searching over multiple heterogeneous repositories structured data is considered challenging. Therefore, authors present a semantic engine for facilitating research related Linked Data. To facilitate high-precision interactive search, they annotated interlinked with ontologies from various an effective model. Furthermore, authors' system adaptive as synchronize new social media accounts efficiently explore datasets.
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sci-hub.se 参考文章(41)
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