6533b82efe1ef96bd1293dc9

RESEARCH PRODUCT

Relating RSS News/Items

Richard ChbeirJoe TekliMarco VivianiKokou YetongnonFekade Getahun

subject

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceRSS[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologySimilarityTheoretical Computer ScienceWorld Wide Web[SCCO.COMP] Cognitive science/Computer science020204 information systemsSimilarity (psychology)0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Neighbourhood (mathematics)[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]Structure (mathematical logic)[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Measure (data warehouse)[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]Information retrievalRelationship[INFO.INFO-WB]Computer Science [cs]/WebComputer Science (all)[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]computer.file_format[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]RSS Relatedne[ SCCO.COMP ] Cognitive science/Computer scienceVector space model020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]InformationSystems_MISCELLANEOUSNeighbourhoodcomputer

description

Merging related RSS news (coming from one or different sources) is beneficial for end-users with different backgrounds (journalists, economists, etc.), particularly those accessing similar information. In this paper, we provide a practical approach to both: measure the relatedness, and identify relationships between RSS elements. Our approach is based on the concepts of semantic neighborhood and vector space model, and considers the content and structure of RSS news items. © 2009 Springer Berlin Heidelberg.

https://doi.org/10.1007/978-3-642-02818-2_36