Sensing Semantics of RSS Feeds by Fuzzy Matchmaking

RSS feeds provide a fast and effective way to publish up-to-date information or renew outdated contents for information subscribers. So far RSS information is mostly managed by content publishers but Internet users have less initiative to choose what they really need. More attention needs to be paid on techniques for user-initiative information discovery from RSS feeds. In this paper, a quantitative semantic matchmaking method for the RSS based applications is proposed. Semantic information is extracted from an RSS feed as numerical vectors and semantic matching can then be conducted quantitatively. Ontology is applied to provide a common-agreed matching basis for the quantitative matchmaking. In order to avoid semantic ambiguity of literal statements from distributed and heterogeneous RSS publishers, fuzzy inference is used to transform an individual-dependent vector into an individual-independent vector. Semantic similarities can be revealed as the result.