DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
YAGO2: exploring and querying world knowledge in time, space, context, and many languages
Proceedings of the 20th international conference companion on World wide web
A self-training approach for resolving object coreference on the semantic web
Proceedings of the 20th international conference on World wide web
PARIS: probabilistic alignment of relations, instances, and schema
Proceedings of the VLDB Endowment
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This paper proposes Fria, a fast and robust instance alignment framework across two independently built knowledge bases (KBs). Our objective is two-fold: (1) to design an effective instance similarity measure and (2) to build a fast and robust alignment framework. Specifically, Fria consists of two-phases. Fria first achieves high-precision alignment for seed matches which have strong evidence for aligning. To obtain high-recall alignment, Fria then divides non-matched instances according to the types identified from seeds, and gives additional chances to the same-typed instances to be matched. Experimental results show that Fria is fast and robust, by achieving comparable accuracy to state-of-the-arts and a 10-times speed up.