Data Cleaning publication categorizer

The Web of Data has emerged as a way of exposing structured
linked data on the Web. It builds on the central building
blocks of the Web (URIs, HTTP) and benets from its
simplicity and wide-spread adoption. It does, however, also
inherit the unresolved issues such as the broken link problem.
Broken links constitute a major challenge for actors
consuming Linked Data as they require them to deal with
reduced accessibility of data. We believe that the broken
link problem is a major threat to the whole Web of Data
idea and that both Linked Data consumers and providers
will require solutions that deal with this problem. Since no
general solutions for xing such links in the Web of Data
have emerged, we make three contributions into this direction:
rst, we provide a concise denition of the broken
link problem and a comprehensive analysis of existing approaches.
Second, we present DSNotify, a generic framework
able to assist human and machine actors in xing broken
links. It uses heuristic feature comparison and employs
a time-interval-based blocking technique for the underlying
instance matching problem. Third, we derived benchmark
datasets from knowledge bases such as DBpedia and evaluated
the eectiveness of our approach with respect to the
broken link problem. Our results show the feasibility of a
time-interval-based blocking approach for systems that aim
at detecting and xing broken links in the Web of Data.