Paper accepted at K-CAP 2017: Detection of Relation Assertion Errors in Knowledge Graphs

The paper "Detection of Relation Assertion Errors in Knowledge Graphs", authored by André Melo and Heiko Paulheim, has been accepted at the Ninth International Conference on Knowledge Capture (K-CAP 2017).

Abstract:

Although the link prediction problem, where missing relation assertions are predicted, has been widely researched, error detection did not receive as much attention. In this paper, we investigate the problem of error detection in relation assertions of knowledge graphs, and we propose an error detection method which relies on path and type features used by a classifier for every relation in the graph exploiting local feature selection. We perform an extensive evaluation on a variety of datasets, backed by a manual evaluation on DBpedia and NELL, and we propose and evaluate heuristics for the selection of relevant graph paths to be used as features in our method.

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