Knowledge Base Unification via Sense Embeddings and Disambiguation

Abstract

We present KB-UNIFY, a novel approach for integrating the output of different Open Information Extraction systems into a single unified and fully disambiguated knowledge repository. KB-UNIFY consists of three main steps: (1) disambiguation of relation argument pairs via a sensebased vector representation and a large unified sense inventory; (2) ranking of semantic relations according to their degree of specificity; (3) cross-resource relation alignment and merging based on the semantic similarity of domains and ranges. We tested KB-UNIFY on a set of four heterogeneous knowledge bases, obtaining high-quality results. We discuss and provide evaluations at each stage, and release output and evaluation data for the use and scrutiny of the community1.

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Cite this paper

@inproceedings{Bovi2015KnowledgeBU, title={Knowledge Base Unification via Sense Embeddings and Disambiguation}, author={Claudio Delli Bovi and Luis Espinosa Anke and Roberto Navigli}, booktitle={EMNLP}, year={2015} }