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 sense-based 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 community
Knowledge base unification via sense embeddings and disambiguation / DELLI BOVI, Claudio; Espinosa Anke, Luis; Navigli, Roberto. - ELETTRONICO. - (2015), pp. 726-736. (Intervento presentato al convegno Conference on Empirical Methods in Natural Language Processing tenutosi a Lisbon, Portugal nel September 17 - 21, 2015).
Knowledge base unification via sense embeddings and disambiguation
DELLI BOVI, CLAUDIO;NAVIGLI, ROBERTO
2015
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 sense-based 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 communityFile | Dimensione | Formato | |
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