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CrumbTrail: An efficient methodology to reduce multiple inheritance in knowledge graphs
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DOI：10.1016/j.knosys.2018.03.030.png)
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Abstract
En 中文
In this paper we present CRUMBTRAIL, an algorithm to clean large and dense knowledge graphs. CRUMB TRAIL removes cycles, out-of-domain nodes and non-essential nodes, i.e., those that can be safely removed without breaking the knowledge graph's connectivity. It achieves this through a bottom-up topological pruning on the basis of a set of input concepts that, for instance, a user can select in order to identify a domain of interest. Our technique can be applied to both noisy hypernymy graphs-typically generated by ontology learning algorithms as intermediate representations-as well as crowdsourced resources like Wikipedia, in order to obtain clean, domain-focused concept hierarchies. CRUMBTRAIL overcomes the time and space complexity limitations of current state-of-art algorithms. In addition, we show in a variety of experiments that it also outperforms them in tasks such as pruning automatically acquired taxonomy graphs, and domain adaptation of the Wikipedia category graph. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Semantic networks
Ontologies
Knowledge graph pruning
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