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Open AccessArticle

Error Detection in a Large-Scale Lexical Taxonomy

Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150000, China
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Information 2020, 11(2), 97; https://doi.org/10.3390/info11020097
Received: 13 December 2019 / Revised: 3 February 2020 / Accepted: 3 February 2020 / Published: 11 February 2020
(This article belongs to the Special Issue Quality of Open Data)
Knowledge base (KB) is an important aspect in artificial intelligence. One significant challenge faced by KB construction is that it contains many noises, which prevent its effective usage. Even though some KB cleansing algorithms have been proposed, they focus on the structure of the knowledge graph and neglect the relation between the concepts, which could be helpful to discover wrong relations in KB. Motived by this, we measure the relation of two concepts by the distance between their corresponding instances and detect errors within the intersection of the conflicting concept sets. For efficient and effective knowledge base cleansing, we first apply a distance-based model to determine the conflicting concept sets using two different methods. Then, we propose and analyze several algorithms on how to detect and repair the errors based on our model, where we use a hash method for an efficient way to calculate distance. Experimental results demonstrate that the proposed approaches could cleanse the knowledge bases efficiently and effectively.
Keywords: knowledge base; error detection; cleansing knowledge base; error detection; cleansing
MDPI and ACS Style

An, Y.; Liu, S.; Wang, H. Error Detection in a Large-Scale Lexical Taxonomy. Information 2020, 11, 97.

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