Inconsistency Detection in Multilingual Knowledge Sharing

Abstract

Multilingual knowledge sharing imposes new requirements on knowledge management systems so as to present digital knowledge resources in multiple languages. Knowledge sharing is degraded by inconsistencies such as contents omitted or altered in one of the languages. To resolve this issue, we present a mechanism for detecting inconsistencies in multilingual knowledge sharing. A state transition model is proposed to define the states of the multilingual contents, the set of actions, and the set of transition functions. Inconsistency detection rules are designed to represent the states of the multilingual contents and thus permit the identification of inconsistencies in knowledge sharing. The analysis of a multilingual Wikipedia article indicates that inconsistencies are present in multilingual contents generated by collaboration. In experiments, the proposed mechanism is applied to a test set of revision histories of multilingual articles; the outcome shows satisfactory results with an average precision of 88% in detecting inconsistencies and a recall of 86%. While the proposal considers only user edit actions, it can detect inconsistencies which will be useful in allowing Natural Language Processing (NLP) based systems to synchronize multilingual contents in an early phase.