A graph-based method for interactive mapping revision in DL-Lite. (January 2023) - Li, Weizhuo; Ji, Qiu; Zhang, Songmao; Fu, Xuefeng; Qi, Guilin
Abstract: Discovering the semantic relationships among heterogeneous ontologies has been one of the core research topics in Semantic Web. As ontology matching systems inevitably adopt heuristic strategies, wrong mappings are often contained in final alignments. Most methods for mapping revision depend on dealing with logical incoherence. However, erroneous mappings that do not cause incoherence may be left out. Hence, manual validations with domain expertise are needed. Nevertheless, existing interactive methods for mapping revision still suffer from two limitations. Firstly, revision methods designed to present friendly graphical interfaces and contextual information related to mappings pay little attention to reducing manual decisions, leading to the efficiency problem. Secondly, revision methods focusing on automated reasoning to reduce manual decisions employ models of high complexity, leading to the practicality problem when dealing with alignments across large-scale ontologies. To address these problems, we propose a novel graph-based method for interactive mapping revision, aiming to reduce the manual efforts as much as possible. DL-Lite ontologies and their mappings are encoded into an integrated graph, where the mapping arcs will be judged by the experts. We define the decision space tailored for mapping revision, which can be used to improve the efficiency of manual making decisions. After a manual decision is made in each interaction, the mapping arcs will be automatically updated in the integrated graph. The whole update process modeled in our defined graph-based decision space could be accomplished in polynomial time. We further design an impact function based on the integrated graph and weights of mappings, which can display the most influential mappings to experts. In this way, the number of manual decisions can be reduced further. To cope with the practicality of our method for alignments of large-scale ontologies, we introduce the notion of "reliable" mappings as an attempt to alleviate the burden of experts for making decisions, and propose two soft principles to ensure the reliability of selected mappings. Moreover, we define influence relation and design a corresponding algorithm to enhance the method for detecting incoherence of the integrated graph, which is transformed from the ontologies beyond DL-Lite and their mappings. We implement our method and evaluate its efficiency by 16 alignments generated across real-world ontologies. Experimental results show that our proposed method can improve the efficiency by 19% on average and save more manual decisions than other interactive revision methods in most cases. Highlights: Update mappings automatically based on manual decisions in each interaction. Impact function of mappings reduces the efforts of manual decisions further. Reliable mappings improve the practicality of method for large-scale ontologies. An influence relation enhances the method for detecting incoherent mappings. Save manual decisions better than other interactive revision methods in most cases.