Multi-user Feedback for Large-scale Cross-lingual Ontology Matching

Abstract

Automatic matching systems are introduced to reduce the manual workload of users that need to align two ontologies by finding potential mappings and determining which ones should be included in a final alignment. Mappings found by fully automatic matching systems are neither correct nor complete when compared to gold standards. In addition, automatic matching systems may not be able to decide which one, among a set of candidate target concepts, is the best match for a source concept based on the available evidence. To handle the above mentioned problems, we present an interactive mapping Web tool named ICLM (Interactive Cross-lingual Mapping), which aims to improve an alignment computed by an automatic matching system by incorporating the feedback of multiple users. Users are asked to validate mappings computed by the automatic matching system by selecting the best match among a set of candidates, i.e., by performing a mapping selection task. ICLM tries to reduce users’ effort require d to validate mappings. ICLM distributes the mapping selection tasks to users based on the tasks’ difficulty, which is estimated by considering the lexical characterization of the ontology concepts, and the confidence of automatic matching algorithms. Accordingly, ICLM estimates the effort (number of users) needed to validate the mappings. An experiment with several users involved in the alignment of large lexical ontologies is discussed in the paper, where different strategies for distributing the workload among the users are evaluated. Experimental results show that ICLM significantly improves the accuracy of the final alignment using the strategies proposed to balance and reduce the user workload