Aligning the large-scale ontologies on schema-level for weaving Chinese linked open data

Abstract

This study purposes a data field and sequence alignment-based ontology mapping architecture, as ontology mapping is an important way to solve the problem of ontology heterogeneous; meanwhile, it also can be considered as a typical scenario on finding the schema-level links in linked open data (LOD). Current researches on LOD is mainly focused on the level of instances so that the task on finding schema-level links between LOD datasets is being ignored. Furthermore, nowadays the knowledge described in Chinese is also an important part of online knowledge base; unfortunately, current Chinese ontology mapping systems have low efficiency and availability, there is still lack of relevant system for large-scale Chinese ontology mapping task in LOD environment. In order to solve this problem, firstly, based on the improved nuclear field-like potential function, we compress the size of unaligned large-scale Chinese ontology. Secondly, we use the sequence alignment algorithm to compute similarity between the concepts. Finally, we compare our system to other typical concept mapping algorithms. Results prove it can reduce the scale of mapping task effectively and also have higher overall performance. This study may have important practical significance for promoting Chinese knowledge sharing, reusing and interoperation.