Along with the rapidly growing scale of relational database (RDB), how to construct domain-related ontologies from various databases effectively and efficiently has been a bottleneck of the ontology-based integration. The traditional methods for constructing ontology from RDB are mainly based on the manual mapping and transformation, which not only requires a lot of human experience but also easily leads to the semantic loss during the transformation. Ontology learning from RDB is a new paradigm to (semi-)automatically construct ontologies from RDB by borrowing the techniques of machine learning, it provides potential opportunities for integrating heterogeneous data from various data sources efficiently. This paper surveys the recent methods and tools of the ontology learning from RDB, and highlights the potential opportunities and challenges of using ontology learning in semantic information integration. Initially, the previous surveys on the topic of the ontology-based integration and ontology learning were summarized, and then the limitations of previous surveys were identified and analyzed. Furthermore, the methods and techniques of ontology learning from RDB were investigated by classifying into three categories: reverse engineering, mapping, and machine learning. Accordingly, the opportunities and possibility of using ontology learning from RDB in semantic information integration were discussed based on the mapping results between the bottlenecks of ontology-based integration and the features of ontology learning. a

To tackle the issues of semantic collision and inconsistencies between ontologies and the original data model while learning ontology from relational database (RDB), a semi-automatic semantic consistency checking method based on graph intermediate representation and model checking is presented. Initially, the W-Graph, as an intermediate model between databases and ontologies, was utilized to formalize the semantic correspondences between databases and ontologies, which were then transformed into the Kripke structure and eventually encoded with the SMV program. Meanwhile, description logics (DLs) were employed to formalize the semantic specifications of the learned ontologies, since the OWL DL showed good semantic compatibility and the DLs presented an excellent expressivity. Thereafter, the specifications were converted into a computer tree logic (CTL) formula to improve machine readability. Furthermore, the task of checking semantic consistency could be converted into a global model checking problem that could be solved automatically by the symbolic model checker. Moreover, an example is given to demonstrate the specific process of formalizing and checking the semantic consistency between learned ontologies and RDB, and a verification experiment was conducted to verify the feasibility of the presented method. The results showed that the presented method could correctly check and identify the different kinds of inconsistencies between learned ontologies and its original data model.

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To provide the personalized services for learners according to user's learning behavior, firstly, through theory of Fuzzy Ontology, the ambiguity and uncertainty of information in E-learning recommendation system was analyzed, and fuzzy information was extended to the ontology. Secondly, the user interest estimation based on behavior was studied in term of user's learning preferences and cognitive status, user profile was described by the learning object, and then a user model updating algorithm was proposed. Finally, a fuzzy recommendation system based on user profile was built. As verified by experiment, the results have shown that the system can provided the personalized learning services according to user's learning behavior, and it is useful in describing user preferences.

Abstract. To provide the personalized services for learners according to user's learning behavior, firstly, through theory of Fuzzy Ontology, the ambiguity and uncertainty of information in E-learning recommendation system was analyzed, and fuzzy information was extended to the ontology. Secondly, the user interest estimation based on behavior was studied in term of user's learning preferences and cognitive status, user profile was described by the learning object, and then a user model updating algorithm was proposed. Finally, a fuzzy recommendation system based on user profile was built. As verified by experiment, the results have shown that the system can provided the personalized learning services according to user's learning behavior, and it is useful in describing user preferences.