For overcoming the drawback of ignoring the semantic relationship between terms and concept structure in the bag of words model, a source code summarization technology based on syntactic analysis was proposed. Firstly, the part-of-speech tagging was utilized to recognize the keywords that characterized the code feature most. Secondly, the chunk parsing was used to revise the errors that could be introduced in the process of part-of-speech tagging. Thirdly, the noise reduction for those keywords was carried out to decrease the influence of text noise. Finally, several keywords with highest weights were selected to compose the summaries. Through the comparison with TF-IDF (Term Frequency-Inverse Document Frequency)-based and extended TF-IDF-based source code summarization technologies in the experiment, with respect to the overlap coefficient of the golden set, the summaries obtained by the proposed technology are improved by at least 9% and 6% respectively, which illuminates that the proposed technology is able to generate more precise source code summaries.

The application of existing ontology aligning technologies based on evolutionary algorithm is limited by the huge search space of large scale ontology aligning problem. To this end, in this paper, a large scale ontology aligning approach based on a fast elitist Non-dominated Sorting Genetic Algorithm for multi-objective optimization (NSGA-Ⅱ) was proposed. To be specific, it worked in three steps: 1) a neighbor similarity based ontology partitioning algorithm was presented to split the source ontology into a set of disjoint concept blocks; 2) a relevant concept filtering method was proposed to determine the concept block in target ontology associated with each source one; 3) NSGA-Ⅱ was utilized to align the various concept block pairs and a greedy algorithm was used to aggregate various results. Small scale bibliographic ontology benchmark and large scale biomedic ontology benchmark in OAEI 2012 were used to test the proposed approach. The comparisons with the participants of OAEI 2012 show that the large scale ontology aligning approach based on NSGA-Ⅱ is able to determine good alignments in a short time, and therefore it is effective.