Abstract: Traditional cross-language information retrieval has problems such as low translation mapping
accuracy and semantic deviation after query expansion． To deal with this problem，a method of integrating
statistics and ontology is proposed to construct a multi-language information retrieval model． Using statistical
translation to solve the problem of translation mapping ambiguity，the multi-ontology BabelNet is used to
reduce the loss of semantic relevance． Because the ontology contains a large number of conceptual
connections，the ontology is used as the semantic layer representation to design the semantic weighting
algorithm． And it is built on the BM25F statistical information retrieval model as the user feedback sorting
algorithm． Finally，the multi-language information retrieval prototype system is designed according to the
established model，and the model is tested with the data set obtained based on the crawler technology． The
experimental results show that the average precision of the model is higher than the traditional machine
translation-based information retrieval model．

Key words: BabelNet, multi-language information retrieval, sorting algorithm, semantic relevance