多关键字查询中LCA剪枝概念树的查询扩展技术研究

Multi-keywords Query Expansion with OLCA Based Concept Tree Pruning

语义查询扩展中,关键一步是扩展词的选择方法和扩展词权重的计算。提出一种改进的LCA(局部上下文分析法):OLCA(Optimize Local Context Analysis)。OLCA应用于分权重的多关键字查询中,结合WordNet概念树,从语义和实际查询语料两方面对初始查询词进行扩展,并根据初始查询词中多个关键词的位置,结合扩展候选集中词间关系计算修正各扩展词的权重。实验证明,与单独基于统计或基于语义的查询扩展方法相比,其查准率和查全率均有较大提高。

In semantic-based query expansion, computing expansion words and its weight is a key step to describe the needed query. We proposed a method called OLCA （keyword to concept method）, the idea comes from LCA（Local context analysis）. We made some improvement and applied it to multi-keywords query with different weight according to their attribute to the query. Combined with concept tree based on WordNet, we made the query expansion performed from both semantic and the real query documents aspects, and calculated the weight of expansion term based on this technique. Compared with the one based on semantic or the traditional expansion which merely is based on statistic, the experiments reveal that this method can achieve a better query quality.