混合语义相似度提取下高维重叠数据智能模糊检索算法

Intelligent Fuzzy Retrieval Algorithm for High Dimensional Overlapping Data under Hybrid Semantic Similarity Extraction

摘要

针对在数据智能检索过程中, 单一语义相似度的局限性可能会影响数据相关性的判断, 导致检索结果的排序敏感度不高的问题, 提出了混合语义相似度提取下高维重叠数据智能模糊检索算法。建立高维重叠数据的信息粒度空间, 通过密度聚类与模糊度量, 识别出空间中的重叠数据所在的区域, 从而在不改变数据结构的前提下, 实现高维数据降维。混合均值函数、近似线性化统计、规则方法3种语义相似计算方法, 综合分析降维数据的相关性。在最近邻算法的遍历下, 按序提取出语义相似度较高的数据信息, 生成数据检索列表。算例测试结果表明, 该算法所表现出的NDCG(Normalized Discounted Cumulative Gain)指标值平均为0.867, 检索结果具有较高的排序敏感度, 检索质量更高, 具有良好的实践应用前景。

Abstract

In the process of intelligent data retrieval, the limitation of a single semantic similarity may affect the judgment of data relevance, resulting in a low ranking sensitivity of the retrieval results. To alleviate this problem, an intelligent fuzzy retrieval algorithm is proposed for high-dimensional overlapping data under hybrid semantic similarity extraction. The information granularity space of high-dimensional overlapping data is established. Through density clustering and fuzzy measurement, the regions where the overlapping data is located in the space are identified, thereby achieving the dimension reduction of high-dimensional data without changing the data structure. Three semantic similarity calculation methods, namely the mixed mean function, approximate linearization statistics, and rule method, are used to comprehensively analyze the correlation of dimensionality reduction data. Under the traversal of the nearest neighbor algorithm, the data information with higher semantic similarity is extracted in sequence to generate the data retrieval list. The results of the case study show that the average NDCG(Normalized Discounted Cumulative Gain) index value exhibited by this algorithm is 0.867. The retrieval results have a high ranking sensitivity and higher retrieval quality, and have a good practical application prospect.

关键词

数据智能检索 / 数据模糊检索 / 高维重叠数据 / 混合语义相似度 / 数据检索 / 智能检索

Key words

data intelligent retrieval / fuzzy data retrieval / high-dimensional overlapping data / mixed semantic similarity / data retrieval / intelligent retrieval

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基金资助

浙江省自然科学基金资助项目(POF1D6215623)