Concept coupling learning for improving concept lattice-based document retrieval
Concept coupling learning for improving concept lattice-based document retrieval
复制标题
用于改进基于概念格的文档检索的概念耦合学习
DOI：
10.1016/j.engappai.2017.12.007
复制
发表时间：
2018
影响因子：
8
通讯作者：
Cao Longbing
中科院分区：
文献类型：
--
作者：
Hao Shufeng;Shi Chongyang;Niu Zhendong;Cao Longbing
任何文档集合中的语义信息对于信息检索中的查询理解都是至关重要的。现有的基于概念格的检索系统主要依靠形式概念的偏序关系来索引文档。然而，这些系统使用的方法往往忽略了从集合中提取的形式概念之间的显式语义信息。本文提出了一种概念耦合关系分析模型，用于学习和聚合概念内和概念间的耦合关系。概念内耦合关系使用形式概念的公共术语来描述形式概念的显式语义。概念间耦合关系采用形式概念的偏序关系来捕捉形式概念的隐含依赖关系。在概念耦合关系分析模型的基础上，提出了一种基于概念格的检索框架。该框架基于模糊形式概念分析在概念空间中表示用户查询和文档，利用概念格作为语义索引来组织文档，并根据学习到的概念耦合关系对文档进行排序。在从智能信息检索系统获取的文本集合上进行了实验。与经典的基于概念格的检索方法相比，我们提出的方法在所有集合的平均MAP、IAP@11和P@10上分别获得了至少9%、8%和15%的改进。
The semantic information in any document collection is critical for query understanding in information retrieval. Existing concept lattice-based retrieval systems mainly rely on the partial order relation of formal concepts to index documents. However, the methods used by these systems often ignore the explicit semantic information between the formal concepts extracted from the collection. In this paper, a concept coupling relationship analysis model is proposed to learn and aggregate the intra- and inter-concept coupling relationships. The intra-concept coupling relationship employs the common terms of formal concepts to describe the explicit semantics of formal concepts. The inter-concept coupling relationship adopts the partial order relation of formal concepts to capture the implicit dependency of formal concepts. Based on the concept coupling relationship analysis model, we propose a concept lattice-based retrieval framework. This framework represents user queries and documents in a concept space based on fuzzy formal concept analysis, utilizes a concept lattice as a semantic index to organize documents, and ranks documents with respect to the learned concept coupling relationships. Experiments are performed on the text collections acquired from the SMART information retrieval system. Compared with classic concept lattice-based retrieval methods, our proposed method achieves at least 9%, 8% and 15% improvement in terms of average MAP, IAP@11 and P@10 respectively on all the collections.