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Semantic Cluster Analysis in Information Retrieval

信息检索中的语义聚类分析

基本信息

批准号：
91548218
负责人：
Professor Dr. Norbert Fuhr
金额：
--
依托单位：
Fachgebiet Informationssysteme
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2009
资助国家：
德国
起止时间：
2008-12-31 至 2015-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/91548218?language=en
关键词：
Semantic Cluster Analysis Information Retrieval

项目摘要

Like its predecessor CAIR I, this proposal for a follow-up project will address research issues in the area of cluster analysis in information retrieval. In CAIR I, we started from the observation that the potential for optimizing and adapting clustering methods in IR has not been fully exploited yet, which leads to unsatisfying solutions for relevant retrieval problems. In order to fill this gap, following the general idea of semantic cluster analysis, CAIR I improved the theoretic basis for document clustering (inspired by the probabilistic IR model), investigated specialized retrieval models (based on models for indexing and representation), developed new strategies and algorithms for web applications and carried out corresponding user studies. While CAIR I worked on enriching the semantics in clustering processes via retrieval models, domain knowledge and the combined application of fusion principles, this proposal for a follow-up project aims at extracting knowledge from the clustering process. Besides the further development of the theoretic foundations, this proposal will focus on the user interface to clustering processes ("human access"). Our goal is to make the IR clustering process more understandable and ease the interpretation of its results for the user. The theoretic part will deal with soft clustering and online clustering. In the area of semantics and cluster interpretation, starting from current research results, we will address research issues such as faceted clustering, semi-supervised clustering and the evaluation of clustering results.

与其前身CAIR I一样，这一后续项目提案将解决信息检索聚类分析领域的研究问题。在CAIR I中，我们从观察到IR中优化和适应聚类方法的潜力尚未得到充分利用开始，这导致相关检索问题的解决方案不令人满意。为了填补这一空白，CAIR I遵循语义聚类分析的一般思想，改进了文档聚类的理论基础（受概率IR模型的启发），研究了专门的检索模型（基于索引和表示模型），开发了新的Web应用策略和算法，并进行了相应的用户研究。虽然CAIR I致力于通过检索模型，领域知识和融合原则的综合应用来丰富聚类过程中的语义，但该后续项目的建议旨在从聚类过程中提取知识。除了进一步发展的理论基础，这项建议将集中在用户界面的聚类过程（“人的访问”）。我们的目标是使IR聚类过程更容易理解，并简化用户对其结果的解释。理论部分主要讨论软聚类和在线聚类。在语义和聚类解释方面，从现有的研究成果出发，我们将讨论分面聚类、半监督聚类和聚类结果评价等研究问题。