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Dynamic taxonomy composition via keyqueries

机译：通过关键查询动态分类法组成

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摘要

This paper presents an unsupervised framework for dynamic, subject-oriented taxonomy composition in digital libraries, which can naturally integrate existing library classification systems. The taxonomy classes in our approach correspond to so-called keyqueries that are run against the digital library's full-text retrieval system. Given a document, a keyquery is a set of few keywords for which the document achieves a high relevance score. Keyqueries can hence be viewed as a general and concise description of the returned retrieval results. The keyquery framework addresses important problems of static classification systems: overlarge classes and overly complex taxonomy structures. If, for instance, a leaf class grows to an indigestible size, keyqueries for the contained documents provide a suitable split mechanism. Since queries are well-known to library users from their daily web search experience, they increase the structural complexity in a transparent way. The paper presents also a strategy for taxonomy-based library exploration. Given a user's information need in the form of library documents, we synthesize a hierarchy of keyqueries that covers this library subset. We manage to solve this difficult set covering problem on-the-fly by combining inverted and reverted indexes along with heuristic search space pruning within a map-reduce application. An empirical evaluation with an ACM collection of scientific papers demonstrates the efficiency and effectiveness of our taxonomy composition framework.
机译：本文提出了一种数字图书馆中动态的，面向主题的分类法组成的无监督框架，该框架可以自然地集成现有的图书馆分类系统。我们的方法中的分类法类别对应于针对数字图书馆的全文检索系统运行的所谓的关键查询。在给定文档的情况下，关键字查询是文档为其获得较高相关性分数的少量关键字的集合。因此，关键字查询可以看作是对返回的检索结果的简要概述。关键字查询框架解决了静态分类系统的重要问题：过大的类和过于复杂的分类结构。例如，如果叶类增长到难以消化的大小，则所包含文档的键查询将提供合适的拆分机制。由于查询是图书馆用户从日常网络搜索经验中众所周知的，因此它们以透明的方式增加了结构的复杂性。本文还提出了基于分类学的图书馆探索策略。给定用户对库文件形式的信息需求，我们综合了覆盖此库子集的键查询层次结构。我们通过在地图化简应用程序中结合倒置和还原的索引以及启发式搜索空间修剪，来快速解决这一难题。 ACM收集的科学论文进行的实证评估证明了我们分类学组成框架的效率和有效性。

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