知识库在以文本为中心的信息检索中的应用

Laura Dietz, Alexander Kotov, E. Meij
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引用次数: 6

摘要

通用知识库在深度(内容)和宽度(覆盖范围)方面日益增长。此外,实体链接和实体检索的算法在过去几年中有了巨大的改进。这些发展产生了一条新的研究路线,利用并结合这些发展来实现以文本为中心的信息检索应用程序。本教程主要关注a)如何检索一组实体用于特定查询,或者更广泛地说,评估所需信息的KB元素的相关性,b)如何用这些元素对文本进行注释,以及c)如何使用这些信息来评估文本的相关性。我们将讨论知识图中可用的不同类型的信息,以及如何最有效地利用每种信息。我们首先简要概述了不同类型的知识库、它们的结构以及流行的通用和特定于领域的知识库中包含的信息。我们特别关注通过名称、术语、关系和类型分类法在知识库中表示以实体为中心的信息。接下来,我们将简要介绍从知识图中检索特定对象以及实体链接和检索。这是必不可少的技术,本教程的其余部分将以此为基础。接下来,我们将介绍成功的实体链接系统中的基本组件,包括实体名称信息的收集以及与上下文实体提及消除歧义的技术。我们将详细介绍先前提出的四个系统,它们成功地利用知识库来改进临时文档检索。这些系统一方面结合了实体检索和语义搜索的概念,另一方面结合了文本检索模型和实体链接。最后,我们还涉及知识图中的实体方面和链接,因为它可以帮助理解实体的上下文。本教程是第一个编译、总结和传播这一新兴领域进展的教程,我们提供了最先进方法的概述,并概述了开放的研究问题,以鼓励新的贡献。
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Utilizing Knowledge Bases in Text-centric Information Retrieval
General-purpose knowledge bases are increasingly growing in terms of depth (content) and width (coverage). Moreover, algorithms for entity linking and entity retrieval have improved tremendously in the past years. These developments give rise to a new line of research that exploits and combines these developments for the purposes of text-centric information retrieval applications. This tutorial focuses on a) how to retrieve a set of entities for an ad-hoc query, or more broadly, assessing relevance of KB elements for the information need, b) how to annotate text with such elements, and c) how to use this information to assess the relevance of text. We discuss different kinds of information available in a knowledge graph and how to leverage each most effectively. We start the tutorial with a brief overview of different types of knowledge bases, their structure and information contained in popular general-purpose and domain-specific knowledge bases. In particular, we focus on the representation of entity-centric information in the knowledge base through names, terms, relations, and type taxonomies. Next, we will provide a recap on ad-hoc object retrieval from knowledge graphs as well as entity linking and retrieval. This is essential technology, which the remainder of the tutorial builds on. Next we will cover essential components within successful entity linking systems, including the collection of entity name information and techniques for disambiguation with contextual entity mentions. We will present the details of four previously proposed systems that successfully leverage knowledge bases to improve ad-hoc document retrieval. These systems combine the notion of entity retrieval and semantic search on one hand, with text retrieval models and entity linking on the other. Finally, we also touch on entity aspects and links in the knowledge graph as it can help to understand the entities' context. This tutorial is the first to compile, summarize, and disseminate progress in this emerging area and we provide both an overview of state-of-the-art methods and outline open research problems to encourage new contributions.
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