论文标题
基于会话的主题建议地理探索性搜索
Session-based Suggestion of Topics for Geographic Exploratory Search
论文作者
论文摘要
探索性信息搜索可以在制定有效搜索查询时向用户挑战。此外，复杂的信息空间（例如由地理信息系统管理的空间）可能会迷失人们，因此很难找到相关的数据。为了解决这些问题，我们开发了一个基于会话的建议模型，该模型通过考虑用户的先前查询来提出概念“您也可能对”功能。我们的模型可以应用于交互式搜索中的逐步生成建议。它可用于查询扩展，通常可以指导用户探索可能复杂的数据类别空间。我们的模型基于一个概念共发生图，该图描述了在搜索会话中一起搜索的频率。从本体论域表示开始，我们通过分析主要搜索引擎的查询日志生成图。此外，我们确定了本体论概念的簇，这些概念经常通过图表上的社区检测在日志的会话中共发生。对我们模型的评估提供了令人满意的精度结果。
Exploratory information search can challenge users in the formulation of efficacious search queries. Moreover, complex information spaces, such as those managed by Geographical Information Systems, can disorient people, making it difficult to find relevant data. In order to address these issues, we developed a session-based suggestion model that proposes concepts as a "you might also be interested in" function, by taking the user's previous queries into account. Our model can be applied to incrementally generate suggestions in interactive search. It can be used for query expansion, and in general to guide users in the exploration of possibly complex spaces of data categories. Our model is based on a concept co-occurrence graph that describes how frequently concepts are searched together in search sessions. Starting from an ontological domain representation, we generated the graph by analyzing the query log of a major search engine. Moreover, we identified clusters of ontology concepts which frequently co-occur in the sessions of the log via community detection on the graph. The evaluation of our model provided satisfactory accuracy results.
