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.

中文翻译：

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基于会话的地理探索性主题建议

探索性信息搜索可以挑战用户制定有效的搜索查询。此外，复杂的信息空间（例如由地理信息系统管理的信息空间）会使人们迷失方向，从而难以找到相关数据。为了解决这些问题，我们开发了一个基于会话的建议模型，通过考虑用户之前的查询，将概念作为“您可能也感兴趣”功能提出。我们的模型可用于在交互式搜索中增量生成建议。它可用于查询扩展，通常可用于指导用户探索可能复杂的数据类别空间。我们的模型基于概念共现图，该图描述了在搜索会话中一起搜索概念的频率。从本体域表示开始，我们通过分析主要搜索引擎的查询日志来生成图。此外，我们通过图上的社区检测确定了在日志会话中经常共同出现的本体概念集群。对我们模型的评估提供了令人满意的精度结果。