基于知识库的移动个性化旅行伴侣开发方法

Paulo Figueiras, R. Costa, P. Maló, Luka Bradesko, Mitja Jermol
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引用次数: 2

摘要

如今,随着智能手机和平板电脑在市场上的普及,几乎每个人都可以使用移动设备,这提供了更好的处理能力,并获得新的信息和服务。考虑到这一点,对新的和更个性化的服务的需求正在增加,这些服务可以帮助用户完成日常任务。为了满足这种需求,目前的导航系统在几个方面缺乏,即实时准确的城市交通信息,个性化系统所使用的信息的可能性,以及设备和旅行者之间更动态和用户友好的交互需求。简而言之,当前的导航系统设计得不够智能,无法有效地将它们的需求传达给人类,它们也不够智能,无法理解人类为满足这些需求所做的努力。本文提出了一种开发个性化移动知识库的方法,用于移动旅行伴侣应用程序,其会话交互众包有关世界的信息,目的是为用户提供高度具体的帮助和建议。这里介绍的工作仍然是欧盟FP7 MobiS项目目前正在进行的工作的一部分。到目前为止取得的结果并不能解决项目的最终结论,但构成了MobiS领域知识和MobiS数据模型形式化的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge base approach for developing a mobile personalized travel companion
Nowadays with the proliferation of a panoply of smartphones and tablets on the market, almost everyone has access to mobile devices, which offers better processing capabilities and access to new information and services. Taking this into account the demand for new and more personalized services which aid users in their daily tasks is increasing. Current navigation systems lack in several ways in order to satisfy such demand, namely, accurate information about urban traffic in real-time, possibility to personalize the information used by such systems and also the need to have a more dynamic and user friendly interaction between device and traveler. In short, the current navigation systems were not designed to be intelligent enough to communicate their needs effectively to humans, and they are not intelligent enough to understand humans' attempts to satisfy those needs. This paper presents an approach for developing a personalized mobility knowledge base to be used by a mobile travelling companion application, whose conversational interactions crowdsource information about the world for the purpose of providing highly specific assistance and recommendations to the user. The work presented here, is still part of on-going work currently addressed under the EU FP7 MobiS project. Results achieved so far do not address the final conclusions of the project but form the basis for the formalization of the MobiS domain knowledge along with the MobiS data models.
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