一种基于模糊逻辑的文本中模糊不确定空间表达式的高效检索方法
A Fuzzy Logic based Method for Efficient Retrieval of Vague and Uncertain Spatial Expressions in Text Exploiting the Granulation of the Spatial Event Queries
摘要：
n维空间中的事物的排列被指定为空间。空间数据由表示地球表面上物体和区域的位置和形状的值组成。空间信息包括地物位置、地理地物关系和地理地物测量等事实。空间认知是机器人学、心理学、地球科学、地理学、政治学、地理经济学、环境、采矿和石油工程、自然资源、流行病学、人口统计学等各种其他领域的主要研究领域，任何包含实际位置说明的文本文件，如地名、地理坐标、地标、国名等，应该包含空间信息。空间信息也可以使用模糊或模糊描述来表示，所述模糊或模糊描述涉及语言学术语，诸如接近、远离、在......以东、非常接近。给定一个涉及事件的查询，这项正在进行的研究工作的目的是从多个文本文档中提取相关信息，解决不确定性和不确定性，并将它们转换为地图中的位置。系统的输入将是文本语料库和空间查询事件。该系统的输出是一个地图，显示了最可能的，消除歧义的位置的事件查询。作者提出了模糊逻辑技术来解决空间表达式中的不确定性。
英文摘要：
The arrangement of things in n-dimensional space is specified as Spatial. Spatial data consists of values that denote the location and shape of objects and areas on the earths surface. Spatial information includes facts such as location of features, the relationship of geographic features and measurements of geographic features. The spatial cognition is a primal area of study in various other fields such as Robotics, Psychology, Geosciences, Geography, Political Sciences, Geographic Economy, Environmental, Mining and Petroleum Engineering, Natural Resources, Epidemiology, Demography etc., Any text document which contains physical location specifications such as place names, geographic coordinates, landmarks, country names etc., are supposed to contain the spatial information. The spatial information may also be represented using vague or fuzzy descriptions involving linguistic terms such as near to, far from, to the east of, very close. Given a query involving events, the aim of this ongoing research work is to extract the relevant information from multiple text documents, resolve the uncertainty and vagueness and translate them in to locations in a map. The input to the system would be a text Corpus and a Spatial Query event. The output of the system is a map showing the most possible, disambiguated location of the event queried. The author proposes Fuzzy Logic Techniques for resolving the uncertainty in the spatial expressions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
