面向地理场景的“文—图—景”转换方法研究
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摘要
自然语言和虚拟地理场景是两种重要的地理场景表达手段,在数据的丰富性、表达的直观性、认知的习惯性、应用的普及性等方面各有所长。如何将具有丰富语义的自然语言信息转换到虚拟地理场景中,已成为地理信息学科的热点研究问题。本文依托国家自然科学基金项目“面向自然语言的虚拟地理场景重构方法”,探索自然语言中地理场景信息“结构化、图形化、空间化”过程中的表达机制,实现地理场景从文本到图形和虚拟场景的语义转换。本文的主要研究内容如下：
(1)地理场景自然语言描述的结构化表达
通过建立地理场景的自然语言描述实例库,分析地理要素和语义的自然语言描述特点,应用框架知识表示法,从“地理要素、空间关系、地理场景结构”三个层次进行地理场景的结构化表达,解决地理场景的自然语言描述与虚拟场景表达的之间数据异构性问题。框架知识表示法不仅能够表达地理场景结构特征,而且保留了地理场景各个地理要素间的语义关联信息。
(2)知识库驱动的地理场景“文—图”转换
空间布局图不仅能够表达出地理要素分布的结构特征,而且反映地理场景各地理要素之间的定性语义关系,是一种比自然语言更加直观的地理场景表达方式。通过构建地理场景知识库,包括地理场景概念分类体系、地理常识库、地理场景符号库和地理场景结构模板库等,解决地理场景自然语言描述的语义一致性问题；顾及空间关系语义约束,实现了地理要素的空间“粗布局”；提出了基于蚁群算法的地理场景空间布局优化方法,形成人机辅助的地理场景“文—图”转换机制。
(3)基于相似性匹配的地理场景的“文—景”转换
通过地理概念相似度、空间关系相似度和空间结构相似度计算,实现了基于多层次空间相似性匹配的自然语言与虚拟地理场景转换方法。实验结果表明,地理场景的自然语言描述与虚拟地理场景在表达方式上存在较大差异,两者之间的转换实际上是一个寻找空间和语义相似解的过程。但是,自然语言地理场景结构特征的描述信息较为匮乏,借助地理场景的空间布局图,能够更加有效地实现地理场景的“文—景”转换。
Natural language and virtual geographical scene, as two important representation types of geographic scene, have different advantages in the aspects such as the abundance of data, the intuitive expression, cognitive habits, and the popularity of applications. It has become a hot research issue in geographic information science on how to bridge the gap between natural language and virtual geographical scene. This paper supported by China National Natural Science Foundation "Reconstruction of Virtual Geographical Scene in Natural Language", aims to solve the expressing mechanism of geographical scene in natural language from the aspect of structured text, spatial sketch and virtual geographical scene, in order to reveal the natural language description habits of geographic scene, and achieve the qualitative to quantitative conversion of geographical scene. This study will further promote the universal application of virtual geographical scene in geography, smart city, film production, visualization of historical events etc. The main research topics and conclusions in this dissertation are discussed as follows:
(1) Structured representation frame of geographic scene in natural language
Based on analysis of the expression methods of geographic scene features in virtual geographical scene and natural language, the structured model is presented with frame knowledge representation method on three levels i.e. geographical feature, spatial relation, geographical scene. Frame knowledge representation could not only describe the spatial structure features of geographic scene, but also identify the semantic relation between the features.
(2)"Text to Sketch" conversion with geographical knowledge bases
Spatial layout sketch of geographic scene could not only express the distribution of geographical features in the scene, but also reflect the qualitative semantic relations between geographic features in the scene. Hence it's a more intuitive and visual way to represent geographical scene than natural language. The knowledge bases of geographic scene are explored including:geographical feature classification scheme, geographical common knowledge base, and geographical entity symbol base and spatial structure templates bases of geographical scene. The spatial relation semantic models are introduced to solve the problem of rough spatial layout of geographic scene. Then, the colony algorithm is developed to implement precise spatial layout, in order to realize conversion of text and sketch of geographic scene with human-computer assistance.
(3)"Text to Scene" conversion of geographical scene bases spatial similarity mapping
Based on the multi-level spatial similarity matching, a conversion method of virtual geographical scene and the geographical scene described in natural language is proposed, of which the similarity calculation of geographical concepts, spatial relation and spatial structure are proposed, and some cases are illustrated to evaluate the performance. The study demonstrates that there are great differences between natural language and virtual geographic scene, and their conversion is to find spatial semantic similarity essentially. Spatial layout sketch just provides distinct spatial structure features of geographical scene which are hidden described in natural language. Based on the spatial layout sketch,"Text to Scene" conversion can be more effectively implemented.
(1)地理场景自然语言描述的结构化表达
通过建立地理场景的自然语言描述实例库,分析地理要素和语义的自然语言描述特点,应用框架知识表示法,从“地理要素、空间关系、地理场景结构”三个层次进行地理场景的结构化表达,解决地理场景的自然语言描述与虚拟场景表达的之间数据异构性问题。框架知识表示法不仅能够表达地理场景结构特征,而且保留了地理场景各个地理要素间的语义关联信息。
(2)知识库驱动的地理场景“文—图”转换
空间布局图不仅能够表达出地理要素分布的结构特征,而且反映地理场景各地理要素之间的定性语义关系,是一种比自然语言更加直观的地理场景表达方式。通过构建地理场景知识库,包括地理场景概念分类体系、地理常识库、地理场景符号库和地理场景结构模板库等,解决地理场景自然语言描述的语义一致性问题；顾及空间关系语义约束,实现了地理要素的空间“粗布局”；提出了基于蚁群算法的地理场景空间布局优化方法,形成人机辅助的地理场景“文—图”转换机制。
(3)基于相似性匹配的地理场景的“文—景”转换
通过地理概念相似度、空间关系相似度和空间结构相似度计算,实现了基于多层次空间相似性匹配的自然语言与虚拟地理场景转换方法。实验结果表明,地理场景的自然语言描述与虚拟地理场景在表达方式上存在较大差异,两者之间的转换实际上是一个寻找空间和语义相似解的过程。但是,自然语言地理场景结构特征的描述信息较为匮乏,借助地理场景的空间布局图,能够更加有效地实现地理场景的“文—景”转换。
Natural language and virtual geographical scene, as two important representation types of geographic scene, have different advantages in the aspects such as the abundance of data, the intuitive expression, cognitive habits, and the popularity of applications. It has become a hot research issue in geographic information science on how to bridge the gap between natural language and virtual geographical scene. This paper supported by China National Natural Science Foundation "Reconstruction of Virtual Geographical Scene in Natural Language", aims to solve the expressing mechanism of geographical scene in natural language from the aspect of structured text, spatial sketch and virtual geographical scene, in order to reveal the natural language description habits of geographic scene, and achieve the qualitative to quantitative conversion of geographical scene. This study will further promote the universal application of virtual geographical scene in geography, smart city, film production, visualization of historical events etc. The main research topics and conclusions in this dissertation are discussed as follows:
(1) Structured representation frame of geographic scene in natural language
Based on analysis of the expression methods of geographic scene features in virtual geographical scene and natural language, the structured model is presented with frame knowledge representation method on three levels i.e. geographical feature, spatial relation, geographical scene. Frame knowledge representation could not only describe the spatial structure features of geographic scene, but also identify the semantic relation between the features.
(2)"Text to Sketch" conversion with geographical knowledge bases
Spatial layout sketch of geographic scene could not only express the distribution of geographical features in the scene, but also reflect the qualitative semantic relations between geographic features in the scene. Hence it's a more intuitive and visual way to represent geographical scene than natural language. The knowledge bases of geographic scene are explored including:geographical feature classification scheme, geographical common knowledge base, and geographical entity symbol base and spatial structure templates bases of geographical scene. The spatial relation semantic models are introduced to solve the problem of rough spatial layout of geographic scene. Then, the colony algorithm is developed to implement precise spatial layout, in order to realize conversion of text and sketch of geographic scene with human-computer assistance.
(3)"Text to Scene" conversion of geographical scene bases spatial similarity mapping
Based on the multi-level spatial similarity matching, a conversion method of virtual geographical scene and the geographical scene described in natural language is proposed, of which the similarity calculation of geographical concepts, spatial relation and spatial structure are proposed, and some cases are illustrated to evaluate the performance. The study demonstrates that there are great differences between natural language and virtual geographic scene, and their conversion is to find spatial semantic similarity essentially. Spatial layout sketch just provides distinct spatial structure features of geographical scene which are hidden described in natural language. Based on the spatial layout sketch,"Text to Scene" conversion can be more effectively implemented.
引文
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