Global machine learning for spatial ontology population
Global machine learning for spatial ontology population
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DOI：
10.1016/j.websem.2014.06.001
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发表时间：
2015-01-01
影响因子：
2.5
通讯作者：
Moens, Marie-Francine
中科院分区：
文献类型：
--
作者：
Kordjamshidi, Parisa;Moens, Marie-Francine
理解空间语言在地理信息系统、人机交互或文本到场景转换等许多应用中都很重要。由于空间本体设计的挑战，从自然语言中提取空间信息仍然需要放置在一个定义良好的框架中。在这项工作中，我们提出了一个本体，它在自然语言中的认知语言空间概念和多个定性空间表示和推理模型之间架起桥梁。为了在自然语言和空间本体之间建立映射，我们提出了一种新的本体总体机器学习框架。在这个框架中，我们考虑了源于概念和空间语言结构之间的本体论关系的关系特征和背景知识。所提出的全局学习模型的优点是推理的可扩展性，以及使用任意语义标签自动描述文本的灵活性，这些标签形成了其内容的结构化本体表示。机器学习框架使用来自空间角色标注任务的SemEval-2012和SemEval-2013数据进行评估。(C) 2014 Elsevier B.V.版权所有
Understanding spatial language is important in many applications such as geographical information systems, human computer interaction or text-to-scene conversion. Due to the challenges of designing spatial ontologies, the extraction of spatial information from natural language still has to be placed in a well-defined framework. In this work, we propose an ontology which bridges between cognitive-linguistic spatial concepts in natural language and multiple qualitative spatial representation and reasoning models. To make a mapping between natural language and the spatial ontology, we propose a novel global machine learning framework for ontology population. In this framework we consider relational features and background knowledge which originate from both ontological relationships between the concepts and the structure of the spatial language. The advantage of the proposed global learning model is the scalability of the inference, and the flexibility for automatically describing text with arbitrary semantic labels that form a structured ontological representation of its content. The machine learning framework is evaluated with SemEval-2012 and SemEval-2013 data from the spatial role labeling task. (C) 2014 Elsevier B.V. All rights reserved.