Inferring Spatio-Temporal Trajectories of Entities from Natural Language Documents

这项探索性研究的重点是开发用于在自然语言文本上集成空间和时间注释的算法，从而能够通过空间和时间跟踪实体。这涉及到词汇资源的使用和两种现有注释方案的集成，以创建一个通过空间和时间位置捕获个体运动的表示。这种表示是使用符号和机器学习方法自动从文档中提取的。这项工作建立在最近出现的技术的基础上，这些技术可以解析叙事的时间结构。这些技术使用TimeML标记语言来结合基于规则的系统、机器学习和时间推理，以及一个称为SpatialML的标记方案来将相对位置和绝对位置映射到地理坐标。然后将来自这些方案的数据结构与事件参数的表示相集成。使用捕获动作动词含义的动词词典，就可以捕获由这些动词描述的事件中涉及的参与者的信息。最后，这些标记表示被映射到标准化上模型本体（SUMO）中适当的本体类别。这一探索性研究的结果具有潜在的重要意义，因为迄今为止，将空间信息提取与文本理解的其他方面相结合的研究很少。此外，通过将自然语言文本上的时间和空间注释表示映射到标准化的本体（如SUMO），我们希望为社区提供资源的互操作性，同时也利用本体研究社区所做的工作。

This exploratory research focuses on the development of algorithms for integrating spatial and temporal annotations over natural language text, thereby enabling the tracking of entities through space and time. This involves the use of lexical resources and the integration of two existing annotation schemes to create a representation capturing the movement of individuals through spatial and temporal locations. This representation is extracted automatically from documents using symbolic and machine learning methods.This work builds on technologies that have emerged recently that parse the temporal structure of narratives. These techniques use the TimeML markup language to combine rule-based systems, machine learning, and temporal reasoning, and a markup scheme called SpatialML to map relative and absolute locations to geo-coordinates. Data structures from these schemes are then integrated with a representation of event arguments. Using a verb lexicon that captures the meaning of motion verbs, information about the participants involved in events described by such verbs is captured. Finally, these markup representations are mapped onto the appropriate ontological categories within the Standardized Upper Model Ontology (SUMO). The results of this exploratory research are potentially significant, as there has to date been little research done on integrating spatial information extraction with other aspects of text understanding. Furthermore, by providing a mapping of the representations of temporal and spatial annotations over natural language texts to a standardized ontology such as SUMO, we hope to provide interoperability of resources to the community, while also leveraging the work done within the ontology research community.