Generating descriptions that summarize geospatial and temporal data

Effective data summarization methods that use AI techniques can help humans understand large sets of data. In this paper, we describe a knowledge-based method for automatically generating summaries of geospatial and temporal data, i.e. data with geographical and temporal references. The method is useful for summarizing data streams, such as GPS traces and traffic information, that are becoming more prevalent with the increasing use of sensors in computing devices. The method presented here is an initial architecture for our ongoing research in this domain. In this paper we describe the data representations we have designed for our method, our implementations of components to perform data abstraction and natural language generation. We also discuss evaluation results that show the ability of our method to generate certain types of geospatial and temporal descriptions.

Using Open Geographic Data to Generate Natural Language Descriptions for Hydrological Sensor Networks

Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available on the Web (such as OpenStreetMap, Geonames, or DBpedia) to automatically construct such descriptions. We present a general method that uses such information to generate sensor descriptions in natural language. The results of the evaluation of our method in a hydrologic national sensor network showed that this approach is feasible and capable of generating adequate sensor descriptions with a lower development effort compared to other approaches. In the paper we also analyze certain problems that we found in public databases (e.g., heterogeneity, non-standard use of labels, or rigid search methods) and their impact in the generation of sensor descriptions.