Enabling semantic search and knowledge discovery for ArcGIS Online: A linked-data-driven approach

Abstract

Abstract ArcGIS Online is a unified Web portal designed by Environment System Research Institute (ESRI). It contains a rich collection of Web maps, layers, and services contributed by GIS users throughout the world. The metadata about these GIS resources reside in data silos that can be accessed via a Web API. While this is sufficient for simple syntax-based searches, it does not support more advanced queries, e.g., finding maps based on the semantics of the search terms, or perform-ing customized queries that are not pre-designed in the API. In metadata, titles and descriptions are commonly available attributes which provide important information about the content of the GIS resources. However, such data cannot be easily used since they are in the form of unstructured natural language. To address these diffi-culties, we combine data-driven techniques with theory-driven approaches to enable semantic search and knowledge discovery for ArcGIS Online. We develop an on-tology for ArcGIS Online data, convert the metadata into Linked Data, and enrich the metadata by extracting thematic concepts and geographic entities from titles and descriptions. Based on a human participant experiment, we calibrate a linear regres-sion model for semantic search, and demonstrate the flexible queries for knowledge discovery that are not possible in the existing Web API. While this research is based on the ArcGIS Online data, the presented methods can also be applied to other GIS cloud services and data infrastructures