Semantic typing of linked geoprocessing workflows

In Geographic Information Systems(GIS),geoprocessing workflows allow analysts to organize their methods on spatial data in complex chains.We propose a method for expressing workflows as linked data,and for semi-automatically enriching them with semantics on the level of their operations and datasets.Linked workflows can be easily published on the Web and queried for types of inputs,results,or tools.Thus,GIS analysts can reuse their workflows in a modular way,selecting,adapting,and recommending resources based on compatible semantic types.Our typing approach starts from minimal annotations of workflow operations with classes of GIS tools,and then propagates data types and implicit semantic structures through the workflow using an OWL typing scheme and SPARQL rules by backtracking over GIS operations.The method is implemented in Python and is evaluated on two real-world geoprocessing workflows,generated with Esri's ArcGIS.To illustrate the potential applications of our typing method,we formulate and execute competency questions over these workflows.

Geo-analytical question-answering with GIS

Question Answering(QA),the process of computing valid answers to questions formulated in natural language,has recently gained attention in both industry and academia.Translating this idea to the realm of geographic information systems(GIS)may open new opportunities for data scientists.In theory,analysts may simply ask spatial questions to exploit diverse geographic information resources,without a need to know how GIS tools and geodata sets interoperate.In this outlook article,we investigate the scientific challenges of geo-analytical question answering,introducing the problems of unknown answers and indirect QA.Furthermore,we argue why core concepts of spatial information play an important role in addressing this challenge,enabling us to describe analytic potentials,and to compose spatial questions and workflows for generating answers.