MAWI: Mapping the Unmapped in Wikipedia via Geographic Information Extraction

Geographic Information Extraction (GIE) involves the extraction of geo-referenced information from a data collection through steps of geoparsing and geocoding. The former is a process that starts from a free textual description of locations with the goal of identifying an unambiguous location, such as specific geographic coordinates expressed as latitude-longitude. Differently, geocoding regards the easier task of translating an exact and well-formatted location such as postal addresses. This paper presents MAWI, i.e. a pipeline that starts from generic texts about cities that first extracts geographic information to automatically detect possible points of interest, then generates textual snippets from their contexts by means of Natural Language Processing (NLP) techniques. The adopted methodology involves several modules, ranging from publicly available geocoding systems to NLP libraries for Named Entity Recognition and text segmentation. In this contribution, we present an experimentation with 50 input Wikipedia pages referring different cities, demonstrating its effectiveness with a running example and evaluating its power to detect and structure a highly-significant amount of novel geo-referenced information with respect to what currently encoded in Wikipedia. Data and code are publicly available for future research at https://anonymous.4open.science/r/PointOfInterest-8D80/ .