Reference data enhancement for geographic information retrieval using linked data

• Published in: Transactions in GIS Vol. 21; no. 4; pp. 683 - 700
• Main Authors: Moura, Tiago H. V. M., Davis, Clodoveu A., Fonseca, Frederico T.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01-08-2017
• Subjects: Data; Data processing; Data sources; Digital mapping; Digital media; gazetteer; Gazetteers; geocoding; Geographic information systems; Geographical locations; Information processing; Information retrieval; Internet; knowledge bases; Landmarks; Linked Data; Messages; Neighborhoods; Open systems; Place names; Social media; Social networks; Urban areas; Web services
• ISSN: 1361-1682; 1467-9671
• DOI: 10.1111/tgis.12238

Gazetteers are instrumental in recognizing place names in documents such as Web pages, news, and social media messages. However, creating and maintaining gazetteers is still a complex task. Even though some online gazetteers provide rich sets of geographic names in planetary scale (e.g. GeoNames), other sources must be used to recognize references to urban locations, such as street names, neighborhood names or landmarks. We propose integrating Linked Data sources to create a gazetteer that combines a broad coverage of places with urban detail, including content on geographic and semantic relationships involving places, their multiple names and related non‐geographic entities. Our final goal is to expand the possibilities for recognizing, disambiguating and filtering references to places in texts for geographic information retrieval (GIR) and related applications. The resulting ontological gazetteer, named LoG (Linked OntoGazetteer), is accessible through Web services by applications and research initiatives on GIR, text processing, named entity recognition and others. The gazetteer currently contains over 13 million places, 140 million attributes and relationships, and 4.5 million non‐geographic entities. Data sources include GeoNames, Freebase, DBPedia and LinkedGeoData, which is based on OpenStreetMap data. An analysis on how these datasets overlap and complement one another is also presented.