Geographic knowledge extraction and semantic similarity in OpenStreetMap

• Published in: Knowledge and information systems Vol. 37; no. 1; pp. 61 - 81
• Main Authors: Ballatore, Andrea, Bertolotto, Michela, Wilson, David C.
• Format: Journal Article
• Language: English
• Published: London Springer London 01-10-2013; Springer; Springer Nature B.V
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Bookstores; Cartography; Citations; Cocitation; Collaboration; Computation; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Crowdsourcing; Data mining; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Database Management; Exact sciences and technology; Freeware; Geographic information systems; Information management; Information resources; Information retrieval; Information Storage and Retrieval; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Information systems. Data bases; IT in Business; Knowledge; Memory organisation. Data processing; Networks; Recommender systems; Regular Paper; Semantic web; Semantics; Similarity; Software; Spatial data; Speech and sound recognition and synthesis. Linguistics; Studies; Web 2.0; Websites; United States > US; Volunteered Geographic Information; Crowdsourcing; OpenStreetMap; Co-citation; Semantic similarity; SimRank; OSM Semantic Network; P-Rank; Similarity; Semantic analysis; Very large databases; Data mining; Information source; Semantics; World wide web; Knowledge discovery; Semantic relation; Data analysis; Content management; Knowledge representation; Information retrieval; Social network; Knowledge extraction; Recommendation; Volunteering; Geographic information system; Semantic network; Metric; Internet; Web site; Usability; Signal to noise ratio
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-012-0571-0

In recent years, a web phenomenon known as Volunteered Geographic Information (VGI) has produced large crowdsourced geographic data sets. OpenStreetMap (OSM), the leading VGI project, aims at building an open-content world map through user contributions. OSM semantics consists of a set of properties (called ‘tags’) describing geographic classes, whose usage is defined by project contributors on a dedicated Wiki website. Because of its simple and open semantic structure, the OSM approach often results in noisy and ambiguous data, limiting its usability for analysis in information retrieval, recommender systems and data mining. Devising a mechanism for computing the semantic similarity of the OSM geographic classes can help alleviate this semantic gap. The contribution of this paper is twofold. It consists of (1) the development of the OSM Semantic Network by means of a web crawler tailored to the OSM Wiki website; this semantic network can be used to compute semantic similarity through co-citation measures, providing a novel semantic tool for OSM and GIS communities; (2) a study of the cognitive plausibility (i.e. the ability to replicate human judgement) of co-citation algorithms when applied to the computation of semantic similarity of geographic concepts. Empirical evidence supports the usage of co-citation algorithms—SimRank showing the highest plausibility—to compute concept similarity in a crowdsourced semantic network.