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In the context of geographic information systems (GIS), points of interest (POIs) are descriptions that denote geographical locations which might be of interest for some user purposes. Examples are public transport facilities, historical buildings, hotels and restaurants, recreation areas, hospitals etc. Because information gathering with respect to POIs is usually resource consuming, the user community is often involved in this task. In general, POI data originate from different sources (or users) and are therefore vulnerable to imperfections which might have a negative impact on data quality. Different POIs referring to, or describing the same physical geographical location might exist. Such POIs are said to be coreferent POIs. Coreferent POIs must be avoided as they could harm the data(base) quality and integrity. In this chapter, a novel soft computing technique for the (semi-)automated cleansing of POI databases is proposed. The proposed technique consists of two consecutive main steps: the detection of collections of coreferent POIs and the fusion, for each collection, of all coreferent POIs into a single consistent POI that represents all the POIs in the collection. The technique is based on fuzzy set theory, whereas possibility theory is used to cope with the uncertainties in the data. It can be used as a component of (semi-)automated data quality improvement strategies for databases and other information sources.

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