Locations that exhibit a certain interest or serve a certain purpose are commonly referred to as Points of Interest (POIs). The concept of a POI is quite broad, encompassing anything from a shop, restaurant or museum to an ATM or bus stop. POI data are the cornerstone of any application, service, and product even remotely related to our physical surroundings. The creation, update, and provision of POI datasets consists a multi-billion cross-domain and cross-border industry, with a value chain natively incorporating most domains of our economy, from mobility and tourism, to logistics and manufacturing. Advances in the timely and accurate provision of POIs result into significant direct and indirect gains throughout our economy. Productivity gains, optimization of value chains, match-making consumers with goods and service providers, new value added products, are just a few examples. POI data are truly one of the foundations and value multipliers of our Digital Economy.
The value and impact of POIs is reflected in the complex, expensive and labor-intensive effort required for their production and maintenance, which inherently involves stakeholders and users throughout their value chain. Their initial production involves field-work, constant monitoring for their evolution and accuracy, integration of user-feedback mechanisms for reporting errors, quality assurance of new data, and roll-out across a plethora of services and products. In the POI market, the competitive advantages of data providers are clear and measurable: the greater the size, timeliness, richness, and accuracy of data, the better. The value chain of POI data has rapidly changed, with new data sources of even greater volume and heterogeneity, introducing opportunities for growth, but also complexity, intensifying the challenges for the quality-assured integration, enrichment, and data sharing of POIs.
POI data are by nature semantically diverse and spatiotemporally evolving, representing different entities and associations depending on their geographical, temporal, and thematic context. Due to their use in various domains and contexts, POI data is typically found in diverse, heterogeneous sources, from which bits and pieces of information need to be combined and assembled to increase value. However, this is hindered by the lack of common identifiers and data sharing formats. Even the means by which we typically identify and share POIs is inherently ambiguous. As a result, the integration of POI data remains labor-intensive and scalable only for domain-specific or small-scale efforts, leading to loss of information and thus lost value.
SLIPO’s objective is to deliver the missing technologies for addressing the data integration challenges of POI data in terms of coverage, timeliness, accuracy, and richness. In SLIPO, we argue that Linked Data technologies can address the limitations, gaps and challenges of the current landscape in integrating, enriching, and sharing POI data. Our goal is to transfer the research output generated by our work in project GeoKnow, to the specific challenge of POI data, introducing validated and cost-effective innovations across their value chain.
