PAVAL validation data: Paval: A Location Aware Virtual Personal Assistant for re-trieving geolocated Points of Interest and Location-Based Services

Along with the recent ICT developments and the increasing growth of Internet and social media, we are currently experiencing an expanding information overflow, often resulting in a lack of focus on the users’ specific needs. Moreover, due to the increasing use of mobile devices, users’ information requirements are moving their focus towards local, georeferenced information, especially on the move. Several solutions have been proposed to meet these new emerging trends in assisting users and satisfying their needs and preferences, such as Virtual Assistants and Location Aware Recommender System (LARS), both in commercial and research literature. However, general purpose virtual assistants have to manage usually large domains, dealing with big amounts of data and online resources, loosing focus on more specific requirements and local information. On the other hand, traditional recommender systems are based on filtering techniques and contextual knowledge, and they usually do not rely on Natural Language Processing (NLP) features on users’ queries, which are useful to understand and contextualize users’ necessities on the spot. Therefore, comprehending the actual users’ information needs and other key information that can be included in the user query, such as geographical references, is a challenging task which has not yet been fully accomplished by current state of the art solutions. In this paper, we propose Paval (Location Aware Virtual Personal Assistant), a semantic assisting engine for suggesting local Points of Interests (POIs) and services by analyzing users’ natural language queries, in order to estimate the information need and potential geographic references expressed by the users. The system exploits NLP and semantic techniques providing as output recommendations on local geolocated POIs and services which best match the users’ requests, retrieved by querying our semantic Km4City Knowledge Base. The proposed solution has been validated against the most popular virtual assistants, such as such as Google Assistant, Apple Siri and Microsoft Cortana, focusing the assessment on the request of geolocated POIs and services, showing very promising capabilities in successfully estimating the users’ information needs and multiple geographic ref