The paper gives an account of an infrastructure that will be integrated into a platform aimed at providing a multi-faceted experience to visitors of Northern Greece using mythology as a starting point. This infrastructure comprises a multi-lingual and multi-modal corpus (i.e., a corpus of textual data supplemented with images, and video) that belongs to the humanities domain along with a dedicated database (content management system) with advanced indexing, linking and search functionalities. We will present the corpus itself focusing on the content, the methodology adopted for its development, and the steps taken towards rendering it accessible via the database in a way that also facilitates useful visualizations. In this context, we tried to address three main challenges: (a) to add a novel annotation layer, namely geotagging, (b) to ensure the long-term maintenance of and accessibility to the highly heterogeneous primary data – even after the life cycle of the current project – by adopting a metadata schema that is compatible to existing standards; and (c) to render the corpus a useful resource to scholarly research in the digital humanities by adding a minimum set of linguistic annotations.

Explanations for algorithmically generated recommendations is an important requirement for transparent and trustworthy recommender systems. When the internal recommendation model is not inherently interpretable (e.g., most contemporary systems are complex and opaque), or when access to the system is not available (e.g., recommendation as a service), explanations have to be generated post-hoc, i.e., after the system is trained. In this common setting, the standard approach is to provide plausible interpretations of the observed outputs of the system, e.g., by building a simple surrogate model that is inherently interpretable, and explaining that model. This however has several drawbacks. First, such explanations are not truthful, as they are rationalizations of the observed inputs and outputs constructed by another system. Second, there are privacy concerns, as to train a surrogate model, one has to know the interactions from users other than the one who seeks an explanation. Third, such explanations may not be scrutable and actionable, as they typically return weights for items or other users that are difficult to comprehend, and hard to act upon so to improve the quality of one’s recommendations.

In this paper, we introduce an approach for improving the quality of the geocoding process. Geocoding refers to the procedure of mapping an address of textual form to a pair of accurate spatial coordinates. While there is a variety of available geocoders, both open source and commercial, that curate this mapping in either a semi-automated or fully-automated way, there is no one-size-fits-all system. Depending on the underlying algorithm of each geocoder, its output may be very accurate for some addresses, districts or countries, while failing to properly locate some others. Given that, our setup can be thought of as a meta-geocoding pipeline, built on top of the available geocoders. We propose a machine learning approach, which, given an address and a sequence of coordinate pairs suggested by standalone geocoders, it is able to identify the most accurate one. In order to achieve this, we formulate the task as a multi-class classification problem and introduce a series of domain specific training features, capturing essential information about each coordinate pair suggestion, as well as computing comparative metrics among different suggestions. These features are fed into several classification algorithms and are evaluated on a proprietary address dataset of a geo-marketing company. Furthermore, we present LGM-GC, a QGIS plugin, which provides the functionality of our approach through a user-friendly interface.

An important task on land registration processes is to be able to determine the prevalent data provenance for a finalized polygon that represents a cadastral parcel, since the finalized polygon is derived by the examination of a set of initial polygons, drawn from several individual registers (databases). These registers might contain different, partially similar or conflicting information regarding the ownership, usage and polygon geometry of a cadastral parcel. In such cases, the cadastration expert either select one of of the initial geometries, or (in cases none of the initial accurately represents the finalized land parcel) creates a new geometry. Maintaining this provenance information is of high importance for further cadastration and validation/quality assessment processes; however, due to the gradual and long lasting nature of cadastration procedures, this information is absent from large parts of cadastral databases. In this paper, we present an approach for effectively classifying such land parcel polygons with respect to their provenance information. We propose a method that can produce highly accurate provenance recommendations based only on attributes derived from the geometry of a land parcel. In particular, we implement a set of spatial training features, capturing polygon properties and relations. These features are fed into several classification algorithms and are evaluated on a proprietary dataset of a cadastration company.

Interlinking spatio-textual data comprises a core problem within the research literature, as well as a task of high practical importance in a plethora of industrial applications involving GIS systems. In its general form, it consists in identifying, between two sources of spatio-texual entities, pairs of entities that match, i.e. correspond to the same real-world entities. In this paper, we focus on interlinking spatio-textual entities based solely on their name, that is we handle the problem of toponym interlinking. To solve the problem, works in the literature exploit generic string similarity measures and either apply them as is, or integrate them as training features in classification models, without adapting/extending them based on the specific characteristics of toponyms. In this work, we showcase that domain knowledge can significantly improve the accuracy of toponym interlinking, by proposing domain specific similarity measures that take into account specificities of toponyms. We assess the implemented measures on Geonames and demonstrate significant increases in interlinking accuracy compared to baseline methods.

Millions of users rely on navigation applications to compute an optimal route for their trips. The basic functionality of these applications is to find the minimum cost route between a source and target node in the transportation network. In this paper, we address a variant of this problem, where the computed route is required to contain a set of Points of Interest of specific types. Our approach is based on the concept of keyword skyline. We formally define this concept, and we show how to compute the keyword skyline for the vertices of a given network and how to use it for computing the shortest keyword covering paths. We present different variants of this method, including an approximation algorithm, providing different trade-offs between preprocessing cost and execution time. Finally, we present an experimental evaluation of our approach using real-world datasets of different sizes, including also a comparison to the current state-of-the-art algorithm for this problem.

Integration of wireless communications in vehicles presents the greatest challenge today in the field of vehicular safety, efficiency and comfort. Data fusion should be able to benefit from this additional source of information. This involves the association and fusion of received vehicle positions and other relevant data with the targets tracked by the onboard sensors' tracking systems. This paper describes a fusion approach for using wireless transmitted data in a vehicle perception system, with focus on data association and tracking methodology. The results were extracted by using two vehicles equipped with wireless communication hardware.