Andrea Ballatore

Geographic knowledge extraction and semantic similarity in OpenStreetMap

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.

Semantically enriching VGI in support of implicit feedback analysis

In recent years, the proliferation of Volunteered Geographic Information (VGI) has enabled many Internet users to contribute to the construction of rich and increasingly complex spatial datasets. This growth of geo-referenced information and the often loose semantic structure of such data have resulted in spatial information overload. For this reason, a semantic gap has emerged between unstructured geo-spatial datasets and high-level ontological concepts. Filling this semantic gap can help reduce spatial information overload, therefore facilitating both user interactions and the analysis of such interaction. Implicit Feedback analysis is the focus of our work. In this paper we address this problem by proposing a system that executes spatial discovery queries. Our system combines a semantically-rich and spatially-poor ontology (DBpedia) with a spatially-rich and semantically-poor VGI dataset (OpenStreetMap). This technique differs from existing ones, such as the aggregated dataset LinkedGeoData, as it is focused on user interest analysis and takes map scale into account. System architecture, functionality and preliminary results gathered about the system performance are discussed.

A Conceptual Quality Framework for Volunteered Geographic Information

The assessment of the quality of volunteered geographic information VGI is cornerstone to understand the fitness for purpose of datasets in many application domains. While most analyses focus on geometric and positional quality, only sporadic attention has been devoted to the interpretation of the data, i.e., the communication process through which consumers try to reconstruct the meaning of information intended by its producers. Interpretability is a notoriously ephemeral, culturally rooted, and context-dependent property of the data that concerns the conceptual quality of the vocabularies, schemas, ontologies, and documentation used to describe and annotate the geographic features of interest. To operationalize conceptual quality in VGI, we propose a multi-faceted framework that includes accuracy, granularity, completeness, consistency, compliance, and richness, proposing proxy measures for each dimension. The application of the framework is illustrated in a case study on a European sample of OpenStreetMap, focused specifically on conceptual compliance.

Computing the semantic similarity of geographic terms using volunteered lexical definitions

Volunteered geographic information (VGI) is generated by heterogenous ‘information communities’ that co-operate to produce reusable units of geographic knowledge. A consensual lexicon is a key factor to enable this open production model. Lexical definitions help demarcate the boundaries of terms, forming a thin semantic ground on which knowledge can travel. In VGI, lexical definitions often appear to be inconsistent, circular, noisy and highly idiosyncratic. Computing the semantic similarity of these ‘volunteered lexical definitions’ has a wide range of applications in GIScience, including information retrieval, data mining and information integration. This article describes a knowledge-based approach to quantify the semantic similarity of lexical definitions. Grounded in the recursive intuition that similar terms are described using similar terms, the approach relies on paraphrase-detection techniques and the lexical database WordNet. The cognitive plausibility of the approach is evaluated in the context of the OpenStreetMap (OSM) Semantic Network, obtaining high correlation with human judgements. Guidelines are provided for the practical usage of the approach.

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing ‘geographic intelligence’ in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users’ spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geoknowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects.

RecoMap: an interactive and adaptive map-based recommender

With the growing availability of geo-referenced information on the Web, the problem of spatial information overload has attracted interest both in the commercial and academic world. In order to tackle this issue, personalisation techniques can be used to tailor spatial contents based upon user interests. RecoMap, the system described in this paper, deducts user interests by monitoring user interaction and context to provide personalised spatial recommendations. After an overview of existing recommendation systems within the geospatial domain, the novel approach adopted by RecoMap to produce such recommendations is described. A case study related to a university campus setting is used to outline an application of this technique. Details of the implementation and initial testing of this prototype are provided.

Conceptualising the geographic world: the dimensions of negotiation in crowdsourced cartography

In crowdsourced cartographic projects, mappers coordinate their efforts through online tools to produce digital geospatial artefacts, such as maps and gazetteers, which were once the exclusive territory of professional surveyors and cartographers. In order to produce meaningful and coherent data, contributors need to negotiate a shared conceptualisation that defines the domain concepts, such as road, building, train station, forest and lake, enabling the communication of geographic knowledge. Considering the OpenStreetMap Wiki website as a case study, this article investigates the nature of this negotiation, driven by a small group of mappers in a context of high contribution inequality. Despite the apparent consensus on the conceptualisation, the negotiation keeps unfolding in a tension between alternative representations, which are often incommensurable, i.e., hard to integrate and reconcile. In this study, we identify six complementary dimensions of incommensurability that recur in the negotiation: (1) ontology, (2) cartography, (3) culture and language, (4) lexical definitions, (5) granularity, and (6) semantic overload and duplication.

Good location, terrible food: detecting feature sentiment in user-generated reviews

A growing corpus of online informal reviews is generated every day by non-experts, on social networks and blogs, about an unlimited range of products and services. Users do not only express holistic opinions, but often focus on specific features of their interest. The automatic understanding of “what people think” at the feature level can greatly support decision making, both for consumers and producers. In this paper, we present an approach to feature-level sentiment detection that integrates natural language processing with statistical techniques, in order to extract users’ opinions about specific features of products and services from user-generated reviews. First, we extract domain features, and each review is modelled as a lexical dependency graph. Second, for each review, we estimate the polarity relative to the features by leveraging the syntactic dependencies between the terms. The approach is evaluated against a ground truth consisting of set of user-generated reviews, manually annotated by 39 human subjects and available online, showing its human-like ability to capture feature-level opinions.

The web will kill them all: new media, digital utopia, and political struggle in the Italian 5-Star Movement

This article examines the role of discourses about new media technology and the web in the rise of the 5-Star Movement (Movimento 5 Stelle, or M5S) in Italy. Founded by comedian and activist Beppe Grillo and web entrepreneur Gianroberto Casaleggio in 2009, this movement succeeded in becoming the second largest party at the 2013 national elections in Italy. This article aims to discuss how elements of digital utopia and web-centric discourses have been inserted into the movement’s political message, and how the construction of the web as a myth has shaped the movement’s discourse and political practice. The 5-Star Movement is compared and contrasted with other social and political movements in western countries which have displayed a similar emphasis on new media, such as the Occupy movement, the Indignados movement, and the Pirate Parties in Sweden and Germany. By adopting and mutating cyber-utopian discourses from the so-called Californian ideology, the movement symbolically identifies itself with the web. The traditional political establishment is associated with “old” media (television, radio, and the printed press), and represented as a “walking dead,” doomed to be superseded and buried by a web-based direct democracy.

Designing a Language for Spatial Computing

We present the design rationale underlying a language for spatial computing and sketch a prototypical implementation in Python. The goal of this work is to provide a high-level language for spatial computing that is executable on existing commercial and open source spatial computing platforms, particularly Geographic Information Systems (GIS). The key idea of the approach is to target an abstraction level higher than that of GIS commands and data formats, yet meaningful within and across application domains. The paper describes the underlying theory of spatial information and shows its evolving formal specification. An embedding in Python exemplifies access to commonly available implementations of spatial computations.