Multi-resolution Semantic Area Search (original) (raw)
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Discovering Areas of Interest Using a Semantic Geo-Clustering Approach
Springer eBooks, 2016
Living in the era of social networking, coupled together with great advances in digital multimedia user-generated content, motivated us to focus our research work on humanistic data generated by such activities towards new, more efficient ways of extracting semantically meaningful information in the process. More specifically, the herein proposed approach aims to extract areas of interest in urban areas, utilizing the increasing socially-generated knowledge from social networks. A part of the area of interest is selected, then split into "tiles" and processed with an iterative merging approach whose goal is to extract larger, "homogeneous" areas which are of special (e.g., tourist) interest. In this work generated areas of interest focus on interesting points from the humanistic point of view, thus covering in general main touristic attractions and places of interest. In order to achieve our goals, we exploit two types of metadata, namely location-based information (geo-tags) geo-tags and simple user-generated tags.
A Semantic Region Growing Algorithm: Extraction of Urban Settings
Lecture Notes in Geoinformation and Cartography, 2015
Recent years have witnessed a growing production of Volunteer Geographic Information (VGI). This led to the general availability of semantically rich datasets, allowing for novel ways to understand, analyze or generalize urban areas. This paper presents an approach that exploits this semantic richness to extract urban settings, i.e., conceptually-uniform geographic areas with respect to certain activities. We argue that urban settings are a more accurate way of generalizing cities, since it more closely models human sense-making of urban spaces. To this end, we formalized and implemented a semantic region growing algorithm-a modification of a standard image segmentation procedure. To evaluate our approach, shopping areas of two European capital cities (Vienna and London) were extracted from an OpenStreetMap dataset. Finally, we explored the use of our approach to search for urban settings (e.g., shopping areas) in one city, that are similar to a setting in another.
Intuitive modelling of place name regions for spatial information retrieval
Reasoning about spatial relevance is important for intelligent spatial information retrieval. In heterogeneous and distributed systems like the Semantic Web, spatial reasoning has to be based on lightweight , interoperable and easy-to-use spatial metadata. In this paper we present an approach to an intuitive and user-friendly creation and application of spatial metadata that are used for spatial relevance reasoning. The metadata are based on discrete approximations of place name regions. Based on knowledge about cognitive aspects of preferred spatial models, our approach allows for the representation and intuitive modelling of indeterminate regions in addition to regions with well-known boundaries.
2008
Location analysis has so far been limited to spatial, and in some cases spatio-temporal, constraints. For example, the optimal site for a shopping center can be determined by taking the location of other centers and of potential customers into account, and factoring in transportation networks, as well as land use and zoning restrictions. Some solutions, like the relatively unknown but seminal work of Dovetrovo, allow for including dynamic constraints.
A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics
Living in the " era of social networking " , we are experiencing a data revolution, generating an astonishing amount of digital information every single day. Due to this proliferation of data volume, there has been an explosion of new application domains for information mined from social networks. In this paper, we leverage this " socially-generated knowledge " (i.e., user-generated content derived from social networks) towards the detection of areas-of-interest within an urban region. These large and homogeneous areas contain multiple points-of-interest which are of special interest to particular groups of people (e.g., tourists and/or consumers). In order to identify them, we exploit two types of metadata, namely location-based information included within geo-tagged photos that we collect from Flickr, along with plain simple textual information from user-generated tags. We propose an algorithm that divides a predefined geographical area (i.e., the center of Athens, Greece) into " tile "-shaped sub-regions and based on an iterative merging procedure, it aims to detect larger, cohesive areas. We examine the performance of the algorithm both in a qualitative and quantitative manner. Our experiments demonstrate that the proposed geo-clustering algorithm is able to correctly detect regions that contain popular tourist attractions within them with very promising results.
A Semantic Enhanced Model for Effective Spatial Information Retrieval
Proceedings of the World Congress on Engineering and Computer Science 2014 Vol I WCECS 2014, 22-24 October, 2014, San Francisco, USA, 2014
A lot of information on the web is geographically referenced. Discovering and retrieving this geographic information to satisfy various users needs across both open and distributed Spatial Data Infrastructures (SDI) poses eminent research challenges. However, this is mostly caused by semantic heterogeneity in user’s query and lack of semantic referencing of the Geographic Information (GI) metadata. To addressing these challenges, this paper discusses an ontology based semantic enhanced model, which explicitly represents GI metadata, and provides linked RDF instances of each entity. The system focuses on semantic search, ontology, and efficient spatial information retrieval. In particular, an integrated model that uses specific domain information extraction method to improve the searching and retrieval of ranked spatial data.
Effective Methods of Semantic Analysis in Spatial Contexts
Raimundo F. Dos Santos Jr. With the growing spread of spatial data, exploratory analysis has gained a considerable amount of attention. Particularly in the fields of Information Retrieval and Data Mining, the integration of data points helps uncover interesting patterns not always visible to the naked eye. Social networks often link entities that share places and activities; marketing tools target users based on behavior and preferences; and medical technology combines symptoms to categorize diseases. Many of the current approaches in this field of research depend on semantic analysis, which is good for inferencing and decision making. From a functional point of view, objects can be investigated from a spatial and temporal perspectives. The former attempts to verify how proximity makes the objects related; the latter adds a measure of coherence by enforcing time ordering. This type of spatio-temporal reasoning examines several aspects of semantic analysis and their characteristics: shared relationships among objects, matches versus mismatches of values, distances among parents and children, and bruteforce comparison of attributes. Most of these approaches suffer from the pitfalls of disparate data, often missing true relationships, failing to deal with inexact vocabularies, ignoring missing values, and poorly handling multiple attributes. In addition, the vast majority does not consider the spatio-temporal aspects of the data. This research studies semantic techniques of data analysis in spatial contexts. The proposed solutions represent different methods on how to relate spatial entities or sequences of entities. They are able to identify relationships that are not explicitly written down. Major contributions of this research include (1) a framework that computes a numerical entity similarity, denoted a semantic footprint, composed of spatial, dimensional, and ontological facets; (2) a semantic approach that translates categorical data into a numerical score, which permits ranking and ordering; (3) an extensive study of GML as a representative spatial structure of how semantic analysis methods are influenced by its approaches to storage, querying, and parsing; (4) a method to find spatial regions of high entity density based on a clustering coefficient; (5) a ranking strategy based on connectivity strength which differentiates important relationships from less relevant ones; (6) a distance measure between entity sequences that quantifies the most related streams of information; (7) three distance-based measures (one probabilistic, one based on spatial influence, and one that is spatiological) that quantifies the interactions among entities and events; (8) a spatio-temporal method to compute the coherence of a data sequence.
This specialist meeting on the theme of spatial search provided a platform for exploring research frontiers at the interface of computer science, cognitive science, and other disciplines, especially in the context of geographically referenced information. This report reviews the discussions among 36 experts from academia and industry over two days, and draws attention to research gaps that will require broad interdisciplinary efforts over the next five to ten years.