37,322 research outputs found

    Enhanced Place Name Search Using Semantic Gazetteers

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    With the increased availability of geospatial data and efficient geo-referencing services, people are now more likely to engage in geospatial searches for information on the Web. Searching by address is supported by geocoding which converts an address to a geographic coordinate. Addresses are one form of geospatial referencing that are relatively well understood and easy for people to use, but place names are generally the most intuitive natural language expressions that people use for locations. This thesis presents an approach, for enhancing place name searches with a geo-ontology and a semantically enabled gazetteer. This approach investigates the extension of general spatial relationships to domain specific semantically rich concepts and spatial relationships. Hydrography is selected as the domain, and the thesis investigates the specification of semantic relationships between hydrographic features as functions of spatial relationships between their footprints. A Gazetteer Ontology (GazOntology) based on ISO Standards is developed to associate a feature with a Spatial Reference. The Spatial Reference can be a GeoIdentifier which is a text based representation of a feature usually a place name or zip code or the spatial reference can be a Geometry representation which is a spatial footprint of the feature. A Hydrological Features Ontology (HydroOntology) is developed to model canonical forms of hydrological features and their hydrological relationships. The classes modelled are endurant classes modelled in foundational ontologies such as DOLCE. Semantics of these relationships in a hydrological context are specified in a HydroOntology. The HydroOntology and GazOntology can be viewed as the semantic schema for the HydroGazetteer. The HydroGazetteer was developed as an RDF triplestore and populated with instances of named hydrographic features from the National Hydrography Dataset (NHD) for several watersheds in the state of Maine. In order to determine what instances of surface hydrology features participate in the specified semantic relationships, information was obtained through spatial analysis of the National Hydrography Dataset (NHD), the NHDPlus data set and the Geographic Names Information System (GNIS). The 9 intersection model between point, line, directed line, and region geometries which identifies sets of relationship between geometries independent of what these geometries represent in the world provided the basis for identifying semantic relationships between the canonical hydrographic feature types. The developed ontologies enable the HydroGazetteer to answer different categories of queries, namely place name queries involving the taxonomy of feature types, queries on relations between named places, and place name queries with reasoning. A simple user interface to select a hydrological relationship and a hydrological feature name was developed and the results are displayed on a USGS topographic base map. The approach demonstrates that spatial semantics can provide effective query disambiguation and more targeted spatial queries between named places based on relationships such as upstream, downstream, or flows through

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

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    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 geo-knowledge 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

    Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web

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    Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions

    Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams

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    Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region.Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure

    SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

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    Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. For those models that consider space, most of them primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location-aware KG embedding model called SE-KGE. It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE in comparison to multiple baselines. Evaluation results show that SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic query answering task. This demonstrates the effectiveness of our spatially-explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI

    A lightweight web video model with content and context descriptions for integration with linked data

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    The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud
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