34,562 research outputs found
Spatial information retrieval and geographical ontologies: an overview of the SPIRIT project
A large proportion of the resources available on the world-wide
web refer to information that may be regarded as geographically
located. Thus most activities and enterprises take place in one or
more places on the Earth's surface and there is a wealth of survey
data, images, maps and reports that relate to specific places or
regions. Despite the prevalence of geographical context, existing
web search facilities are poorly adapted to help people find
information that relates to a particular location. When the name of
a place is typed into a typical search engine, web pages that
include that name in their text will be retrieved, but it is likely
that many resources that are also associated with the place may
not be retrieved. Thus resources relating to places that are inside
the specified place may not be found, nor may be places that are
nearby or that are equivalent but referred to by another name.
Specification of geographical context frequently requires the use
of spatial relationships concerning distance or containment for
example, yet such terminology cannot be understood by existing
search engines. Here we provide a brief survey of existing
facilities for geographical information retrieval on the web, before
describing a set of tools and techniques that are being developed
in the project SPIRIT : Spatially-Aware Information Retrieval on
the Internet (funded by European Commission Framework V
Project IST-2001-35047)
Technology Integration around the Geographic Information: A State of the Art
One of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented
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 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
A unified framework for building ontological theories with application and testing in the field of clinical trials
The objective of this research programme is to contribute to the establishment of the emerging science of Formal Ontology in Information Systems via a collaborative project involving researchers from a range of disciplines including philosophy, logic, computer science, linguistics, and the medical sciences. The researchers will work together on the construction of a unified formal ontology, which means: a general framework for the construction of ontological theories in specific domains. The framework will be constructed using the axiomatic-deductive method of modern formal ontology. It will be tested via a series of applications relating to on-going work in Leipzig on medical taxonomies and data dictionaries in the context of clinical trials. This will lead to the production of a domain-specific ontology which is designed to serve as a basis for applications in the medical field
Use of Subimages in Fish Species Identification: A Qualitative Study
Many scholarly tasks involve working with subdocuments, or contextualized fine-grain information, i.e., with information that is part of some larger unit. A digital library (DL) facil- itates management, access, retrieval, and use of collections of data and metadata through services. However, most DLs do not provide infrastructure or services to support working with subdocuments. Superimposed information (SI) refers to new information that is created to reference subdocu- ments in existing information resources. We combine this idea of SI with traditional DL services, to define and develop a DL with SI (SI-DL). We explored the use of subimages and evaluated the use of a prototype SI-DL (SuperIDR) in fish species identification, a scholarly task that involves work- ing with subimages. The contexts and strategies of working with subimages in SuperIDR suggest new and enhanced sup- port (SI-DL services) for scholarly tasks that involve working with subimages, including new ways of querying and search- ing for subimages and associated information. The main contribution of our work are the insights gained from these findings of use of subimages and of SuperIDR (a prototype SI-DL), which lead to recommendations for the design of digital libraries with superimposed information
Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization
This paper tackles the problem of large-scale image-based localization (IBL)
where the spatial location of a query image is determined by finding out the
most similar reference images in a large database. For solving this problem, a
critical task is to learn discriminative image representation that captures
informative information relevant for localization. We propose a novel
representation learning method having higher location-discriminating power. It
provides the following contributions: 1) we represent a place (location) as a
set of exemplar images depicting the same landmarks and aim to maximize
similarities among intra-place images while minimizing similarities among
inter-place images; 2) we model a similarity measure as a probability
distribution on L_2-metric distances between intra-place and inter-place image
representations; 3) we propose a new Stochastic Attraction and Repulsion
Embedding (SARE) loss function minimizing the KL divergence between the learned
and the actual probability distributions; 4) we give theoretical comparisons
between SARE, triplet ranking and contrastive losses. It provides insights into
why SARE is better by analyzing gradients. Our SARE loss is easy to implement
and pluggable to any CNN. Experiments show that our proposed method improves
the localization performance on standard benchmarks by a large margin.
Demonstrating the broad applicability of our method, we obtained the third
place out of 209 teams in the 2018 Google Landmark Retrieval Challenge. Our
code and model are available at https://github.com/Liumouliu/deepIBL.Comment: ICC
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