6,892 research outputs found
Geographic Information Retrieval using Query Aware Document Ranking Method Case Study for Surakarta
This paper discusses the development of a Geographic Information Retrieval for Surakarta City in Indonesia. Surakarta City was chosen as the location of the place in the system because Surakarta got an award for the best tourist spot in Indonesia. In this case, Geographic Information Retrieval is a system that can handle geographic data by analyzing existing text data and generate output that can be used as decision-making on problems related to geographical. The method used in processing the information is Query Aware Document Ranking. The purpose of using this method is to provide relevant results such as output answer, answerâs images and coordinates of the answer
Spatio-textual indexing for geographical search on the web
Many web documents refer to specific geographic localities and many
people include geographic context in queries to web search engines. Standard
web search engines treat the geographical terms in the same way as other terms.
This can result in failure to find relevant documents that refer to the place of
interest using alternative related names, such as those of included or nearby
places. This can be overcome by associating text indexing with spatial indexing
methods that exploit geo-tagging procedures to categorise documents with
respect to geographic space. We describe three methods for spatio-textual
indexing based on multiple spatially indexed text indexes, attaching spatial
indexes to the document occurrences of a text index, and merging text index
access results with results of access to a spatial index of documents. These
schemes are compared experimentally with a conventional text index search
engine, using a collection of geo-tagged web documents, and are shown to be
able to compete in speed and storage performance with pure text indexing
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)
Combination of content analysis and context features for digital photograph retrieval.
In recent years digital cameras have seen an enormous rise
in popularity, leading to a huge increase in the quantity of
digital photos being taken. This brings with it the challenge of organising these large collections. The MediAssist project uses date/time and GPS location for the
organisation of personal collections. However, this context
information is not always sufficient to support retrieval
when faced with a large, shared, archive made up of
photos from a number of users. We present work in this
paper which retrieves photos of known objects (buildings,
monuments) using both location information and content-based
retrieval tools from the AceToolbox. We show that
for this retrieval scenario, where a user is searching for
photos of a known building or monument in a large shared
collection, content-based techniques can offer a significant
improvement over ranking based on context (specifically
location) alone
A geo-temporal information extraction service for processing descriptive metadata in digital libraries
In the context of digital map libraries, resources are usually described according to metadata records that define the relevant subject, location, time-span, format and keywords. On what concerns locations and time-spans, metadata records are often incomplete or they provide information in a way that is not machine-understandable (e.g. textual descriptions). This paper presents techniques for extracting geotemporal information from text, using relatively simple text mining methods that leverage on a Web gazetteer service. The idea is to go from human-made geotemporal referencing (i.e. using place and period names in textual expressions) into geo-spatial coordinates and time-spans. A prototype system, implementing the proposed methods, is described in detail. Experimental results demonstrate the efficiency and accuracy of the proposed approaches
Extending Yioop! With Geographical Location Local Search
It is often useful when doing an internet search to get results based on our current location. For example, we might want such results when we search on restaurants, car service center, or hospitals. Current open source search engines like those based on Nutch do not provide this facility. Commercial engines like Google and Yahoo! provide this facility so it would be useful to incorporate it in an open source alternative. The goal of this project is to include location aware search in Yioop!(Pollett, 2012) by using geographical data from OpenStreetMap(âOpen Street map wikiâ, 2012) and hostip.info (âDMOZâ, n.d.) database to geolocate IP addresses
Dating Texts without Explicit Temporal Cues
This paper tackles temporal resolution of documents, such as determining when
a document is about or when it was written, based only on its text. We apply
techniques from information retrieval that predict dates via language models
over a discretized timeline. Unlike most previous works, we rely {\it solely}
on temporal cues implicit in the text. We consider both document-likelihood and
divergence based techniques and several smoothing methods for both of them. Our
best model predicts the mid-point of individuals' lives with a median of 22 and
mean error of 36 years for Wikipedia biographies from 3800 B.C. to the present
day. We also show that this approach works well when training on such
biographies and predicting dates both for non-biographical Wikipedia pages
about specific years (500 B.C. to 2010 A.D.) and for publication dates of short
stories (1798 to 2008). Together, our work shows that, even in absence of
temporal extraction resources, it is possible to achieve remarkable temporal
locality across a diverse set of texts
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