3,194 research outputs found
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
The DIGMAP geo-temporal web gazetteer service
This paper presents the DIGMAP geo-temporal Web gazetteer service, a system providing access to names of places, historical periods, and associated geo-temporal information. Within the DIGMAP project, this gazetteer serves as the unified repository of geographic and temporal information, assisting in the recognition and disambiguation of geo-temporal expressions over text, as well as in resource searching and indexing. We describe the data integration methodology, the handling of temporal information and some of the applications that use the gazetteer. Initial evaluation results show that the proposed system can adequately support several tasks related to geo-temporal information extraction and retrieval
Graphene: Semantically-Linked Propositions in Open Information Extraction
We present an Open Information Extraction (IE) approach that uses a
two-layered transformation stage consisting of a clausal disembedding layer and
a phrasal disembedding layer, together with rhetorical relation identification.
In that way, we convert sentences that present a complex linguistic structure
into simplified, syntactically sound sentences, from which we can extract
propositions that are represented in a two-layered hierarchy in the form of
core relational tuples and accompanying contextual information which are
semantically linked via rhetorical relations. In a comparative evaluation, we
demonstrate that our reference implementation Graphene outperforms
state-of-the-art Open IE systems in the construction of correct n-ary
predicate-argument structures. Moreover, we show that existing Open IE
approaches can benefit from the transformation process of our framework.Comment: 27th International Conference on Computational Linguistics (COLING
2018
GEO INFORMATION EXTRACTION AND PROCESSING FROM TRAVEL NARRATIVES
Travel narratives published in electronic formats can be very important especially to the tourism community because of the great amount of knowledge that can be extracted. However, the low exploitation of these documents opens a new area of opportunity to the computing community. In this way, this article explores new ways to visualize travel narratives in a map in order to take advantage of experiences of individuals to recommend and describe travel places. Our approach is based on the use of a Geoparsing Web Service to extract geographic coordinates from travel narratives. Once geographic coordinates are extracted by using eXtensible Markup Language (XML) we draw the geo-positions and link the documents into a map image in order to visualize textual information
Automatic Extraction of Commonsense LocatedNear Knowledge
LocatedNear relation is a kind of commonsense knowledge describing two
physical objects that are typically found near each other in real life. In this
paper, we study how to automatically extract such relationship through a
sentence-level relation classifier and aggregating the scores of entity pairs
from a large corpus. Also, we release two benchmark datasets for evaluation and
future research.Comment: Accepted by ACL 2018. A preliminary version is presented on
AKBC@NIPS'1
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