-
ity, I
Keywords:Spatiotemporal semantic informationretrievalNatural
language processingHazard ontology
nfoth
. Ex
component in the process of semantic information extraction. We
show how ontologies can be used in
al (Gcally o
For example, it is possible to extract ZIP codes, addresses,
well-known landmarks, peoples names, or telephone numbers fromWeb
documents. While the elds of NLP and GIR, as rapidlyexpanding elds
of scientic research, have contributed solutionsfor helping users
to nd information based on their interests, thepossibility of
automatically tracking spatiotemporal and semantic
. In this research,rts aboutted with hvent), as w
higher-level events (e.g., more general hazard impact orresponse
events). Making spatiotemporal information moreingful by adding
semantics (e.g., hazard impact, response, orery) improves an
understanding about the dynamics that underliehazard outbreaks.
Users can directly visualize spatial and temporaltrends of
different hazard events and map these events and theirsemantics at
multiple levels of detail, capturing aspects that arenot explicitly
described in text documents. This work can also beapplied to
dynamics more generally, for example, extractingsemantics about
disease outbreak events or political campaigns.
Corresponding author.E-mail addresses:[email protected] (W.
Wang), kathleen-stewart@uiowa.
edu (K. Stewart).
Computers, Environment and Urban Systems 50 (2015) 3040
Contents lists availab
Computers, Environmen
vietexts to perform useful tasks with a set of computational
algo-rithms and statistical approaches (Chowdhury, 2003).
BridgingGIR and NLP techniques, salient geographic information can
beautomatically extracted from large volumes of unstructured
text.
to reveal more about the nature of the disastersemantic
information from online news repostorms includes domain-related
events associa(e.g., an airport closed event or power outage
ehttp://dx.doi.org/10.1016/j.compenvurbsys.2014.11.0010198-9715/
2014 Elsevier Ltd. All rights reserved.severeazardsell
ashazardmean-recov-Internet search engines that involve not only
traditional informa-tion retrieval (IR) methods, such as indexing,
searching, browsing,crawling and querying, Web documents, but also
the geographicscope of documents (Teitler et al., 2008; Purves
& Jones, 2010;Karimzadeh et al., 2013). Natural language
processing (NLP) is usedto analyze the content and context of
documents, and manipulate
when, or what events have occurred. The contribution of
thisresearch is to show how semantic information about
events,important for understanding the different kinds of events
thatoccur during natural hazards such as hurricanes, blizzards or
otherdisasters, can be extracted and used in combination with
spatialand temporal information that is also extracted from
documentsGISGazetteers
1. Introduction
Geographic information retrievopportunities for users to
automaticonjunction with natural language gazetteers in order to
process semantic information about hazardevents and augment
spatiotemporal extraction with semantics. We are interested in
capturing thespatiotemporal patterns of hazard-related events from
online news reports to track the occurrencesand evolution of
natural hazards, such as severe storms. A hazard ontology has been
created to assistthe spatiotemporal information extraction process,
especially with the automatic detection of differentkinds of events
at multiple granularities from unstructured texts revealing
relationships between theevents over spacetime. The extraction and
retrieval of semantic information about event dynamicsprovides
information about the progression of events using both natural and
human perspectives.
2014 Elsevier Ltd. All rights reserved.
IR) provides valuablebtain information from
information about events in Web news articles is still a
challenge.Extracting spatiotemporal and semantic information from a
set ofWeb documents makes it possible to build a rich
representationof geographic knowledge that is described in text,
capturing where,Available online 26 November 2014documents enables
us to build a rich representation of geographic knowledge described
in text, capturingwhere, when, or what events have occurred. This
work investigates the role ontologies play as a keySpatiotemporal
and semantic informationreports about natural hazards
Wei Wang , Kathleen StewartDepartment of Geographical and
Sustainability Sciences, The University of Iowa, Iowa C
a r t i c l e i n f o
Article history:Received 12 March 2014Received in revised form
10 October 2014Accepted 2 November 2014
a b s t r a c t
In the eld of geographic iinformation extracted fromgrowing area
of research
journal homepage: www.elseextraction from Web news
A, United States
rmation science, modeling geographic dynamics based on
spatiotemporale Web, especially unconstructed data such as online
news reports, is atracting spatiotemporal and semantic information
from a set of Web
le at ScienceDirect
t and Urban Systems
r .com/locate /compenvurbsys
-
framework is developed to assist GIR by mapping between
alterna-tive spatiotemporal classications. Three ontologies are
integrated
nmeThis paper investigates the role ontologies play as a key
compo-nent in the process of geographic information retrieval.
Ontologiesdeal with the actual nature of the phenomena, focusing on
the nat-ure and the organization of reality (Kalfoglou &
Schorlemmer,2003; Welty & Murdock, 2003). In information
science, ontologieshave been applied as a tool for knowledge
management. Theyprovide a concept-based framework using formal
methods thatrepresent entities, attributes, relationships, and
values for a spe-cic domain (Noy, 2004; Sawsaa & Lu, 2012;
Wiegand & Garcia,2007). Well-developed ontologies can serve as
a standard forconceptualizing and understanding domains of
interest, andontologies enable semantic interoperability (Cruz,
Ganesh, &Mirrezaei, 2013; Cruz & Xiao, 2005; Fernadez et
al., 2011;Schorlemmer & Kalfoglou, 2008). Ontologies also play
an impor-tant role in GIR techniques, by providing a knowledge base
thatsupports semantic understanding of text and improves
searchresults as well as extracted information (e.g., geographic
informa-tion). Associations between different semantics can be
achieved byapplying ontologies with classication schemes and
hierarchies(Jones & Purves, 2008; Kemp, Tan, & Whalley,
2007;Kontopoulos, Berberidis, Dergiades, & Bassiliades, 2013).
Ontolo-gies can also be applied for the disambiguation of
geographicnames and improving gazetteer interaction (Janowicz &
Keler,2008; Machado, Alencar, Campos, & Clodoveu, 2011; Volz,
Kleb,& Mueller, 2007). Events can also be represented in
ontologies(Allen & Ferguson, 1994; Imran, Elbassuomi, Castillo,
Diaz, &Meier, 2013; Lee, Wu, Yang, Wen, & Chiang, 2013;
Worboys &Stewart Hornsby, 2004). In this research, we show how
usingontologies in GIR applications is helpful for relating
multipleperspectives of hazards (in this case, environmental and
humanperspectives), for example, weather-related and human
impactaspects using terms and relations from the ontology. Two
keyresearch questions are addressed through this work:
How can gazetteers and ontologies be integrated in order to
beused for semantic information extraction of hazard event
infor-mation at multiple granularities?
How does representing semantic information associated withhazard
events using ontological relations on maps contributeto an
understanding of event dynamics?
For this work, we are interested in capturing the
spatiotemporalpatterns of hazard-related events from texts and to
track the differ-ent kinds of events relating to both environmental
and humanperspectives over spacetime. A hazard-based ontology has
beenbuilt to assist the spatiotemporal and semantic information
extrac-tion and retrieval process, especially useful for the
automaticdetection of semantics in unstructured texts and for
representingthe relationships between events. This approach to
automaticinformation extraction is particularly useful if a large
set of docu-ments describing many different events needs to be
analyzed forcontent. When harnessed to a GIS for mapping, the
semantic infor-mation retrieval provides richness to the resulting
maps, offeringmultiple perspectives of the hazard as well as
multiple levels ofdetails. The approach used in this research is
shown in Fig. 1, usesinputs (i.e., online news reports) and
integrates text linguisticprocessing with spatial, temporal, and
semantics gazetteers, anda hazard ontology in order to perform
information extraction pro-cessing. The outputs of this processing
is stored in a geodatabase,and geocoded for mapping in ArcGIS.
The rest of this paper is organized as follows. Section
2discusses related work on applying ontologies for geographic
infor-mation retrieval. Section 3 describes the development and use
of a
W. Wang, K. Stewart / Computers, Envirohazard ontology, and then
demonstrates how the ontology isintegrated with semantic and
spatial and temporal gazetteers inan NLP environment for
information extraction. The followingto capture the semantics of
spatial (Marine surface), temporal (fourseasons) and thematic
(shery-related) dimensions drawn fromtwo heterogeneous shery
databases (structured data). The the-matic or domain ontology
provides a hierarchical structure of con-cept terms and also links
between concepts in one domain (i.e., theshery) with other domains
(e.g., biology). In this paper, we alsosections explain how event
dynamics and semantic informationare extracted from online news
reports and represented usingGIS. The proposed approach is applied
to a document set of newsreports on Hurricane Sandy from
OctoberNovember 2012. Themethods are evaluated in Section 7.
Conclusions and futureresearch are discussed in the nal
section.
2. Related work
Digital text information is widely available in the form of
Webarticles, news reports, blogs, Twitter feeds, and other formats.
Spa-tial and temporal information is commonly referenced in
theseWeb documents, and extracting such information provide
detailsabout dynamic change patterns and trends of world events
overspacetime (Grover et al., 2010; Janowicz, Scheider, Pehle,
&Hart, 2012; Sankaranarayanan, Teitler, Lieberman, &
Sperling,2009). Extracted results can be visualized in a GIS
environment,transferring a textual surrogate of text documents to a
visual sur-rogate (Gregory & Hardie, 2011). Ontologies have
been included inthe GIR process to facilitate the retrieval of
heterogeneous geo-graphic information from texts, and knowledge
representationand reasoning (Buscaldi, Bessagnet, Royer, &
Sallaberry, 2014;Kontopoulos et al., 2013; Machado et al., 2011).
Ontologies reectdifferent relations that hold among entities, for
example, entitiesare arranged into superclasses and subclasses
related by is-a rela-tions in order to classify entities into
subgroups, or to capture part-whole relationships through part-of
relations. Ontologies have alsobeen applied to capture events, such
as biomedical events (Huet al., 2011) and business events
(Arendarenko & Kakkonen,2012; Saggion, Funk, Maynard, &
Bontcheva, 2007). Ontologiescan refer to upper-level concepts or
more specialized domain-based concepts. An upper-level ontology is
a model that capturescommon (high-level) abstraction of entities in
the world such asRegion, Event, Physical Object and Feature (e.g.,
DOLCE and BFO)(Guarino, 1998; Smith & Ceusters, 2010). Upper
ontologies maybenet GIR by formalizing and integrating extracted
informationrelating to high-level semantics (Gangemi, Guarino,
Masolo,Oltramari, & Schneider, 2002). Domain ontologies on the
otherhand, capture classes that are more specialized and relate to
a par-ticular domain (e.g., medicine, transportation, hazards)
(Guarino,1998; Stewart, Fan, & White, 2013). Domain ontologies
are espe-cially useful for processing and reasoning over text
content, andenhance semantic information construction in
information extrac-tion systems. The SPIRIT project
(SPatially-aware InformationRetrieval on the InTernet) incorporates
a domain ontology, a geo-graphic ontology, footprints, and spatial
indexing to create a spatialsearch engine in which geospatial
semantic differences betweenplaces are distinguished (Jones &
Purves, 2008; Purves et al.,2007). The developed geographic
ontology contains actual andalternative place names, place types,
spatial footprints (i.e.,geometric extent), and spatial
relationships (i.e., part-of), and isapplied to recognize place
names and support disambiguation ofplace name expression in user
queries. For a project on marineenvironmental management (Kemp et
al., 2007), an ontology
nt and Urban Systems 50 (2015) 3040 31are interested in spatial
and temporal aspects as well as hazardsemantics, as we wish to
automatically extract the spatiotemporalpatterns of hazard-related
events from texts in order to represent
-
y
singnceer,
mp
nmeand track the evolution of different kinds of hazards over
space andtime.
In the next section, we discuss the development of an
ontologythat supports information extraction relating to hazard
eventsespecially severe storms (hurricanes, tornadoes, blizzards,
etc.).The ontology is used for distinguishing different kinds of
hazard-related events and semantic information extraction. It
includeshazard-related terms from not only a natural perspective
(e.g., geo-logical hazard, hydrological hazard, etc.), but also
from a humanperspective, where the impacts of hazards (e.g.,
airport closings,ight cancellations, closed roads, power outages,
etc.) and thehuman responses to hazards (e.g., evacuations, power
restored)are modeled.
Web Text Documents
Hazard Ontolog
Spaal, Temporal,and Semanc Gazeeers
Linguisc Proces(Tokenizer, Sentesplier, POS taggRules, etc)
Input
Process
GATE 8.0
Fig. 1. The process for incorporating ontologies into
spatiote
32 W. Wang, K. Stewart / Computers, Enviro3. Constructing a
hazard ontology
To support semantic information extraction tasks involvingevents
that occur during natural hazards such as severe storms,an ontology
was created that includes key hazard terms. For thiswork, an open
source toolkit, NeOn (http://neon-toolkit.org/) isused to construct
the hazard ontology (Fig. 2). This platform waschosen for its
exible environment that supports easy importing,editing,
visualizing, and exporting ontologies. The ontology is cre-ated
from authoritative sources on hazards, e.g., the US
FederalEmergency Management Agency (http://www.fema.gov/)
thatprovides a source for common hazards terminology, and the
USNational Weather Service (http://www.weather.gov/) for
differentkinds of hazardous weather entities. Existing ontologies
(e.g.,OpenCyc (http://sw.opencyc.org/) and GeoNames
(http://www.geonames.org/)) are also used as sources for terms
relatingto natural hazards, as well as terms extracted from news
documentsets during training (discussed in detail in Section 4).
Classes arelinked by either is-a or part-of relations, and for
classes linked byis-a relations, there is a single superclass for a
set of subclasses(i.e., this ontology does not use multiple
inheritance). In this work,part-of relations model relationships
where a part has a functionwith respect to the whole (e.g.,
HazardManagement part-of Natual-Hazard). Both of these relations
support reasoning over multiplegranularities of events and features
in the news reports. Althoughthe ontology was designed to be
comprehensive to support theextraction process and currently
consists of approximately 250classes, we anticipate that this
ontology could certainly beextended for future uses (see Wang, 2014
for a version of theontology).
Upper-level classes in the ontology describe the most
abstractentities and includes two classes, Object and Happening.
ClassObject describes high-level categories of entities that can be
spe-cialized as one of three subclasses, Agent, Place, or Time.
Happeningrefers to the dynamic processes that occur during all
hazards.Event, is a subclass of Happening, and is associated with
subclassesNaturalHazard and MeteorologicalEvent. Four subclasses
aresubsumed by class NaturalHazard including
GeologicHazard,ClimateHazard, HydrologicHazard, and WildreHazard.
Meteorologi-calEvent subsumes weather classes including
Precipitation, Stormand Wind.
Spaal, Temporal and Semanc Informaon Set in a Geodatabase
Geocoding
Geovisualizaon
Integrate
Output
oral and semantic information extraction for hazard events.
nt and Urban Systems 50 (2015) 3040HazardManagement captures the
human aspects associatedwith a natural hazard. This class shares a
part-of relation with Nat-uralHazard, and has four subclasses,
Prediction, HazardImpact,HazardResponse, and HazardRecovery.
HazardImpact has subclassesCommercialImpact, FacilityImpact,
IndividualImpact, ResidentialIm-pact, and PoliticalImpact. For
example, Hurricane Sandy occurredduring the 2012 US Presidential
election, and numerous election-eering events were affected
(typically cancelled) as a result of thesevere weather. These
events are modeled as PoliticalImpact events.Subclasses of
HazardResponse include CommunityResponse, Emer-gencyResponse, and
TransportationResponse. Recovery subsumesCommericalRecovery,
FacilityRecovery, ResidentialRecovery, Trans-portationRecovery, and
UtilityRecovery.
4. Integrating the ontology with spatial, temporal and
semanticgazetteers
In order to prepare Web news reports for the
informationextraction process, the content of the news reports is
annotatedwith respect to spatial, temporal and hazard-related
information.In this work, information extraction is implemented
using GATE8.0, General Architecture for Text Engineering
(http://gate.ac.uk/).The principal information extraction engine
uses three differentgazetteers. Traditionally, a gazetteer is
regarded as a dictionarythat contains lists of geographic
references (Goodchild & Hill,2008), and is used for extracting
place names in information retrie-val systems. The term gazetteer
in GIR is applied more broadly.
-
terf
nmeHere, gazetteer refers to a dictionary with lists of specic
terms orphrases that are used to match the corresponding
informationfrom text documents. In this work, different gazetteers
store thedifferent kinds of vocabulary found in news reports on
hazards.
Fig. 2. A view of the NeOn in
W. Wang, K. Stewart / Computers, EnviroGATEs default gazetteer
supports extracting locations and datefrom text documents. Most of
GATEs regional references arerelated to general world geographical
references, especially geo-graphical locations in the UK. US
geographical information is notwidely covered in GATEs default
gazetteer. The spatial gazetteerdeveloped for this research extends
GATEs original gazetteer byimporting U.S. state abbreviations,
county names for all states,and 25,150 regional places (e.g.,
cities, towns, villages, census des-ignated places, airports,
schools, etc.). The geographic data isobtained from StreetMap
Premium for ArcGIS (http://www.esri.-com/data/streetmap/), Geonames
(http://www.geonames.org)and the U.S. Gazetteer Files for the 2010
Census
(https://www.census.gov/geo/mapsdata/data/gazetteer-2010.html). The
spatialdata were stored with a set of .lst les describing spatial
termsbelonging to the same type, e.g., city.lst, county.lst,
state.lst,airport.lst, etc. Currently, this gazetteer contains
locations in theUS including the Caribbean that supports the
extraction of placesfor the news reports describing the hazard
events used in thisresearch, but the gazetteer could be expanded as
necessary. In atest we found that the developed spatial gazetteer
could detect801 locations from 11 test documents, as compared to
only 380locations extracted using GATEs default gazetteer running
on thesame documents. A temporal gazetteer has also been
developedto complement GATEs built-in support, and contains
commontemporal references, such as day, week, month, and year, for
tem-poral processing with additional references, such as early
morningor late Monday evening to expand further the temporal
annotationcapabilities. Finally, a semantic gazetteer has also been
developed.For the semantic gazetteer, a set of 180 news reports on
hazardsserved as training documents for collecting samples of
hazard-related terms or phrases based on different hazard topics.
Ingeneral, there is a range to the number of training articles
used,however, it is not uncommon to see 70% of a data set used for
train-ing purposes (Resnik & Lin, 2010). In this work, 60% of a
set of CNNnews documents on blizzards, hurricanes, ooding,
tornadoes, andwildres were processed to build the semantic
gazetteer. Hazard-
ace for the hazard ontology.
nt and Urban Systems 50 (2015) 3040 33related references were
manually annotated and stored in a set of.lst les in the semantic
gazetteer to correspond with differentclasses in the hazard
ontology, e.g., storm.lst, precipitation.lst,
hydro-logicalhazard.lst, communityresponse.lst, hazardimpact.lst,
etc. Eachlist le contains a set of related hazard events as
identied in thedocuments during training. Rules have been
implemented in theJAPE transducer (a Java Annotation Pattern
Engine) in GATE tosupport spatial, temporal, and semantic reference
matching fromthe gazetteers and extraction from the texts. As a
result of training,the ontology contains terms that are
representative of the contentsof the news reports as well as the
contents of the expert hazardsources also used to create the
ontology, and in this way, the ontol-ogy is able to map to the
contents of the news articles.
To link the terms from the semantic gazetteer with classes inthe
hazard ontology, the ontology is imported into GATE as a lan-guage
resource, an OWLIM ontology. An ontology API in GATE sup-ports
several plugins, such as OntoGazetteer, and this is used tolink the
terms from the semantic gazetteer with classes in theontology. In
this way, gazetteer terms at different granularitiescan be related
using the ontology. To build the connection betweenthe gazetteer
and ontology classes in OntoGazetteer, a mapping leis created. The
mapping le is used to connect lists in the semanticgazetteer and
classes in the hazard ontology. For example,
precipi-tation.lst:http://gate.ac.uk/hazardontology.owl:Precipitation
links thelist of different precipitation-related terms in the
semantic gazet-teer to the class Precipitation in the ontology.
Terms in the semanticgazetteer are associated with different
granularities in the ontol-ogy using the relations specied in the
ontology (e.g., is-a andpart-of). This allows for reasoning over
the ontological relations,and allows us to model these event types
at different levels ofgranularity in the hazard ontology. Is-a
relations hold betweensuperclasses and subclasses and supports
reasoning over multiple
-
according to the details given in a sentence (Wang &
Stewart,2014). These rules assist in correctly assigning locations
and tem-
mapping of extracted spatiotemporal and semantic informationis
implemented using ESRIs ArcMap 10.1 software. Further discus-
nmeporal information to these events, as well as retrieving a
temporalordering of hazard events. For example, for the
sentenceMore than2000 Westar Energy Customers in Riley County were
without powerSaturday evening, Saturday evening and without power
areannotated as temporal and event information respectively andare
assigned to Riley County after processing. The event withoutpower
was matched with terms in the PowerShortage.lst le inthe semantic
gazetteer. In the ontology, PowerShortage is a subclassof
UtilityImpact, and UtilityImpact is a subclass of HazardImpact.
Inthis case, without power is associated with the higher-level
classHazardImpact through linking the semantic gazetteer and the
haz-ard ontology. Saturday evening, Riley County, without power
andHazardImpact are processed as a set (along with all other
extractedgranularities but does not hold between all classes, for
example,cases such as county and states where a part-of relation is
used.Is-a is not used to describe the relations between certain
spatialfeatures. Together, is-a and part-of relations are used to
reasonacross various levels of granularity in the hazard ontology.
Forexample, assume the term supercell is annotated in one of the
textdocuments, and that this term also exists in the ontology as a
sub-class of Thunderstorm. Given that Thunderstorm, is a Storm, it
can beinferred that Supercell is-a Storm after the processing in
GATE.Combining semantic retrieval with a spatiotemporal
perspectiveaffords important opportunities for humanenvironment
applica-tions offering new insights about the dynamics of reported
eventsin document collections.
5. Spatiotemporal and semantic information retrieval fornatural
hazards
Incorporating hazard-related semantics as part of
spatiotempo-ral information extraction provides a story-telling
approach tomapping that involves both environmental and human
perspec-tives. The process of automated spatiotemporal and
semanticinformation extraction from text documents uses the hazard
ontol-ogy and the spatial, temporal and semantic gazetteers to map
theresults of spatiotemporal information extraction, i.e., the
differentkinds of events (e.g., hazard events, response events and
impactevents) that unfold over space and time. These events, i.e.,
theactual terms and phrases from the news reports as well
ashigher-level semantics derived from the ontology, can be
visual-ized and represented at different spatial granularities.
The extraction process involves automated parsing of
spatial,temporal and semantic information from text documents,
process-ing these terms with rules developed and implemented in
GATE forlinking the events with the appropriate spatial and
temporal infor-mation (Wang, 2014; Wang & Stewart, 2014). The
extracted resultsare then saved to a geodatabase. For many
documents, events aredescribed in connection to one or more
geographical referencesand also possibly associated with temporal
expressions. However,not every sentence will have both spatial and
temporal informa-tion available. Five possible cases can arise with
respect to spatio-temporal information present in a sentence: only
spatialinformation is present; one spatial term and one temporal
refer-ence is present; one spatial term and multiple temporal
references;multiple spatial terms and a single temporal reference;
and multi-ple spatial and multiple temporal terms are present
(StewartHornsby & Wang, 2010; Wang, 2014; Wang & Stewart,
2014).The rules are implemented in Java with GATE API for
assigningproper temporal and location information with event
terms
34 W. Wang, K. Stewart / Computers, Enviroterms) to a
geodatabase. In additiona, all temporal informationneeds to be
converted to a standard time format (i.e., YYYY-MM-DD hh:mm:ss) in
the geodatabase. In this work, temporalsion relating to the data
quality of extracted information is inSection 7.
Using these methods, spatial, temporal and semantic informa-tion
about events are automatically extracted fromWeb text docu-ments.
Ontologies bolster the semantic processing by linkingannotated
terms to the different kinds of semantics they representandmodeling
the events at different granularities. In thisway, eventsemantics
(e.g., hazard impact events, response events, or recovery-related
events) can be represented on amap. This affords
importantopportunities for humanenvironment applications as the
spatio-temporal evolutionof a set of events canbe tracked andnew
insightsrevealed about the dynamics and different kinds of reported
eventsin document collections that might otherwise be unknown.
6. A case study-Hurricane Sandy, OctoberNovember, 2012
To illustrate this work, a case study is presented using a set
of50 CNN news reports collected from October 24November 4,2012
about Hurricane Sandy, a very severe weather event thathit the east
coast of the US. The evolution of reported events canbe tracked,
visualized and analyzed on maps using this approachfor
spatiotemporal and semantic information extraction.
As a result of text processing, event-related terms
extractedfrom all 50 news reports are represented in Fig. 3, with
the tempo-ral pattern of events represented using graduated colors
to empha-size temporal order, light pinkdark reddark bluelight
bluewhere light pink refers to the earliest dates of extracted
eventinformation, October 24, 2012, and light blue is associated
withthe latest date extracted, November 5, 2012. Most of the
earliestevents reported in the tracked period (Oct 24 to Oct 27),
werelocated as far south as the western Caribbean Sea, Jamaica,
Cuba,Bahamas, and Florida. By October 27th, events were being
reportedfurther north along the coast to locations including
Charleston, SC,Cape Hatteras, NC, and Mount Airy, NC. After
processing all 50 oftheWeb news reports, mapped events generally
shifted from southto north as far up as Maine in the northeast.
Inland events werealso reported, for example, events in West
Virginia, Ohio, andexpressions undergo additional processing to
standardize temporalinformation and order the events over time. For
example, theduration of events is assigned to a discrete time
period, e.g., earlyThursday is assigned a period between 5:00 am
and 11:00 am onThursday. The published date of the news reports
also serves as acriterion to compare temporal expressions (e.g.,
Thursday, tomor-row, the next day) in the documents so that these
expressionsmay be ordered temporally and assigned with appropriate
dates.The data is then ready for geocoding and mapping.
Using Yahoos geocoding API, geographic coordinates areassigned
to each extracted location (http://www.gpsvisualiz-er.com). In this
research, for generalized (i.e., high-level)geographic references
including states, counties, and regional geo-graphic entities
(e.g., southwest Florida, Riley county), geocodingselects a central
point of reference. Researchers are investigatingtechniques for
improved handling of vague places (Chasin,Woodward, Witmer, &
Kalita, 2013; Delboni & Borges, 2007;Jones, Purves, Clough,
& Joho, 2008; Vasardani, Winter, & Richter,2013), and this
aspect remains a challenge for geographic informa-tion retrieval.
For spatial references that are lower-level, such as aneighborhood
or street reference (e.g., City Island, New York orWest Street,
Manhattan), the gazetteers can be parsed and loca-tions geocoded on
maps to show where events are occurring. The
nt and Urban Systems 50 (2015) 3040Pennsylvania. This map
illustrates the focus of reporting on thehighly populated regions
of New York and New Jersey aroundOctober 30th, 2012. The text
extraction process shows other details
-
nmeW. Wang, K. Stewart / Computers, Envirotoo, for example, the
transition of the storm from tropical storm tohurricane in the
afternoon of Oct 24th.
With the addition of ontology-based semantic processing, it
ispossible to learn more about the kinds of events occurring
during
Fig. 3. Extracted events relating to Hurricane Sandy from 50nt
and Urban Systems 50 (2015) 3040 35the hurricane and where and when
they occurred. Applying theontology makes it possible to represent
the extracted texts accord-ing to the different classes of events.
An analysis based on a subsetof 12 news articles published on
October 30th, a peak day during
CNN news reports for the period Oct 24Nov 04, 2012.
-
nme and Urban Systems 50 (2015) 304036 W. Wang, K. Stewart /
Computers, Envirothe Hurricane Sandy, shows events classied as
HazardImpact,HazardResponse and HazardRecovery (Fig. 4). For
example, powerfailed, ights cancelled, and lost homes are
specializations of theclass HazardImpact while emergency evacuation
and reghters bat-tled blaze activities signify aspects of
HazardResponse. The results ofthe analysis capture the fact that
power outages were a key eventfor facilities impacted by Hurricane
Sandy as reported in Manhat-tan, Queens, and Staten Island in New
York, and Newark in NewJersey. Response-related events were
triggered in Breezy Point,Brooklyn, and Staten Island in New York,
with emergency evacua-tion in Moonachie, Queens, and Roosevelt in
New York. Eventsassociated with Recovery were reported in Manhattan
and Syosset.To understand the spatiotemporal pattern of classied
events withrespect to the population density of the U.S. in 2012
obtained from
Fig. 4. Semantic information retrieval for New York and New
Jersey over space and time.also reported in the texts.ntESRIs 2012
Updated Demographics1, the results are mapped overspace and time
against high population areas. The extracted haz-ard-related events
are clustered in major cities with high populationdensity, such as
Manhattan, Brooklyn, and Queens in New York.Based on the results,
more spatiotemporal and semantic queriescan be conducted to detect
inherent facts that cannot be directlyretrieved from the texts. For
example, which locations in New YorkCity were reported to be
impacted by Sandy from the noon of Oct29th until the morning of the
30th? The spatial restriction for thisquery is New York City, while
the semantic restriction is HazardIm-pact. The temporal
restriction, Oct29th afternoon to Oct 30th morning
The news reports are from Oct 30th, 2012, although events from
Oct 29Oct 31 were
1
http://www.arcgis.com/home/item.html?id=a18f489521ba4a589762628893be0c13.
-
W. Wang, K. Stewart / Computers, Envi nmeis between 10/29/2012
12:00 pm and 10/30/2012 8:00 am. Theresults returned that satisfy
these three conditions include the bor-oughs of Queens and
Manhattan in New York City.
Hazard impact events extracted from the 50 news reports arealso
mapped with a kernel density analysis in order to show thespatial
distribution pattern of hazard impact events (Fig. 5a). Ker-nel
density analysis estimates the intensity of events by generatinga
smooth surface using a quadratic kernel function (Li,
Goodchild& Xu, 2013; Silverman, 1986). Two parameters are used
in thekernel density analysis: kernel search radius (bandwidth) to
calcu-late density and cell size for the output raster data. A
kernel searchradius of 55 km has been used to avoid creating a map
that is toosmooth or too ambiguous to interpret. A cell size of 6
km was usedto show sufcient detail. The areas with high density of
hazardimpact events are represented with the darkest hue of red,
suchas the east coast of New York, New Jersey, Philadelphia,
VirginiaMaryland, Baltimore, and Washington. As shown in Fig.
5bHurricane Sandy impact data obtained from a FEMAModeling
TaskForce2 is mapped for the areas impacted by Hurricane Sandy
with
Fig. 5. (a) Kernel density analysis for hazard impact-related
events extracted fromFEMA Modeling Task Force.
2
http://fema.maps.arcgis.com/home/webmap/viewer.html?webmap=307dd522499d4a44a33d7296a5da5ea0.ro,
,,
50 CNnt and Urban Systems 50 (2015) 3040 37four different levels
(i.e., very high, high, moderate, and low). Fig. 5aand b shows a
similar spatial pattern for the extracted HazardImpactevents and
the impact analysis from FEMA, suggesting that in certaincases
automated extraction may be useful for a rst approximationof event
occurrences.
However, there are also certain locations, for example,
Wash-ington DC, that have different impact levels on both gures.
Oneof the possible reasons is that a large number of hazard
impactevents, such as school closures, airline cancellations, power
out-ages, and road closures associated with these areas might not
bemeasured by FEMA. Another possible reason is that the
collectedFEMA data was starting from 11/8/2012, which was slightly
laterthan the news reports. Fig. 5b uses a composite of
buildingdestroyed, surge, wind, precipitation and snow data from
FEMAto assess the impact. The event information retrieved from
textdocuments can be used in combination with other sources of
data(e.g., FEMA impact data) to enhance the understanding of
hazardevents, for example, to estimate the number of different
kinds ofevents reported. In addition, integrating data from
differentsources also allows us to discover additional information,
such asnd all recovery events in FEMA very high impact areas from
the earlymorning of Oct 30th to late afternoon on November 2nd.
While GIR isvulnerable to the quality of the text that is being
processed (e.g., if
N news reports; (b) Hurricane Sandy impact analysis
(11/8/20124/18/2013) from
-
8. Conclusions and future work
nmeerroneous information about events is reported, then that
informa-tion will be mapped), we are optimistic about the utility
of GIR forrevealing the pattern of events and their semantics. The
evaluationdiscussed in the next section underscores further the
degree towhich the results are meaningful and not just due to
chance.
7. Evaluation of approach
The set of steps presented in Section 5 show how
extractedinformation from documents can be integrated to represent
thedynamics of events described in news reports on hazard events.In
addition to comparing the results with FEMA maps, an evalua-tion
has been undertaken for testing the automatic association
ofextracted events with the appropriate location, time and
semanticinformation. The results suggest that these methods are
promisingfor generating appropriate spatiotemporal and semantic
assign-ments. The evaluation was conducted using 10% of the
HurricaneSandy data set (10 news reports). In IR and NLP, the most
com-monly used evaluation approach is to compute precision and
recallstatistics over a set of evaluation data (Manning, Raghavan,
&Schtze, 2008). Precision refers to how many of the
extractedresults are correct. The higher precision, the fewer
errors are con-tained in the extracted results. Recall indicates
the numbers ofitems that should have been detected, and records how
many areeffectively extracted. The highest recall value (i.e., 1)
indicatesthe results that need to be extracted are actually all
extracted(Sitter, Calders, & Daelemans, 2004). In this
evaluation, we haverevised slightly the traditional evaluation
metrics of using preci-sion and recall to measure the performance
of our approach.
STSprecision = the number of correctly resolved
spatiotemporalsemantic references/the number of spatiotemporal
semanticreferences that the system or users attempt to resolve;
STSrecall = the number of correctly resolved
spatiotemporalsemantic references/the number of all such
references.
To compute the STSprecision and STSrecall values, automati-cally
processed results by the system are compared with a goldstandard in
order to acquire the numbers of correctly resolved spa-tiotemporal
references, incorrectly resolved spatiotemporal refer-ences, and
missing spatiotemporal references. We recruitedhuman subjects to
provide a gold standard for this evaluation(Clark, Fox, &
Lappin, 2010). For human evaluators, ve volunteerswere hired to
manually process the evaluation data. The volunteerswere trained
before they conducted the evaluation tasks by provid-ing two sample
news reports along with instructions of how to rec-ognize spatial,
temporal and event information. In the two trainingdocuments,
spatial, temporal, and event information have beenannotated by a
human expert. The instructions provide predeter-mined criteria and
examples for volunteers so they understandthe denition of spatial,
temporal, semantic information and theassignment tasks. After
training, volunteers were assigned 10CNN news reports to process.
Each volunteer manually annotatedspatial, temporal, and semantic
terms from the 10 news reports,and assigned the annotated terms to
events based on the contextin the text documents. The results are
expressed as a set of {spatial,temporal, semantic} vectors stored
in a .csv database le.
It is recognized that human subjects might not be consistent
intheir judgments of spatial, temporal and semantic
informationcontained in the news reports, as well as how to detect
spatialand temporal information and assign it to an event.
Therefore,Kappa statistics are applied to the results of the human
evaluatorsto achieve the gold standard (Viera & Garrett, 2005;
Manning et al.,
38 W. Wang, K. Stewart / Computers, Enviro2008). This involves
comparing the results from each volunteer. Inthis evaluation, the
gold standard is based on results with at least80% agreement (four
out of ve volunteers). Results that are lowerIn this research, an
approach to automatically extract spatio-temporal and semantic
information from Web news reports abouthazards using ontologies
with NLP and GIR techniques is pre-sented. The extracted results
are mapped using ArcGIS to representthe spatiotemporal patterns of
hazard events. Events can be cate-gorized into different classes
according to their semantic proper-ties and formalized in an
ontology. Incorporating semantics inspatiotemporal information
retrieval provides insight into factsthat cannot be directly
retrieved from texts, for example, whatkinds of events were
reported over a certain time window or in acertain region. The
approach has been applied to a case study of50 CNN Web news reports
on Hurricane Sandy from October toearly November, 2012. The spatial
and temporal characteristicsassociated with the hazard-related
events can be automaticallyextracted and visualized without going
through each individualdocument. The results provide new details
about the kinds ofevents occurring during a hazard (impact,
response or recoveryevents), patterns of change (e.g., tropical
storm to hurricane), andspatiotemporal trends for hazard events as
well as an understand-ing of events at multiple spatial and
temporal granularities. Thiswork addresses two research questions
including one on the inte-gration of gazetteers and ontologies for
semantic informationretrieval and the contribution of semantic
information for mappinghazard dynamics. We describe how gazetteers
can be linked withontologies to relate event information at
different granularities.Mapping semantic information associated
with hazard events asshown in this work, also contributes to an
understanding of eventdynamics from different perspectives
(human-related activities vs.environmental phenomena).than 80%
agreement are required to be rechecked and discussedwith the
tester, to decide whether they should be included orexcluded from
the gold standard. Results with 0% agreement areexcluded.
The results are based on 113 processed spatiotemporal
andsemantic references in the text documents. For this
evaluation,there were 94 correct references, 19 incorrect
references, and 21missed spatiotemporal and semantic references
compared withthe gold standard (134 spatiotemporal and semantic
referencesset). Based on this performance, STSprecision and
STSrecall are cal-culated as 0.83 and 0.82 respectively. The
STSprecision and STSre-call values achieved by our method compares
well with otherprecision and recall values in the eld of
information extraction,for example, the precision (0.756) and
recall (0.4135) values fromGATE using ANNIE on spatial information
retrieval (Clough,2005), and average evaluation results (precision
77.6 and recall86.1) using benchmark data to process spatial and
temporal infor-mation (Strotgen, Gertz, & Popv, 2010).
In this evaluation, the quality of extracted spatial results for
the50 CNN Web news reports was also checked. The geographic
loca-tions can be grouped into two different classes: explicit
locations(i.e., places that are equal or ner in spatial granularity
as cities)and generalized locations (e.g., Southern New Jersey or
East Coast).The results contain 262 explicit locations and 386
generalized loca-tions. It is worth noting that for the case study,
the analysis wasundertaken with CNN articles. It is probably the
case that detailedgeographic locations (e.g., explicit street names
or addresses) areless likely to be reported in these news reports
than generalizedlocations. Moving to a different, regional or local
news source mayincrease the relative amounts ofmore detailed
location information.
nt and Urban Systems 50 (2015) 3040While the results obtained
for spatiotemporal and semanticinformation retrieval are exciting,
developing the approach is nottotally straightforward and many
steps are needed to ensure that
-
nmeintegration occurs between the various stages. Future efforts
willbe directed toward improving and extending the gazetteers,
forexample, including spatial and temporal relations and dealing
withmore complex phrases, e.g., in and around Iowa City. Also
sincemultiple news reports can refer to the same event, having a
strat-egy to lter the documents to avoid multiple
representationswould be useful (Wang & Stewart, 2013).
Additional open chal-lenges remain since generalized or vague
locations or temporal ref-erences, for example, are commonly
included in text, and how totranslate these places into relevant
event locations is still an openresearch question. News articles
have been useful to develop andtest this research and this approach
may be especially promisingfor obtaining information about local
dynamics where text-baseddescriptions may be one of the few
available sources of informa-tion about events that are happening
in a local area, although thechoice of news outlet could impact the
granularity of spatial infor-mation. In addition, twitter messages
are another source for spatio-temporal event information that could
be examined as tweets aredesigned to work as micro versions of
blogs or news reports. Oneof the most important advantages of
Twitter is the rapid transmis-sion of information. Future work
could involve developing anextension of the research applied to
twitter feeds that adds real-time spatiotemporal information to
complement the results of thisresearch for example, for hazard
damage assessment and hazardresponse planning.
References
Allen, J. F., & Ferguson, G. (1994). Actions and events in
interval temporal logic.Journal of Logic and Computation, Special
Issue on Action and Processing, 205245.
Arendarenko, E., & Kakkonen, T. (2012). Ontology-based
information and eventextraction for business intelligence. Articial
Intelligence: Methodology, Systems,and Applications. Lecture Notes
in Computer Science, 7557, 89102.
Buscaldi, D., Bessagnet, M. N., Royer, A., & Sallaberry, C.
(2014). Using the semanticsof texts for information retrieval: A
concept- and domain relation-basedapproach. In Proceedings of the
17th East European conference on advances indatabases and
information systems: New trends in databases and informationsystems
(Vol. 241, pp. 257266), Genoa, Italy.
Chasin, R., Woodward, D., Witmer, J., & Kalita, J. (2013).
Extracting and displayingtemporal and geographical entities from
articles and historical events. TheComputer Journal, 57(2).
http://dx.doi.org/10.1093/comjnl/bxt112.
Chowdhury, G. (2003). Natural language processing. Annual Review
of InformationScience and Technology, 37, 5189.
Clark, A., Fox, C., & Lappin, S. (2010). The handbook of
computation linguistics andnatural language processing.
978-1-4051-5581-6. Wiley_Blackwell.
Clough, P. (2005). Extracting metadata for spatially aware
information retrieval onthe internet. In Proceedings of the 2005
workshop on geographic informationretrieval (pp. 2530), Bremen,
Germany.
Cruz, I. F., Ganesh, V. R., & Mirrezaei, S. I. (2013).
Semantic extraction of geographicdata from web tables for big data
integration. In Proceedings of the 7th workshopon geographic
information retrieval (pp. 1926), Orlando, Florida.
Cruz, I. F., & Xiao, H. (2005). The role of ontologies in
data integration. EngineeringIntelligent Systems for Electrical
Engineering and Communications, 13(4), 245.
Delboni, T. M., & Borges, K. A. (2007). Semantic expansion
of geographic web queriesbased on natural language positioning
expressions. Transactions in GIS, 3,377397.
Fernadez, M., Cantador, I., Lopez, V., Vallet, D., Castells, P.,
& Motta, E. (2011).Semantically enhanced information retrieval:
An ontology-based approach.Web Semantics: Science, Services and
Agents on the World Wide Web, 9(4),434452.
Gangemi, A., Guarino, N., Masolo, C., Oltramari, A., &
Schneider, L. (2002).Sweetening ontologies with DOLCE. In
Proceedings of the 13th internationalconference on knowledge
engineering and knowledge management, London, UK.
Goodchild, M. F., & Hill, L. L. (2008). Introduction to
digital gazetteer research.International Journal of Geographical
Information Science, 22(10), 10391044.
Gregory, I. N., & Hardie, A. (2011). Visual GISting:
Bringing together corpuslinguistics and geographical information
systems. Literary and LinguisticComputing, 26(3), 297314.
Grover, C., Tobin, R., Byrne, K., Woollard, M., Reid, J., Dunn,
S., et al. (2010). Use of theEdinburgh geoparser for dereferencing
digitized historical collections. Literaryand Linguistic Computing,
368(1925), 38753889.
Guarino, N. (1998). Formal ontology and information systems. In
Formal ontology ininformation systems. Proceedings of the 1st
international conference on formalontology in information systems
(pp. 315), Trento, Italy.
W. Wang, K. Stewart / Computers, EnviroHu, Z., Li, J. S., Zhou,
T. Sh., Yu, H. Y., Suzuki, M., & Araki, K. (2011).
Ontology-basedclinical pathways with semantic rules. Journal of
Medical Systems, 36,22032212.Imran, M., Elbassuomi, S., Castillo,
C., Diaz, F., & Meier, P. (2013). Extractinginformation nuggets
from disaster-related messages in social media. InProceedings of
the 10th international ISCRAM conference, Baden-Baden,Germany.
.
Janowicz, K., & Keler, C. (2008). The role of ontology in
improving gazetteerinteraction. International Journal of
Geographical Information Science, 22(10),11291157.
Janowicz, K., Scheider, S., Pehle, T., & Hart, G. (2012).
Geospatial semantics andlinked spatiotemporal data-past, present,
and future. Semantic Web, 3, 321332.
Jones, C. B., & Purves, R. (2008). Geographical information
retrieval. InternationalJournal of Geographical Information
Science, 22(3), 219228.
Jones, C. B., Purves, R. S., Clough, P. D., & Joho, H.
(2008). Modelling vague placeswith knowledge from the web.
International Journal of Geographical InformationScience, 22(10),
10451065.
Kalfoglou, Y., & Schorlemmer, M. (2003). Ontology mapping:
The state of the art. TheKnowledge Engineering Review, 18(1),
131.
Karimzadeh, M., Huang, W., Wallgrun, J. O., Hardisty, F.,
Pezanowski, S., Mitra, P.,et al. (2013). GeoTxt: A web API to
leverage place references in text. InProceeding of the 7th workshop
on geographic information retrieval (pp. 7273),Orlando,
Florida.
Kemp, Z., Tan, L., & Whalley, J. (2007). Interoperability
for geospatial analysis: Asemantics and ontology-based approach. In
Proceedings of the eighteenthAustralian database conference (Vol.
63, pp. 8392), Ballarat, Victoria, Australia.
Kontopoulos, E., Berberidis, C., Dergiades, T., &
Bassiliades, N. (2013). Ontology-based sentiment analysis of
twitter posts. Expert System with Applications,40(10),
40654074.
Lee, C. H., Wu, Ch. H., Yang, H. Ch., Wen, W. Sh., & Chiang,
Ch. Y. (2013). Exploitingonline social data in ontology learning
for event tracking and emergencyresponse. In 13 Proceedings of the
2013 IEEE/ACM international conference onadvances in social
networks analysis and mining (pp. 11671174), New York, NY.
Li, L., Goodchild, M., & Xu, B. (2013). Spatial, temporal
and socioeconomic patternsin the use of twitter and icker.
Cartography and Geographic Information Science,40(2), 6177.
Machado, I. M. R., Alencar, R. O. D., Campos, R. D. O., &
Clodoveu, A. D. (2011). Anontological gazetteer and its application
for place name disambiguation in text.Journal of the Brazilian
Computer Science, 17(4), 267279.
Manning, C. D., Raghavan, P., & Schtze, H. (2008).
Introduction to InformationRetrieval. Cambridge: Cambridge
University Press.
Noy, N. F. (2004). Semantic integration: A survey of
ontology-based approaches.SIGMOD Record, 33(4), 6570.
Purves, R. S., Clough, P., Jones, C. B., Arampatzis, A., Bucher,
B., Finch, D., et al. (2007).The design and implementation of
SPIRIT: A spatially aware search engine forinformation retrieval on
the internet. International Journal of GeographicalInformation
Science, 21(7), 717745.
Purves, R., & Jones, C. (2010). Geographic information
retrieval. SIGSPATIAL Special,3(2), 24.
Resnik, P., & Lin, J. (2010). Evaluation of NLP systems. The
Handbook ofComputational Linguistics and Natural Language
Processing, 57, 271.
Saggion, H., Funk, A., Maynard, D., & Bontcheva, K. (2007).
Ontology-basedinformation extraction for business intelligence. The
Semantic Web LectureNotes in Computer Science, 4825, 843856.
Sankaranarayanan, J., Teitler, B. E., Lieberman, M. D., &
Sperling, J. (2009).TwitterStand: News in tweets. In Proceedings of
17th ACM SIGSPATIALinternational conference on advances in
geographic information systems (pp. 4251), Seattle, WA, USA.
Sawsaa, A. F., & Lu, J. (2012). Developing a domain ontology
of information science(OIS). International Conference on
Information Society, 447452.
Schorlemmer, W. M., & Kalfoglou, Y. K. (2008).
Institutionalising ontology-basedsemantic integration. Applied
Ontology, 3(3), 131150.
Silverman, B. W. (1986). Density estimation for statistics and
data analysis. London:Chapman and Hall.
Sitter, A. D., Calders, T., & Daelemans, W. (2004). A formal
framework for evaluation ofinformation extraction. .
Smith, B., & Ceusters, W. (2010). Ontological realism: A
methodology forcoordinated evolution of scientic ontologies.
Applied Ontology, 5(34),139188.
Stewart, K., Fan, J. C., & White, E. (2013). Thinking about
spacetime connections:Spatiotemporal scheduling of individual
activities. Transactions in GIS, 17(6),791807.
Stewart Hornsby, K., & Wang, W. (2010). Representing dynamic
phenomena basedon spatiotemporal information extracted from web
documents. Extendedabstracts. In Proceedings of the sixth
international conference on geographicinformation science, Zurich,
Switzerland. .
Strotgen, J., Gertz, M., & Popv, P. (2010). Extraction and
exploration ofspatiotemporal information in documents. In
Proceedings of the 6th workshopon geographic information retrieval,
GIR 2010 (pp. 18), Zurich, Switzerland.
Teitler, B. E., Lieberman, M. D., Panozzo, D., Sankaranarayanan,
J., Samet, H., &Sperling, J. (2008). NewsStand: A new view on
news. In Proceedings of the 16thACM SIGSPATIAL international
conference on advances in geographic informationsystems (Vol. 18,
pp. 110), Irvine, California, USA.
nt and Urban Systems 50 (2015) 3040 39Vasardani, M., Winter, S.,
& Richter, K. F. (2013). Location place names from
placedescriptions. International Journal of Geographical
Information Science, 27(12),25092532.
-
Viera, A. J., & Garrett, J. M. (2005). Understanding
interobserver agreement: TheKappa statistic. Family Medicine,
37(5), 360363.
Volz, R., Kleb, J., & Mueller, W. (2007). Towards
ontology-based disambiguation ofgeographical identiers. In
Proceedings of WWW2007 Workshop I3: Identity,identiers,
identication, entity-centric approaches to information and
knowledgemanagement on the web, Banff, Canada. .
Wang, W. (2014). Automated spatiotemporal and semantic
information extraction forhazards. Ph.D. dissertation. University
of Iowa Library.
Wang, W., & Stewart, K. (2013). Extracting spatiotemporal
and semanticinformation from documents. In Proceeding of the 7th
workshop on geographicinformation retrieval (pp. 1718), Orlando,
Florida.
Wang, W., & Stewart, K. (2014). Creating spatiotemporal
semantic maps from webtext documents. In M.-P. Kwan, D. Richardson,
D. Wang, & C. Zhou (Eds.), Spacetime integration in geography
and GIScience: Research Frontiers in the US andChina. Dordrecht:
Springer.
Welty, C. A., & Murdock, J. W. (2003). IBM research report.
The semantics of multipleannotations. .
Wiegand, N., & Garcia, C. (2007). A task-based ontology
approach to automategeospatial data retrieval. Transaction in GIS,
11(3), 355376.
Worboys, M. F., & Stewart Hornsby, K. (2004). From objects
to events: GEM, thegeospatial event model. In Proceeding of the 3rd
international conference ongeographic information sciences (Vol.
3234, pp. 327344), Adelphi, MD.
40 W. Wang, K. Stewart / Computers, Environment and Urban
Systems 50 (2015) 3040
Spatiotemporal and semantic information extraction from Web news
reports about natural hazards1 Introduction2 Related work3
Constructing a hazard ontology4 Integrating the ontology with
spatial, temporal and semantic gazetteers5 Spatiotemporal and
semantic information retrieval for natural hazards6 A case
study-Hurricane Sandy, OctoberNovember, 20127 Evaluation of
approach8 Conclusions and future workReferences
