PosterPDF Available

Abstract and Figures

In a natural disaster situation, it is crucial to orchestrate an efficient response, which prevents, or - at least - mitigates damages. Based on the assumption, that a well-informed decision maker can make the best decisions, s/he should have access to all available information. Thus, employing both internal and external data empowers decision makers. Since natural disasters are usually limited to a certain (previously unknown) area, it is of high importance to get to know about the local context of a disaster. Critical infrastructure, such as hospi-tals, energy supply, buildings with vulnerable beings (kindergarten, elder care, etc.) play an important role in crisis management. Nevertheless, a decision maker might not be aware of all of these places; yet, knowledge about these can often be found in external, public knowledge bases, such as Wikidata. Semantic Web Technology offers tools to integrate data from diverse data stores, offering a giant source of information. To improve situational awareness, this information should be tapped. By employing geospatial semantic features of knowledge bases, it is possible to integrate several data stores and only find information, that is valid within the range of a disaster and therefore of interest to a decision maker. The poster presents the integration of Wikidata as an external knowledge-base into a Decision-Support-System by using federated queries. Through employing geo-spatial semantic features, only relevant information is retrieved.
Content may be subject to copyright.
The project leading to these results has received funding
from the European Union’s Horizon 2020 research and
innovation program under Grant No. 700475.
Motivation
The extent and frequency of natural disasters grows. In disaster management, authorities must cope with
challenging situations characterized with overwhelming and confusing information streams. Data can foster
situational awareness, yet informational overflow must be prevented.
to empower decision makers, we employ federated queries with geospatial functions for the
identification of places in the hot-zone that need special action during a disaster.
Taking external knowledge sources into respect can support decision makers by complementing their
knowledge. Pictures off important places, the indication of hazards, or even only the indication of a
sensible place can be valuable information.
Since the extent of a disaster is usually confined to an allocable area, the selection of information based
on geospatial metadata prevents the decision maker from informational overflow. Thepresented
approach is integrated within the beAWARE platform.
holistically supports decision makers through all
phases of a natural disaster related crisis. Through new tools for
information analysis and presentation, situational awareness is improved.
Collected information is evaluated based on its
geospatial semantics. If it is not located within a
certain area, it is not considered:
Federated querying for supplemental data from external knowledge bases to ensure situational
awareness and context consideration:
SELECT ?incidentReport ?incidentLocation ?placeOfInterest ?picture
WHERE {{[...] WHERE {
?incidentReport a beaware:IncidentReport;
beaware:hasReportLocation ?incidentLocation. }}
{SERVICE <https://query.wikidata.org/sparql> {
SERVICE wikibase:around {
?placeOfInterest wdt:P625 ?location.
bd:serviceParam wikibase:center ?incidentLocation;
wikibase:radius "1".} […]
FILTER EXISTS { ?placeOfInterest wdt:P31/wdt:P279* wd:Q811979 }
[…]}}}
EMPLOYING GEOSPATIAL SEMANTICS AND SEMANTIC WEB
TECHNOLOGIES IN NATURAL DISASTER MANAGEMENT
Tobias Hellmund,
Manfred Schenk, Philipp Hertweck and Jürgen Moßgraber
© OpenStreetMap
P
P
P
WIKIDATA
© OpenStreetMap
Employing Geospatial Semantics and Semantic Web
Technologies in Natural Disaster Management
Tobias Hellmund*1, Manfred Schenk1[0000-0001-6463-0704], Philipp Hertweck1, and Jürgen
Moßgraber1[0000-0002-2614-4980]
1 Fraunhofer Institute of Optronics, System Technologies and Image Exploitation
IOSB, Karlsruhe, Germany
{tobias.hellmund, manfred.schenk, philipp.hertweck,
juergen.mossgraber}@iosb.fraunhofer.de
https://www.iosb.fraunhofer.de
Abstract. In a natural disaster situation, it is crucial to orchestrate an efficient
response, which prevents, or - at least - mitigates damages. Based on the assump-
tion, that a well-informed decision maker can make the best decisions, s/he
should have access to all available information. Thus, employing both internal
and external data empowers decision makers. Since natural disasters are usually
limited to a certain (previously unknown) area, it is of high importance to get to
know about the local context of a disaster. Critical infrastructure, such as hospi-
tals, energy supply, buildings with vulnerable beings (kindergarten, elder care,
etc.) play an important role in crisis management. Nevertheless, a decision maker
might not be aware of all of these places; yet, knowledge about these can often
be found in external, public knowledge bases, such as Wikidata. Semantic Web
Technology offers tools to integrate data from diverse data stores, offering a giant
source of information. To improve situational awareness, this information should
be tapped. By employing geospatial semantic features of knowledge bases, it is
possible to integrate several data stores and only find information, that is valid
within the range of a disaster and therefore of interest to a decision maker. The
poster presents the integration of Wikidata as an external knowledge-base into a
Decision-Support-System by using federated queries. Through employing geo-
spatial semantic features, only relevant information is retrieved.
Keywords: Crisis Management, Geospatial Semantics, Situational Awareness,
Federated Queries
1 Introduction
During a crisis situation, responsible managers have to take momentous decisions:
while good decisions can mitigate or even prevent damage, bad decisions can allow or
even amplify the extent. In a flood or large-scale fire event for example, authorities
must decide if buildings in the endangered area need special protection or whether they
have to be evacuated. Based on the assumption, that good decisions are likely to be
made, when all available information is taken into account, a decision maker should
2
have access to all available data sources to ensure situational awareness. Nevertheless,
information overload must be prevented [1]. To do so, Decision Support Systems (DSS)
disburden decision makers by (amongst other functionality) preprocessing and select-
ing relevant information and appropriately presenting their informational content [2].
We present an approach, in which an external knowledge base is integrated into an
existing DSS through Semantic Web Technology whereas irrelevant information is
sorted out by geospatial aspects.
2 Related Work
The poster presents an approach to retrieve data, based on geospatial semantics
through federated queries, as well as visualizing the data and integrating it into a crisis-
management context. Related work from all the named domains, preliminary work, as
well as the project in which this approach was developed, is going to be presented be-
low.
The discussed approach was developed in the context of the project beAWARE1.
It’s holistic approach yields into a single DSS, providing support over all phases of a
natural disaster, including the forecasting and early warning phases until the end and
reflection of such. Amongst other things, the beAWARE-platform comprises new tools
for information retrieval and analysis, e.g. algorithms analyzing multi-modal input in
form of pictures, videos, speech recordings and written texts from social media or mes-
sages directly sent to the platform. To ensure the correct understanding of the data, it is
semantically integrated through the beAWARE Ontology, where the spatiotemporal
context is saved. This ontology is presented by Kontopoulos et al. [3]. The interested
reader can access the ontology on https://github.com/beAWARE-project/ontology.
In the presented approach, we utilize the Knowledge Base (KB) Wikidata, in which
RDF-structured data is publicly made available. It offers data and information about a
broad field, including places of interest during a natural disaster. Since it also contains
geospatial metadata, it could offer information that is a priori not known to a decision
maker in a natural disaster situation [4]. An often-criticized aspect of Wikidata is data
vandalism requiring the verification of retrieved data [5].
For geospatial semantics, GeoSPARQL was established as a standard developed by
the Open Geospatial Consortium. It defines a top-level ontology for spatial objects and
geometries to explicitly capture the semantics of these. Additionally, it standardizes
functions to support topological queries [6] [7].
Schulze et al. present an approach to combine datasets from different sources, such
as mobile devices, Social Media and Semantic Web to empower authorities to detect
natural disasters. The application is an event detection system for catastrophic events
from large data streams. Through information collection, classification and semantic
enrichment, the operator of the system shall gain situational awareness. By employing
Linked Open Data, the expected usefulness of collected is calculated. Yet, geospatial
semantics are not part of the approach [8].
1 https://beaware-project.eu/
3
3 Geospatial Semantics in Crisis Situations
In natural disasters, time is the enemy. Therefore, a decision maker has to gather all
relevant information quickly to improve situational awareness and ensure a timely
disaster response. Since the extent, urgency and need for action of a crisis is highly
dependent on its geographic context, geospatial semantics offer a good filtering pos-
sibility to sort out irrelevant data. Semantic Web Technologies and geospatial se-
mantics offer the possibility to exactly describe the user’s needs and monitor differ-
ent data stores for data of interest. Through geospatial semantics, a user can retrieve
solely data close to a specific point or within a certain area.
In the following, the pipeline to retrieve data with the correct geographic context
from an external data store is described. The usage of this data is simplified depicted
in Fig. 1. During a crisis, heterogeneous data is collected through various sensors.
The raw data is analyzed and semantically enriched with geospatial information ac-
cording to the ontology. This data is stored internally and, by convention, considered
trustworthy. Whenever an analysis tool identifies a need for action, an incident re-
port is created in the beAWARE Knowledge Base. This incident is presented to the
decision maker in the DSS (blue path). On request, the user can query for local con-
text from the external data store Wikidata, whereas the location of interest is defined
through the geospatial metadata of the incident report. Now, information about rel-
evant structures close by and corresponding pictures are presented within a map in
the DSS (orange path) and can be taken into account by the authorities. Through the
identification of possibly endangered, so far undetected infrastructure, the decision
maker can now coordinate rescue actions with more complete situational awareness.
Fig. 1. A high-level view on beAWARE’s semantic information retrieval
4 Querying and mapping information from different knowledge
sources
To retrieve the situational context of an incident report, a query as shown below is
used. The query is split in two sub-queries. Firstly, all incident reports in the internal
knowledge base and their latitude ?lat and longitude ?lon are retrieved (line 3-11). The
function STRDT in the SELECT function constructs a geo:wktLiteral, specified by the
4
geoSPARQL ontology, as ?incidentReportLocation (line 3-4). The second subquery is
send to wikidata’s SPARQL-endpoint and searches for all instances and instances of
subclasses of architectural structures, that are within 1kilometer range of ?inciden-
tReportLocation (line 12-21).
SELECT DISTINCT ?incidentReportLocation ?name ?location ?place
?picture WHERE {
{SELECT DISTINCT (STRDT(CONCAT("Point(", STR(?lon), " ",
STR(?lat), ")"), geo:wktLiteral) AS ?incidentReportLocation)
?name ?picture WHERE {
?incidentReport a beaware:IncidentReport;
beaware:instanceDisplayName ?name;
beaware:hasReportLocation ?location.
?location beaware:latitude ?lat;
beaware:longitude ?lon.
}
}{SERVICE <https://query.wikidata.org/sparql> {
SERVICE wikibase:around {
?place wdt:P625 ?location.
bd:serviceParam wikibase:center
?incidentReportLocation;
wikibase:radius "1".
}
OPTIONAL { ?place wdt:P18 ?picture. }
FILTER EXISTS { ?place wdt:P31/wdt:P279* wd:Q811979 } .
FILTER NOT EXISTS { ?place wdt:P31 wd:Q15893266 }.
}}}
The retrieved results are depicted on a map (see Fig. 2), as shown below. Internal
knowledge, integrated into the beAWARE KB is depicted in fully colored needle points.
External knowledge coming from wikidata is depicted with a transparent needle point.
The letter p indicates the availability of a picture showing the element at this position.
Fig. 2. Mapping data retrieved with geo-semantic queries in Openstreetmap
5
5 Conclusion
The proposed poster shows the experimental application of Semantic Web Technol-
ogies using federated SPARQL-queries with the geospatial functions of Wikidata in a
natural disaster scenario. By integrating external knowledge sources, situational aware-
ness can be improved and authorities can make better grounded decision, characterized
by better management of available first responders and resources.
Still, the knowledge coming from an external source must be validated. Further, the
geospatial functions of Wikidata are not fully GeoSPARQL compliant and limited; still,
the expressiveness was sufficient within the presented application. In a next step, the
retrieved information from external knowledge bases must be refined. Not only archi-
tectural structures, but also cultural events might be of interest for a decision maker.
6 Acknowledgement
This project has received funding from the European Union's Horizon 2020 research
and innovation programme under grant agreement H2020-700475 beAWARE.
Reference
1. M. J. C. van den Homberg, R. Monné and M. R. Spruit: Bridging the Information Gap:
Mapping Data Sets on Information Needs in the Preparedness and Response Phase.
In:
Technologies for Development, pp. 213-225 (2016).
2. J. Moßgraber, Ein Rahmenwerk für die Architektur von Frühwarnsystemen. KIT Scientific
Publishing, Karlsruhe (2016).
3. E. Kontopoulos, P. Mitzias, J. Moßgraber, P. Hertweck, H. van der Schaaf, D. Hilbring, F.
Lombardo, D. Norbiato, M. Ferri, A. Karakostas, S. Vrochidis and I. and Kompatsiaris:
Ontology-
based Representation of Crisis Management Procedures for Climate Events. In:
1s
t International Workshop on Intelligent Crisis Management Technologies for Climate
Events (ICMT 2018), Rochester NY, USA (2018).
4. „Wikidata,“ wikimedia, [Online]. Available: https://www.wikidata.org/. [Last accessed
2019/07/23].
5. S. Heindorf, M. Potthast, B. Stein and G. Engles. Vandalism Detection in Wikidata. In:
Proceedings of the 25th ACM International on Conference on Information and Knowledge
Management , Indianapolis, IN, USA (2016).
6. R. Battle and D. Kolas. GeoSPARQL: Enabling a Geospatial Semantic Web. In Semantic
Web Journal, vol. 3, no. 4, pp. 355-370 (2011).
7. Open Geospatial Consortium Homepage,
https://www.opengeospatial.org/standards/geosparql, last accessed 2019/06/13.
8. A. Schulz, H. Paulheim und F. Probst, „Crisis Information Management in the Web 3.0
Age,“ in Proceedings of the 9th International ISCRAM Conference, Vancouver, Canada,
2012.
... The following two examples show the wide range of usage of semantic technologies. Semantic integration [4] [5] can be applied in the context of crisis response to support decision support [6]. Another example shows the implementation of semantic data to protect cultural heritage [7]. ...
Conference Paper
Full-text available
Recently, the usage of triplestores has increased in complex computer systems. Traditionally, they are used for representing static knowledge. In the last years, systems started using semantic triplestores in highly dynamic scenarios, e.g., in the context of civil protection. In these use cases, performance characteristics are more and more important. There are various aspects influencing the query performance. We have noticed that already the query structure has a significant impact on the execution time. SPARQL Protocol And RDF Query Language (SPARQL) is a widely used standard for querying triplestores. In this work, we have developed SPARQL query patterns and evaluated their performance characteristics. For this, a literature review was done to select a suitable benchmark. As a result, we provide eight recommendations for formulating SPARQL queries. These can be easily used by everybody without a deeper knowledge about the implementation of the triplestore, which contains the desired data.
Conference Paper
Full-text available
One of the most critical challenges faced by authorities during the management of a climate-related crisis is the overwhelming flow of heterogeneous information coming from humans and deployed sensors (e.g. cameras, temperature measurements, etc.), which has to be processed in order to filter meaningful items and provide crisis decision support. Towards addressing this challenge, ontologies can provide a semantically unified representation of the domain, along with superior capabilities in querying and information retrieval. Nevertheless, the recently proposed ontologies only cover subsets of the relevant concepts. This paper proposes a more "all-around" lightweight ontology for climate crisis management, which greatly facilitates decision support and merges several pertinent aspects: representation of a crisis, climate parameters that may cause climate crises, sensor analysis, crisis incidents and related impacts, first responder unit allocations. The ontology could constitute the backbone of the decision support systems for crisis management. Keywords Crisis management, ontology, semantic integration, decision support, description logics. Kontopoulos et al. Ontology-based Representation of Crisis Management WiPe/CoRe Paper-Track Name
Article
Full-text available
The effectiveness of emergency response largely depends on having a precise, up-to-date situational picture. With the World Wide Web having evolved from a small read-only text collection to a large-scale collection of socially created data accessible both to machines and humans alike, with the advent of social media and ubiquitous mobile applications, new sources of information are available. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. In this paper, we show an approach for turning massive amounts of unstructured citizen-generated content into relevant information supporting the command staff in making better informed decisions. We leverage Linked Open Data and crowdsourcing for processing data from social media, and we show how the combination of human intelligence in the crowd and automatic approaches for enhancing the situational picture with Linked Open Data will lead to a Web 3.0 approach for more efficient information handling in crisis management.
Book
Frühwarnsysteme dienen zur möglichst frühzeitigen Information über eine sich anbahnende oder auftretende Gefahr, um Personen und Organisationen die Möglichkeit zu geben entsprechend darauf reagieren zu können. Die Konzeption eines Frühwarnsystems stellt komplexe Herausforderungen an die Systemarchitekten, hierzu liefert die vorliegende Arbeit ein Rahmenwerk für die Architektur zukünftiger Frühwarnsysteme.
Article
As the amount of Linked Open Data on the web increases, so does the amount of data with an inherent spatial context. Without spatial reasoning, however, the value of this spatial context is limited. Over the past decade there have been several vocabularies and query languages that attempt to exploit this knowledge and enable spatial reasoning. These attempts provide varying levels of support for fundamental geospatial concepts. In this paper, we look at the overall state of geospatial data in the Semantic Web, with a focus on the upcoming OGC standard GeoSPARQL. GeoSPARQL attempts to unify data access for the geospatial Semantic Web. We first describe the motivation for GeoSPARQL, then the current state of the art in industry and research, followed by an example use case, and the implementation of GeoSPARQL in the Parliament triple store.
Vandalism Detection in Wikidata
  • S Heindorf
  • M Potthast
  • B Stein
  • G Engles
S. Heindorf, M. Potthast, B. Stein and G. Engles. Vandalism Detection in Wikidata. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, Indianapolis, IN, USA (2016).