The document discusses crisis information management in the Web 3.0 age. It proposes combining social and semantic web data to provide structured and relevant information to enhance situational awareness for emergency responders. A process is described that involves collecting information from citizens and social media, classifying it, enriching it with linked open data, and providing it to decision makers to improve response. Future work could involve user studies and applying machine learning to the classification process.

1. Crisis Information Management
in the Web 3.0 Age
Axel Schulz
Heiko Paulheim
Florian Probst
April, 23 2012
Telecooperation Lab
The work is partly funded by a grant of the German Federal Ministry for Education and Technische Universität Darmstadt
Research

2. Motivation
No
Do we need additional
rescue squads?
Task 1
Task 2
Fragmented Situational Picture
Apps
Yes
? Additional Information
Citizens Web Data
Linked Data
Experts Social Media
• For making informed decisions a decision maker must be aware of all relevant
elements in his environment
• Currently decision makers are dependent on information provided by onsite rescue squads
• The information landscape is changing rapidly, but potentially valuable information
sources are not used by the command staff
• Heterogeneous and unstructured nature of the data, lack of time to analyze flood of data
Crisis Information Management in the Web 3.0 Age 2

3. Vision for Decision Making in
Emergency Management
No
Do we need additional
rescue squads?
Task 1
Task 2
Apps
Additional Information
Citizens Web Data
Yes Linked Data
Experts Social Media
Enhanced Situational Picture
• Combing Social and Semantic Web to provide structured and relevant
information for enhancing the situational picture
 More informed decisions, shorter reaction times, overall increase of
successful operations
Crisis Information Management in the Web 3.0 Age 3

4. Related Work
 Few approaches for structuring and augmenting information provided by
citizens or that utilize data sources as Linked Open Data (LOD)
 Tweak the Tweet project proposes a syntax to support a more efficient extraction of
relevant data
 Ushahidi as a live example for filtering user-generated information (Okolloh, 2008)
 Architecture for applying the Linked Data paradigm to represent and process
information in emergencies (De Faria Cordeiro, Marino, Campos, Borges, 2011)
 Texts from social networks are turned into structured information based on Linked
Open Data principles and enriched using volunteers (Ortmann, 2011)
 Social media is constantly crawled and tagged with links to LOD in SemSor (Heim and
Thom, 2011), for retrieval of related information
Approaches have shortcomings when it comes to deal with the large amount
of information from social networks and LOD
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5. Web 3.0 Process for Crisis Information
Management
Information Collection
Incident Reporter Information Collection
Social Media • Gathering information from social platforms
• Incident Reporter mobile application for submission of images,
I1 audio and textual descriptions as reports from onsite reporters
Cannot directly be used to satisfy the information need of the
decision maker
Unstructured, unsorted information base
Information Overflow
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6. Web 3.0 Process for Crisis Information
Management
Information Collection
Incident Reporter Information Classification
Social Media • Reduce the incoming flood of information to a relevant set
which suffices the information need
I1 • Approach: “Question-guided Relevance Rating”
Information • Command staff asks questions to articulate information need,
Classification e.g. "Are there still people in the burning building?"
• Reports that are helpful for answering questions are
considered to be relevant for the command staff
I2
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7. Web 3.0 Process for Crisis Information
Management
Information Collection
Incident Reporter Pre-Classification
Social Media • Pre-filtering of information base by augmenting textual
information with semantic annotations and references to
I1 entities in Linked Open Data
Information • E.g. “It is burning at TUD” -> retrieve the entity for label TUD ->
Classification “Technische Universität Darmstadt”
Automatic • Result: Similarity score between information objects to pre-sort
preclassification
I2
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8. Web 3.0 Process for Crisis Information
Management
Information Collection
Incident Reporter Crowd-based classification
Social Media • Crowdsourcing is applied to filter according to the relevancy for
a specific question
I1 • Report Classifier application is based on Question-guided
Information Relevance Rating
Classification • All citizens can access the application from everywhere and rate
Automatic a report with respect to usefulness for answering a question
preclassification
Crowd-based
• Relevance model is used to determine a relevance score for
classification each rated information object
I2
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9. Report Classifier Web Application
(Demo Session on Tuesday)
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10. Web 3.0 Process for Crisis Information
Management
Information Collection
Incident Reporter Information Enrichment
Social Media • Further augmentation with additional information
I1
Explanation
Information • Missing Knowledge -> Existing information must not be self-
Classification
explanatory
Automatic
preclassification • E.g. additional information like the chemical properties of
Crowd-based sulfur for risk assessment can be provided
classification
• Create non-obstrusive references to entities in Linked Open
I2
Datasets
Information
Enrichment Extension
Explanation • Additional background information can be provided
Extension
• E.g. important buildings near an incident (chemistry plants,
hospitals)
Crisis Information Management in the Web 3.0 Age 10

11. Mashup for Identifying Critical
Infrastructure (Demo Session Tuesday)
• MICI submitted for the AI Mashup Challenge 2012 (ESWC ‘12)
Crisis Information Management in the Web 3.0 Age 11

12. Web 3.0 Process for Crisis Information
Management
Information Collection
Incident Reporter
Social Media
I1
Information
Classification
Automatic
preclassification Result is a structured information base that could
Crowd-based
classification
enhance the situational picture of the decision
I2 maker
Information
Enrichment
Explanation
Extension
I3
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13. Conclusion & Outlook
• Combination of Social and Semantic Web to provide structured
and relevant information for enhancing the situational picture
• Reducing the information overflow using crowd-based classification
• Linked Open Data for clustering and enhancing information base
 Future Work
 User Study: Evaluation of applicability in real environments
 Apply machine learning methods for pre-classification
 The information provided by LOD also needs to be classified
 Alternative ways of clustering of unstructured social sensor streams, e.g. as
virtual citizen sensors
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14. THANK YOU!
Questions?
Can also be addressed to:
axel.schulz@sap.com
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15. Bibliography
 De Faria Cordeiro, K., Marino, T., Campos, M. L. M., and Borges, M. R. S. (2011) Linked Open Data
and the Design of Information Infrastructure for Emergency Management Systems. ISCRAM 2011,
1-6.
 Heim, P. and Thom, D. (2011) SemSor: Combining Social and Semantic Web to Support the Analysis
of Emergency Situations. Proceedings of the 2nd Workshop on Semantic Models for Adaptive
Interactive Systems SEMAIS.
 Okolloh, O. (2008) Ushahidi, or ‘testimony’: Web 2.0 tools for crowdsourcing crisis information.
Participatory Learning and Action 59, 65-70.
 Ortmann, J., Limbu, M., Wang, D., and Kauppinen, T. (2011) Crowdsourcing Linked Open Data for
Disaster Management. Framework.
 http://wiki.crisiscommons.org/wiki/Tweak_the_Tweet [Accessed: 16-Feb-2011]
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16. Linked Open Data Principles
 Use URIs as names for things
 Use HTTP URIs so that people can look up those names.
 When someone looks up a URI, provide useful information, using
the standards (RDF*, SPARQL)
 Include links to other URIs. so that they can discover more things.
(http://www.w3.org/DesignIssues/LinkedData.html)
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17. Example from DBPedia showing some
data about Darmstadt [1]
 [1] Paulheim & Fürnkranz (2011)

18. Relevance Models
Relevance Aware Unguided Question Guided
Experts Relevance Rating Relevance Rating
Time Rating Rating
Cool Down Cool Down
Karma Karma
Domain Experts