Semantic search engine for emergency management€¦ · emergency management . Information Systems...

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Semantic search engine for emergency management

Firenze, 25/06/2013 INSPIRE 2013: The Green Renaissance

M.Casu(*), M.Velluto(**), S.Griffa(**) (*)CELI/(**) CSI Piemonte

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IT for Public Services: we design and develop innovative public services, to simplify life for citizens and enterprises and to facilitate and speed up their relations with the Public Administration

long experience in geospatial data: since 1980 deals with the entire life

cycle of both the data and the processing functions, from their creation (through topographic

measurement or images interpretation) to their circulation

(from the map to the web)

CSI Piemonte: Consortium for Information Systems

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Titolo della presentazione

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What Celi is

• Università di Torino, Scuola Normale Superiore di Pisa, Brandeis , Stanford, DFKI, Nielsen

• 1999: born in Turin • 2006: Grenoble • 2007: BlogMeter (Milan) • 2010: CLS: spin-off in Trento • 2011: Madrid • 2012: Rome


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• Semantic Search Engines • Social listening and analytics

Success stories in semantic search: • Citizen portals, OPACs • Web Archives and digital

libraries (LaStampa, Guarini, LibrInLinea)

What Celi does


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Summary

• General issues

• Semantic interoperability in emergency management

• The case study • Step 1 • Step 2 • Step 3

• Opportunities and challenges


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Interoperability (the ability to collect and automatically integrate data from heterogeneous sources)

syntactical regarding differences

between the data format to be integrated

semantical regarding differences in

meaning of the data, in the context of provenance and

end-use sectors

Standardized web services (WMS, WFS, …)

Definition and query language for RDF

geodata (GeoSPARQL)

General Issues

referred to geospatial datasets


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GeoSPARQL • OGC Standard (2012) • Defines:

• an ontological vocabulary which encompasses the standard models (Simple Features, GML)

• a set of functions (topological relations) to use on top of SPARQL – ex. equals, disjoint, intersects, touches, crosses, within, contains, overlaps

SPARQL • query language for RDF • W3C Recommendation since 2008 (version 1.1 in march

2013)

General Issues


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Like spatial SQL, but in the semantic world – with no need of custom solutions

Advantages

Example: “define the targets in a flood event scenario, selecting civilian buildings within a flooding area”

General Issues

Why is important a good level in semantic interoperability: it may enable a decisive step up, ensuring that the sense of a conceptual model referred to a real-world object is understood

and shared between different users and different scopes Firenze, 25/06/2013 INSPIRE 2013: The Green Renaissance

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Semantic interoperability in EM

Semantic interoperability is particularly important in the emergency management

Information Systems supporting this kind of activity, in fact, must access external data sources, selecting and summarizing the ' useful ' ones for forecasting and prevention activities or emergency management


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The case study


An ongoing work started recently by CSI Piemonte, in the context of Information Systems supporting Civil Protection

activities

in particular the steps carried out so far and the next, required to design and implement a semantic search

engine, able to select data from different sources, transforming them into useful information for emergency

managers, always considering as central their cartographic component

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Step 1: Case study - Based upon a limited knowledge base - Not normalized data, no metadata available - Limited context - Semplified use case

Aim: performing ontology driven

searches (categories and tags)

Step 3: Search engine Online - More use cases, more users and more informations - Ontology improved by users - Extension to other contexts Aim: towards «expert systems»

- Strumento di supporto alla gestione

Step 2: Generalization - Increasing the knowledge base - a more complex use case

Aim: performing searches using ontology to make inferences

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The case study

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01/2

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Knowledge base about 50 text documents more than 20.000 features (shapefile) sources: Province of Turin, Arpa, CNR/Irpi Data quality: No normalization done or metadata available Area of interest: Floods events in Province of Turin Use Case: Search engine which uses contents Aim: performing ontology driven searches

Step 1

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Search filters derived from Ontology

Frequency: Number of documents connected with each filter

Search by any kind of text

Summary of search results: divided by type of document (textual or geographical) and by source

Tag cloud: Suggestion of other documents connected with specific search results

Step 1


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Search by any kind of text: ‘Frane a Moncalieri’

Search results: 7 features

Step 1


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Search by any kind of text: ‘Frane a Moncalieri’

Search results: 7 features

Service Sld, viewer

The engine invoke a specific service, transferring the parameters which identify the features resulting from the search

wms

Each single feature in the layers is considered as a document

Step 1

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06/2

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Knowledge base enlargement - relational databases - data from monitoring network - open data - Social network (SMART EU Project) Area of interest: Support to Emergency Management Use Case (more complex) - support to planning activities - support to emergency management Aim: to realize a framework for rules formalization enabling the system to perform authomatic inferences

Step 2

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Step 3: Search engine online and perspective Increasing: Users community - Civil Protection authorities Knowledge base - input users data Use cases (customizations) - Civil Protection Plans (local and sovralocal) - population involvement Ontology: Defined and validated thanks to the contribution of skilled users Extension to other contexts: - Urban Planning - Ontological view for complex ISs - summary of complex proceedings Aim: To create specific expert systems able to improve thanks to the use

Step 3

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Opportunities and challenges

Opportunities: Make knowledge available for similar applications in other areas (mainly teaching, but also health care, tourism, etc.) Challenges: • The very definition of intended meaning for a datset in a

certain context involves a sharing within the community of experts in that context

• The success of this type of application depends on the active involvement of users, for contribute to the generation of ' authoritative ' ontologies, and in sharing pieces of knowledge, translating them into an appropriate language. Non-trivial effort.


Semantic search engine for emergency management€¦ · emergency management . Information Systems...

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