Geo know general presentation 2013

EU-FP7 LOD2 Project Overview . 02.09.2010 .

http://lod2.eu

Creating Knowledge out of Interlinked Data

http://geoknow.eu

GeoKnowMaking the Web an Exploratory Place for

Spatial Data

Collaborative Project 2012-2015in Information and Communication Technologies

Project No. 318159Start Date 01/12/2012

EU-FP7 GeoKnow. 21.01.2013 . http://geoknow.eu

Making the Web an Exploratory Place for Geospatial Data

Achievements1. Extension of the Web

with a data commons (currently amounting to 25 Bn. facts)

2. Vibrant, global RTD community

3. Industrial uptake begins (e.g. BBC, Thomson Reuters, Eli Lilly)

4. Emerging governmental adoption in sight

5. Establishing Linked Data as a deployment path for the Semantic Web.

The Spatial Data Web: Achievements and Challenges

Challenges1. Big Data: Large volumes

of frequently updated data

2. Coherence: Relatively few, expensively maintained links

3. Quality: partly low-quality data and inconsistencies

4. Data Consumption: large-scale processing, schema mapping and data fusion for enterprises

5. Simplicity: easily create and deploy spatial semantic web applications

Web - a global, distributed platform for data, information and knowledge integration

Exposing, sharing, and connecting pieces of data on the Semantic Web using RDF

Large Spatial Databases (OpenStreetMaps, Google Maps etc.) integrated in many applications



GeoKnow in a Nutshell

Spatial Knowledge Store & Co-Evolution

Triples Stores RDF Views

GIS Databases

Linked Data

protected public

E-Commerce Data

Supply Chain Management Data

GeoKnow GeneratorLinkedData

Access

Spatial Linked Data

Fusing

Interlinking

Aggregation

Metrics

Spatial Authoring / Browsing Tools

Faceted Browsing

Spatial Widgets

Adaptive Authoring

SPARQL / GeoSPARQL

WP1 WP1

WP2 WP2

WP3 WP3

WP4 WP4

WP6WP6

WP5WP5

Open Data /LOD Cloud

… further use cases …



Work Packages

WP4: Spatial-semantic exploration, visualization, analysis & authoring interfaces

WP4: Spatial-semantic exploration, visualization, analysis & authoring interfaces

WP2: Semantics based geospatial data management

WP2: Semantics based geospatial data management

WP3: Spatial knowledge aggregation, fusing & quality assessment

WP3: Spatial knowledge aggregation, fusing & quality assessment

WP1: Requirements, design, prototyping, interfaces definition, component integration and GeoKnow GeneratorWP1: Requirements, design, prototyping, interfaces definition, component integration and GeoKnow Generator

WP5:GeoKnow for Supply chain managementWP5:GeoKnow for Supply chain management WP6:GeoKnow for E-CommerceWP6:GeoKnow for E-Commerce

WP7: Dissemination community building Exploitation & standards, fertilisation

WP7: Dissemination community building Exploitation & standards, fertilisation

WP8: Project ManagementWP8: Project Management



Consortium

Institute for Applied InformaticsGermany

OpenLink SoftwareUnited Kingdom

BroxGermany

OntosSwitzerland

UnisterGermany

Athena Research andInnovation CenterGreece



WP1: Requirements, Design, Benchmarking, Component Integration Requirements collection and analysis Continuous Benchmarking GeoKnow Generator design and development Component integration

Different data sources: private, pubic, RDF, non-RDF, static, streams.

Performance monitoring and Secured data in all layers

Data enrichment and continuous quality assurance

New visualisation paradigms

GeoKnow Generator



WP2: Semantics-Based Geospatial Information Management Objective: Develop the storage, query execution and optimization capabilities of the storage

engine to meet the Geospatial requirements of the project.

Tasks:

• Geospatial query optimization - The cost model for geospatial queries will merge query optimization and execution by partially executing joins during optimization

• Geospatial clustering - adds geospatial clustering support for rearranging physical storage according to geospatial criteria

• Distributed geospatial capabilities – Showcase advances in scale-out capabilities with large geospatial data, e.g. all of OSM, with heavy online query load and concurrent updates.

• Geospatial problem solving – Implementation of complex application logic co-resident with the data, required for supply chain route planning.

• Exposing INSPIRE data as Linked Data - provide technical solutions and tools to expose INSPIRE data and metadata as geospatial Linked Data.



WP3: Spatial Knowledge Aggregation, Fusing & Quality Assessment

1. Spatial knowledge mappingMotivation: How do we interlink spatial and non-spatial RDF data?Goal: Lift implicit geographic references in RDF data and interlink with spatial and non-spatial data, transform geospatial data to RDF with emphasis on big, evolving, geospatial data e.g. interlink RDF data with DBpedia,

Geonames e.g. transform typical shapefiles to RDF and

interlink with other data e.g. continuously process and transform

OpenStreetMap data (earth-scale, thousands revisions/day) to RDF

sameAs

Combine crowdsourced /open and closed spatial RDF data to produce data of increased coverage, accuracy, semantic enrichment,

timeliness and valueLeverage the wisdom of crowds in the geospatial Data Web



WP3: Spatial Knowledge Aggregation, Fusing & Quality Assessment2. Spatial knowledge fusion

Motivation: How do we combine different spatial RDF data, with differences in RDF representation, metadata and geometries for the same geographical features?Goal: Develop algorithms to fuse geospatial RDF data: map different RDF representations of geometry, merge metadata associated with spatial objects, combine representations of relations between spatial objects e.g. identify same POIs from different data sets with differences

in location (e.g. coordinates), metadata (e.g. name, working hours), and relations (e.g. within a mall, south of a square)

3. Quality-aware spatial knowledge aggregationMotivation: Volunteers produce valuable spatial knowledge in the Web. How can we guide them towards purposeful contributions without restricting them, and consolidate their efforts?Goal: Develop algorithms to semantically consolidate categories and produce a community-driven consolidation process e.g. category cretan_cuisine becomes a subcategory of

greek_cuisine e.g. OpenStreetMap users consolidate greek_cuisine with

greek_restaurant and cretan_menu



WP3: Spatial Knowledge Aggregation, Fusing & Quality Assessment4. Metrics for volunteered (crowdsourced) geographic information

Motivation: Crowdsourced geoinformation can surpass the quality of official/commercial /closed maps. How do we apply and leverage this knowledge source?Goal: Define metrics to compare different maps from user-contributed geospatial information modeled in RDF (coverage, precision, pertinence, timeliness) E.g. compare coverage of a specific category between two regions of a

city

Count/

compare

density

of streets

Compare

precision

of geometry

5. Quality assessment for geoinformationMotivation: We have fused crowdsourced geoinformation with official/commercial/closed maps. How do we measure the quality of this value added data source?Goal: Define, extend, adapt quality metrics for spatial RDF, and develop methods to detect/repair errors and enrichment. E.g. produce a map with more/accurate POIs based on OSM data



WP4: Spatial-semantic Browsing, Visualization, Authoring Interfaces1. Spatial-semantic visualization and exploration

Motivation: Given spatial RDF datasets, how to quickly generate previews and easily turn them into interactive, user friendly, visualizations and widgets (making use of vector and raster data)?

Goal: Develop reusable user interface components for display of geographic feature information on a map and as forms. Leverage re-use of vocabularies by means of default and customizable presentations (e.g. for hotels, events, routes, SC network, …)Furthermore develop library components for dealing with different RDF representations and level of detail of geographic data. Offer keyword and facetted search.



WP4: Spatial-semantic Browsing, Visualization, Authoring Interfaces2. Adaptive spatial-semantic authoring and curation

Motivation: There are many reasons for the need to edit RDF data, however without the right tool this is often very cumbersome; Examples:

• After displaying recently geocoded data on a map errors become obvious.• A use case requires introducing new labels to POIs that include country

and ZIP codeGoal: Develop re-usable and composable widgets for supporting single editing, batch editing as well as reconciliation of spatial RDF data.The requirement for authoring can often stem from data quality issues. As such there will also be UI components offering access to (some) of the functionality provided by the quality assurance tools.

3. Spatial social networkingMotivation: Social networking is ubiquitous. Users and organisations need to be able to register for relevant updates. This applies to both projects managed in the GeoKnow Generator itself, as well as generated applications.Goal: Enhance the GeoKnow Generator with subscription, filtering, and notification mechanisms based on the Open-Social standard. Furthermore, implement adapters to popular social networks, such as Facebook, Google+, Twitter, LinkedIn.



WP4: Spatial-semantic Browsing, Visualization, Authoring Interfaces4. Public-private spatial data co-evolution

Motivation: Example: A tourism portal combines a private hotel database with public OpenStreetMap data for becoming capable of answering queries such as “find all hotels near beaches”. For convenience, users can fix mistakes on the portal. However, when the hotel database or OpenStreetMap evolves (re-import or incremental updates), how can the prior fixes be retained?Goal: Identify types of enterprise RDF data synchronization workflows, define and implement tools that support them. This will include ETL processes, query federation, transformation/patching of data and change set propagation.



WP5: Spatial Linked Data in the Supply Chain

• Logistics companies face big-data challenges when dealing with complex international tiered structures

• Information integration is critical for effective enterprise processes

• Geospatial linked data may help to derive a unified collaborative spatial view on important parts of a logistic process

• Linked data applications make different private supply chain data points available and connect these layers with intelligent and secure APIs

• Close to real time observation of information flows (e.g. materials, products, other supply chain assets)

• May improve supply chain performance• Quick integration of additional supply chain partners and

information layers



WP6: GeoKnow for E-Commerce

Same Challenges of E-Commerce Applications and Semantic Web Community• independent, different data providers• integration of entities without unique identifiers• data sets are growing rapidly• match user demand with technology push

Validation of GeoKnow results within e-Commerce scenario: this use case is the perfect match for evaluation and research on semantic web challenges

Objectives: Answer central questions:• What kind of new products is my customer likely willing to buy?• Which geographical regions are most suitable for a special event?• How to integrate internal data with the open linked data by interlinking with

geographical data, social network structures from many different sources?• How to provide the information to a user via search applications?



WP7: Dissemination, Community Building, Exploitation & Standards

Dissemination• www.geoknow.eu• Flyer and stickers• Social Networks• Community Groups • Conference presenting• Web-based showcases

Exploitation• Open source• GeoKnow generator exploitation in further use cases

Standardization• W3C SPARQL Working Group• W3C Relational Data Bases to RDF Working Group• W3C Semantic Web Deployment Working Group

http://www.geoknow.eu/



WP8: Project Management

Means

Total Budget: 4,1 M€

Total Funding: 2,95 M€

Total Resources: 495 PM

Partners: 6

Project

Instrument: Collaborative Project (STREP)

Objective: Intelligent Information Management

Call: FP7-ICT-2011.4.4

Duration: 12/2012 – 12/2015

LOD2 Title . 02.09.2010 . http://lod2.eu

Address

Institute for Applied InformaticsUniversity of LeipzigNeumarkt 2004109 LeipzigGermany

Phone: +49 341 3928738 0Fax: +49 341 3928738 9

Coordinator

Thanks for your attention!

http://geoknow.eu

Contact


Dr Jens LehmannScientific Project LeaderPhone: +49 (341) 97-32260Fax: +49 (341) 97-32329Email: [email protected] Web: http://jens-lehmann.org

Sandra PrätorProject ManagerPhone: +49 (341) 97-32332Fax: +49 (341) 97-32329Email: [email protected] http://aksw.org/SandraPraetor.html

mailto:[email protected]

http://jens-lehmann.org/

mailto:[email protected]

http://aksw.org/SandraPraetor.html

http://aksw.org/SandraPraetor.html

Geo know general presentation 2013

Education

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