Cloud based linked data platform for Structural Engineering Experiment

1. Cloud based linked data platform for Structural Engineering Experiment Xiaohui Zhang xh-zh@msn.cn

2. Outline • Motivation • The CLDP-SEE Platform • Conclusion and Future Work

3. Motivation • Structural Engineering • A discipline analyzing the force and deformation of buildings by mechanical methods. • Experiment is one of the main means for domain research. • Large amounts of experimental data is accumulated, but be maintained by each experimental user dispersedly. • Due to the complexity and heterogeneity of the experimental data, the sharing and integrating with the traditional methods is difficult.

4. Motivation • Linked Data • Linked Data is simply about using the Web to publish structured data and create typed links between data from different sources. • Based on semantic web, linked data uses RDF to make typed statements that link arbitrary things in the world. • Linked data provides a wonderful approach to publish and consume data on the web and make the web be a global data space which can be understood both by computer and human.

5. Motivation • Linked Data for Structure Engineering • The data represented based on semantic can be understood by machines, which is helpful for the integration and processing of experimental data. • The interlinking among data from different sources is a effective measure for the heterogeneity. • Linked data will make it easy for the sharing and intelligent processing of experimental data.

6. Motivation • A huge challenge for domain researchers to deploy and use Linked Data related tools to make operations on the data: • Conversion of data format • Publication of experiment data • Integration of experiment data • Consuming of linked experiment data

7. Motivation • A centralized platform providing all the functions needed by experiencing linked data in services is necessary for domain researchers. • A linked data platform based on cloud for Structural Engineering Experiment (CLDP-SEE) is proposed by this paper. • The publishing, interlinking and consuming of experiment data is an intact ecosystem of data sharing. CLDP-SEE can • lower the threshold of sharing data with linked data technology for domain users; • promote the growth of the linked data ecosystem and the development of Structural Engineering discipline.

8. The CLDP-SEE Platform The application scenario of CLDP-SEE

9. The CLDP-SEE Platform • The operations in application scenario： • Uploading and managing the RDF data, setting access control policies of each datasets. • Uploading raw data in traditional formats, such as CSV, Excel, Relational Database. And then converting these raw data into RDF. • Querying datasets from the shared data space, private data space according to the authority and even the datasets from the Web, and then interlinking data among these datasets to generate a Virtual Data Space. • Reasoning and querying the data in Virtual Data Space. • Publishing data with Linked Data Server.

10. The CLDP-SEE Platform • The Architecture of CLDP-SEE

11. The CLDP-SEE Platform • Portal Layer • Provides graphical web interface for users to experience almost all the functions providing by CLDP-SEE.

12. The CLDP-SEE Platform • Core Service Layer • Data Manage Service: is mainly used to help users to manage their data. • Data Upload • Data Format Transform • Dataset Registry • Dataset Manage • Data Publish • Authority Manage

13. The CLDP-SEE Platform • Core Service Layer • Data Link Service • Provides the capabilities of data integration; • Coreference Interlink is responsible for getting the request of users, and finding the coreference relations between data from different datasets. • The coreference relation of RDF data refers to two different URI pointing to the same entity. • Two methods of coreference interlinking: • Similarity computation: implemented according to SILk(Isele, R.; Jentzsch, A. & Bizer, C. 2010) • Rules matching: Link Rule Manage service provides graphical interface for the experts and users to define rules. • Links Update will update the links with the information collected by Dataset Monitor service.

14. The CLDP-SEE Platform • Core Service Layer • Data Reason Service • The rule-based inference is mainly done by this service. • Users can select any datasets from Virtual Data Space, Private Space or Shared Data Space according to the authority. • Inference Rule Manage supports each user to define and manage their private inference rules, and check the consistency with default rules provided by domain experts. • Default rules and user-defined rules can be applied in the inference.

15. The CLDP-SEE Platform • Core Service Layer • Data Query Service • The basis of consuming linked experiment data. • Two kinds of query interfaces: • navigation query based on SEE ontology • query based on keywords • Support users self-defining the scope of query. • Query Engine is responsible for processing the request from self-service portal, and executes SPARQL query on the datasets selected by users.

16. The CLDP-SEE Platform • Supporting Service Layer • The services in this layer are mainly supporting the functions of the services in Core Service Layer. • Data service mainly provides the underlying functions of RDF data management and access. • Ontology Manage service, Dataset Access service ，Dataset Storage service,Dataset Monitor. • Publish Service mainly supports the Data Publish in Data Manage Service. • Linked Data Server • RDF File Server

17. The CLDP-SEE Platform • Supporting Service Layer • User Service: • Metadata Manage service: manages the information of users and make user can update personal materials. • Role Manage service: be provided for platform administrator to manage the roles of users. • Social Network Manage service: manages the friend relationships among users, and provides personal space for each user.

18. The CLDP-SEE Platform • Data Storage Layer • SEE Ontology • stores the unified ontology schema and the data in Shared Data Space. • RDF Datasets • stores the datasets in users’ Private Data Space, and ensure the isolation between users. • Links of Data • stores the relation between the entities from different datasets. • Rule Base • default rule bases • user defined rules

19. Conclusion and Future Work • CLDP-SEE provides almost all the services needed by Structural Engineering domain users to manage and share experiment data based on linked data technology. • Future work: • Improving the performance of data linking and inference. • More flexible access control policy and fine-grained access control model.

20. Thank You!

21. Related Works • Publication of Linked Data • D2R Server (Prud’hommeaux & Seaborne. 2006) ：publishing the content of relational databases as RDF. • Pubby and Elda: providing Linked Data interfaces for RDF data sources.

22. Related Works • Searching and Browsing of Linked Data • linked data browser: enables people to view data from one dataset to another by following RDF links. • Tabulator (Berners-Lee et al., 2006) • OpenLink Browser (http://oat.openlinksw.com/rdfbrowser2/) • Marbles (http://marbles.sourceforge.net/) • linked data engine: provides service for people querying the Web of Data. • Falcons, Sindice, Swoogle and SWSE

23. Related Works • Interlinking of Linked Data • SILK (Robert et al., 2010) • DSNotify(Haslhofer & Popitsch, 2009) • LinkedDataBR (Kelli et al., 2011): a platform used by Brazil for linking open Brazilian governmental data. • Talis: a platform for RDF data sharing via weaving data with the Web to create a highly available and adaptable environment. (http://www.talis.com/platform/)

24. Related Works • CLDP-SEE provides services for the storage, query, publishing and management of RDF data. • CLDP-SEE provides more perfect services with cloud characteristics: • More flexible and personalized self-service model; • Query the datasets according to subject, and ineterlink the data in the result datasets; • Elastical reasoning service on the user-defined datasets; • A shared RDF repository with rich interlinks among data.