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

Outline Motivation The CLDP-SEE Platform Conclusion and Future Work

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.

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.

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.

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

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.

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

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.

The CLDP-SEE Platform The Architecture of CLDP-SEE

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

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

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.

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.

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.

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

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.

The CLDP-SEE Platform Data Storage Layer SEE Ontology RDF Datasets 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

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.

Thank You!

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.

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

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/)

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.