The appearance of linked data on the web calls for novel database management technologies for linked data collections. The classical challenges from database research need to be now raised for linked data: how to define exact logical queries, how to manage dynamic updates, and how to automatize the search for appropriate queries. In contrast to mainstream linked open data, the LINKS project will focus on linked data collections in various formats, under the assumption that the data is correct in most dimensions. The challenges remain difficult due to incomplete data, uninformative or heterogeneous schemas, and the remaining data errors and ambiguities. We will develop algorithms for evaluating and optimizing logical queries on linked data collections, incremental algorithms that can monitor streams of linked data and manage dynamical updates of linked data collections, and symbolic learning algorithms that can infer appropriate queries for linked data collections from examples.

Description

The Semantic Web initiative resulted in publishing a huge number of linked data collections in various formats. The evaluation of exact (logical) queries on the Semantic Web requires to be able to formally describe how the data organized according to one schema is to be represented following another, target schema. Such formal description is usually given by a schema mapping. Schema mappings have been defined for relational data with different schemas, for data in XML format, and for the nested relational data format. Such mappings are high level descriptions, from which it is possible to derive algorithms that allow for the actual exchange of data, i.e. effectively translating data from one schema to another, and for integrating data, which consists in providing mechanisms for evaluating logical queries on heterogeneous data collections.
With this thesis we are interested in schema mappings between relational and graph-shaped data, such as RDF. The first objective of the thesis is to define a high-level language for defining such schema mappings, and to propose the appropriate algorithms for exchanging and integrating data of heterogeneous formats. The second objective is to study symbolic learning techniques for inferring schema mappings from examples. The methods and algorithms defined during the thesis should be implemented as prototypes, ideally based on standard technologies such as the query languages SQL and SPARQL. This implementation would in particular allow to test how the methods scale for voluminous data collections as those that can be found on the Semantic Web.
The PhD supervisors are Pcof. Angela Bonifati, Dr Iovka Boneva and Dr Aurélien Lemay.
• Make propositions on the subject of the thesis, submit the results to international conferences
• Participate in the various activities of the research team: seminars, projects, small administrative tasks
• Present the work to scientific audience and to partners from industry.

Nr of positions available : 1

Research Fields

Computer science

Career Stage

Early stage researcher or 0-4 yrs (Post graduate) 

Research Profiles

First Stage Researcher (R1) 

Benefits

• Possibility of French courses
• Help for housing
• Financial support from Inria to catering and transportation expenses
• Scientific Resident card and help for visa
• Catering service
• Monthly salary after taxes: around 1580 € the 1st two years and 1660 € the 3rd year (social security included).
Duration: 36 months
Starting date of the contract: 1st October 2015 
Salary: 1958 € the first two years and 2059 € the third year.
Scientific contact: links-apply@lists.gforge.inria.fr
HR contact: Natacha Lecomte (natacha.lecomte@inria.fr)

Security and defense procedure: In the interests of protecting its scientific and technological assets, Inria is a restricted-access establishment. Consequently, it follows special regulations for welcoming any person who wishes to work with the institute. The final acceptance of each candidate thus depends on applying this security and defense procedure.

Comment/web site for additional job details

www.inria.fr/en/institute/recruitment/offers/phd/campaign-2015/%28view%29/details.html