Interlinking the web of data

The web of data is made of connected data sources based on web standards. It exposes data in the RDF language which allows for linking resources together [1]. This is a very convenient way to publish data on the web that can be exploited by algorithms which do not have to know the schema under which they are stored.

Interlinking resources needs effort that is actually supported by semi-automatic tools requiring a considerable amount of manual input. While these methods and tools, e.g., Silk [2], are efficient to interlink a limited number of datasets, the expected explosion of data publication on the Web of data requires the development of more automated methods.

The goal of the proposed thesis is to investigate new methods automating the data interlinking process. This should lead to the design of algorithms and a system to automatically perform data interlinking.

Several tracks may be considered to achieve this goal:

• One path, inspired from record linkage in databases, investigates statistical methods to find equivalent items in two data sources. This can be based on the computation of key properties in datasets. Such methods will have to consider the specificity of the semantic web environment: heterogeneous schemas, no necessary keys, etc.
• Another path, proposed in [3], would be to use ontology matching techniques [4] in order to first find correspondences between the ontology describing data sets (when they exist) before looking for links at the instance level. Here, it may be possible to induce from the alignment a link specification which is a synthetic description for a set of links.

The candidate will have the opportunity to experiment on large real-world datasets in the framework of the Datalift project.

Références:
[1] http://tomheath.com/papers/bizer-heath-berners-lee-ijswis-linked-data.pdf
[2] http://www4.wiwiss.fu-berlin.de/bizer/silk/
[3] http://melinda.inrialpes.fr
[4] Jérôme Euzenat, Pavel Shvaiko, Ontology matching, Springer-Verlag, Heildelberg (DE), 2007

Qualification: Master or equivalent in computer science.

Researched skills:

• Interaction with other researchers.
• Autonomous researcher.
• Taste for neat theoretical work.
• Innovative.
• Design and programming skills.
• English or French.

Environnement: The doctoral work is to be carried out in the Exmo team, a reputed team within the semantic web research area and particularly on ontology matching. We are in contact with the most important teams in France and Europe on these topics and this work could be the occasion of cooperation on sharing alignments.

Doctoral school: Doctoral school MSTII, Grenoble.

Advisor: Jérôme Euzenat (Jerome:Euzenat#inrialpes:fr) and François Scharffe (Francois:Scharffe#inria:fr).

Group: Exmo, INRIA Grenoble Rhône-Alpes & LIG

Hiring date: October 2010.

Place of work: The position is located at INRIA Grenoble Rhône-Alpes, Montbonnot (near Grenoble, France) a main computer science research lab, in a stimulating research environment. Research will be carried out in the Exmo team under the supervision of Jérôme Euzenat and François Scharffe. It will require the involvement of the candidate in the Datalift project.

Duration: 36 months

Salary: 1537 EUR/month net (including full health insurance and social benefits) upgraded to 1620 EUR/month the 3rd year.

Contact: For further information, contact Jerome:Euzenat#inrialpes:fr or Francois:Scharffe#inria:fr.

File: Provide Vitæ, motivation letter and references.