Section: Highlights of the Year

Highlights of the Year

SheX

SheX Schemas for RDF Graphs in Cooperation with the W3C

I. Boneva and S. Staworko present the RDF schema language SheX [22] in cooperation with members of the W3C. The usual open world approach of RDF is schemaless in the alphabets of RDF data are left open, so that data from different sources and with different alphabets can be unified. This raises serious problems for query writing and thus linked data integration, since the same query may become invalid when the alphabet changes. A SheX schema allows express constraints on the alphabets, node labels and edge labels of RDF graphs, so that databases queries become safe with respect to future changes, without that the alphabets need to be closed. This work is highly relevant for the future on data integration for RDF data based on schema mappings.

IJCAI

Reasonable Highly Expressive Query Languages

In his IJCAI paper [17] P. Bourhis develops a highly expressive Web query language of the Datalog family, for which static analysis problems such as query containment remain decidable. The relevance of this result is explained to non-experts in a popularization article: http://www.cnrs.fr/ins2i/spip.php?article1465

Awards

This paper obtained the honorable mention of IJCAI .

IJCAI-highlight

Learning Join Queries from Examples

Ciucanu, A. Boneva, and S. Staworko published an article at ACM TODS [7] , where they show how to learn join queries for relational databases from examples. The learning algorithm they provide is shown to satisfy Gold's learning model. Previously this model got applied only to inference of automata rather than logical queries. Furthermore, this is the first query learning algorithm that relies on equalities of data values rather than on the structure of metadata.

Best Paper Award:

[17]