RT Journal Article
T1 Estimating query rewriting quality over LOD
A1 Anai, Torre Bastida
A1 Bermadez, Jesas
A1 Illarramendi, Arantza
AB Nowadays it is becoming increasingly necessary to query data stored in different datasets of public access, such as those included in the Linked Data environment, in order to get as much information as possible on distinct topics. However, users have difficulty to query those datasets with different vocabularies and data structures. For this reason it is interesting to develop systems that can produce on demand rewritings of queries. Moreover, a semantics preserving rewriting cannot often be guaranteed by those systems due to heterogeneity of the vocabularies. It is at this point where the quality estimation of the produced rewriting becomes crucial. In this paper we present a novel framework that, given a query written in the vocabulary the user is more familiar with, the system rewrites the query in terms of the vocabulary of a target dataset. Moreover, it informs about the quality of the rewritten query with two scores: a similarity factor which is based on the rewriting process itself, and a quality score offered by a predictive model. This Machine Learning based model learns from a set of queries and their intended (gold standard) rewritings. The feasibility of the framework has been validated in a real scenario.
SN 1570-0844
YR 2019
FD 2019
LK https://hdl.handle.net/11556/3871
UL https://hdl.handle.net/11556/3871
LA eng
NO Anai , T B , Bermadez , J & Illarramendi , A 2019 , ' Estimating query rewriting quality over LOD ' , Semantic Web , vol. 10 , no. 3 , pp. 529-554 . https://doi.org/10.3233/SW-180311
NO Publisher Copyright: © 2019-IOS Press and the authors. All rights reserved.
DS TECNALIA Publications
RD 1 sept 2024