Browsing by Author "Illarramendi, Arantza"
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Item Estimating query rewriting quality over LOD(2019) Anai, Torre Bastida; Bermadez, Jesas; Illarramendi, Arantza; HPANowadays 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.Item Query approximation in the case of incompletely aligned datasets(2015) Torre, Ana I.; Bermudez, Jesus; Illarramendi, Arantza; HPAClouds of Linked Open Data about the same domain promote the formulation of a query over a source dataset and then try to process the same query over different target datasets, one after another, in order to obtain a broader set of answers. However, heterogeneity of vocabularies used in the datasets and the scarce number of alignments among those datasets makes that querying task extremely difficult. This paper presents a proposal that allows on demand transformations of queries by using a set of transformation rules that are able to rewrite a query formulated over a source dataset into another query adequate for a target dataset, which approximates the original one. The approach relieves users from knowing the vocabulary used in the targeted datasets and even more it considers situations where alignments do not exist or they are not suitable for the formulated query. Therefore, in order to favor the possibility of getting answers, sometimes there is no guarantee of obtaining a semantically equivalent translation. Experiments with benchmark queries validate the feasibility of the proposal.Item Query rewriting for an incremental search in heterogeneous linked data sources(2013) Torre-Bastida, Ana I.; Bermúdez, Jesús; Illarramendi, Arantza; Mena, Eduardo; González, Marta; HPA; SGNowadays, the number of linked data sources available on the Web is considerable. In this scenario, users are interested in frameworks that help them to query those heterogeneous data sources in a friendly way, so avoiding awareness of the technical details related to the heterogeneity and variety of data sources. With this aim, we present a system that implements an innovative query approach that obtains results to user queries in an incremental way. It sequentially accesses different datasets, expressed with possibly different vocabularies. Our approach enriches previous answers each time a different dataset is accessed. Mapping axioms between datasets are used for rewriting the original query and so obtaining new queries expressed with terms in the vocabularies of the target dataset. These rewritten queries may be semantically equivalent or they could result in a certain semantic loss; in this case, an estimation of the loss of information incurred is presented.Item A rule-based transducer forquerying incompletely aligned datasets(2018-09) Torre-Bastida, Ana I.; Bermúdez, Jesús; Illarramendi, Arantza; HPAA growing number of Linked Open Data sources (from diverse provenances and about different domains) that can be freely browsed and searched to find and extract useful information have been made available. However, access to them is difficult for different reasons. This study addresses access issues concerning heterogeneity. It is common for datasets to describe the same or overlapping domains while using different vocabularies. Our study presents a transducer that transforms a SPARQL query suitably expressed in terms of the vocabularies used in a source dataset into another SPARQL query suitably expressed for a target dataset involving different vocabularies. The transformation is based on existing alignments between terms in different datasets. Whenever the transducer is unable to produce a semantically equivalent query because of the scarcity of term alignments, the transducer produces a semantic approximation of the query to avoid returning the empty answer to the user. Transformation across datasets is achieved through the management of a wide range of transformation rules. The feasibility of our proposal has been validated with a prototype implementation that processes queries that appear in well-known benchmarks and SPARQL endpoint logs. Results of the experiments show that the system is quite effective in achieving adequate transformations.