Browsing by Keyword "query rewriting"
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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 Incremental SPARQL query processing(Springer Verlag, 2013) Torre-Bastida, Ana I.; HPAThe number of linked data sources available on the Web is growing at a rapid rate. Moreover, users are showing an interest for any framework that allows them to obtain answers, for a formulated query, accessing heterogeneous data sources without the need of explicitly specifying the sources to answer the query. Our proposal focus on that interest and its goal is to build a system capable of answering to user queries in an incremental way. Each time a different data source is accessed the previous answer is eventually enriched. Brokering across the data sources is enabled by using source mapping relationships. User queries are rewritten using those mappings in order to obtain translations of the original query across data sources. Semantically equivalent translations are first looked for, but semantically approximated ones are generated if equivalence is not achieved. Well defined metrics are considered to estimate the information loss, if any.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.