Relational keyword search (R-KwS) systems based on schema graphs take the keywords from the input query, find the tuples and tables where these keywords occur and look for ways to "connect" these keywords using information on referential integrity constraints, i.e., key/foreign key pairs. The result is a number of expressions, called Candidate Networks (CNs), which join relations where keywords occur in a meaningful way. These CNs are then evaluated, resulting in a number of join networks of tuples (JNTs) that are presented to the user as ranked answers to the query. As the number of CNs is potentially very high, handling them is very demanding, both in terms of time and resources, so that, for certain queries, current systems may take too long to produce answers, and for others they may even fail to return results (e.g., by exhausting memory). Moreover, the quality of the CN evaluation may be compromised when a large number of CNs is processed. Based on observations made by other researchers and in our own findings on representative workloads, we argue that, although the number of possible Candidate Networks can be very high, only very few of them produce answers relevant to the user and are indeed worth processing. Thus, R-KwS systems can greatly benefit from methods for accessing the relevance of Candidate Networks, so that only those deemed relevant might be evaluated. We propose in this paper an approach for ranking CNs, based on their probability of producing relevant answers to the user. This relevance is estimated based on the current state of the underlying database using a probabilistic Bayesian model we have developed. Experiments that we performed indicate that this model is able to assign the relevant CNs among the top-4 in the ranking produced.In these experiments we also observed that processing only a few relevant CNs has a considerable positive impact, not only on the performance of processing keyword queries, but also on the quality of the results obtained.978-1-4799-7964-6/15/$31.00

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Identifying record replicas in Digital Libraries and other types of digital repositories is fundamental to improve the quality of their content and services as well as to yield eventual sharing efforts. Several deduplication strategies are available, but most of them rely on manually chosen settings to combine evidence used to identify records as being replicas. In this paper, we present the results of experiments we have carried out with a novel Machine Learning approach we have proposed for the deduplication problem. This approach, based on Genetic Programming (GP), is able to automatically generate similarity functions to identify record replicas in a given repository. The generated similarity functions properly combine and weight the best evidence available among the record fields in order to tell when two distinct records represent the same real-world entity. The results of the experiments show that our approach outperforms the baseline method by Fellegi and Sunter by more than 12% when identifying replicas in a data set containing researcher's personal data, and by more than 7%, in a data set with article citation data.

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