Incrementally improving dataspaces based on user feedback

Belhajjame, Khalid; Paton, Norman W.; Embury, Suzanne M.; Fernandes, Alvaro A. A.; Hedeler, Cornelia

doi:10.1016/j.is.2013.01.006

Cited by 29 publications

(32 citation statements)

References 53 publications

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“…To do so, we had to specify mapping candidates that allow Fig. 3 Clustering obtained using distance overlap based on 100 feedback instances for this. Using the initial mapping candidates, it was difficult to define ground truth expectations for the users such that the above pairs of users have the same precision estimates.…”

Section: Experiments Setupmentioning

confidence: 99%

“…We have shown in previous work [3,25], that schema mappings can be selected, refined and annotated with metrics specifying their fitness based on this kind of feedback. We have also showed through empirical exercises that feedback scarcity impacts negatively on the quality of the data integration system.…”

Section: Introductionmentioning

confidence: 99%

“…For example, we have shown that using a few feedback instances, e.g. 10, commenting on tuple membership to a relation in the integration schema, the precision estimates computed for assessing the quality of the schema mappings that are candidates to populate such a relation, suffer from a high error [3].…”

Section: Introductionmentioning

confidence: 99%

“…For example, it can be used to improve the quality of schema mappings by refining them to meet the expectations of the users within a cluster [3]. In this paper, we report on the results of two applications of clustering.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enabling community-driven information integration through clustering

Belhajjame

Paton

Hedeler

et al. 2014

Distrib Parallel Databases

Self Cite

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It has become widely recognized that user feedback can play a fundamental role in facilitating information integration tasks, e.g., the construction of integration schema and the specification of schema mappings. While promising, existing proposals make the assumption that the users providing feedback expect the same results from the integration system. In practice, however, different users may anticipate different results, due, e.g., to their preferences or application of interest, in which case the feedback they provide may be conflicting, thereby deteriorating the quality of the services provided by the integration system. In this paper, we present clustering strategies for grouping information integration users into groups of users with similar expectations as to the results delivered by the integration system. As well as grouping information integration users, we show that clustering results can be used as inputs to a wide range of functionalities that are relevant in the context of crowd-driven information integration. Specifically, we show that clustering can be used to identify feedback of relevance to a given user by exploiting the feedback provided by other users in the same cluster. We report on evaluation exercises that assess the effectiveness of the clustering strategies we propose, and showcase the benefits communityand crowd-driven information integration can derive from clustering.

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Section: Experiments Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enabling community-driven information integration through clustering

Belhajjame

Paton

Hedeler

et al. 2014

Distrib Parallel Databases

Self Cite

View full text Add to dashboard Cite

show abstract

“…In an extended version of our empirical evaluation (not shown here due to space consideration) we have also shown the use of reasoning for correcting erroneous feedback. Belhajjame et al [3] suggest the use of indirect feedback to improve the quality of a set of correspondences. We seek direct expert feedback, aiming at the minimization of the interaction.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Human Effort in Reconciling Match Networks

Nguyen

Wijaya

Miklós

et al. 2013

Conceptual Modeling

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Abstract. Schema and ontology matching is a process of establishing correspondences between schema attributes and ontology concepts, for the purpose of data integration. Various commercial and academic tools have been developed to support this task. These tools provide impressive results on some datasets. However, as the matching is inherently uncertain, the developed heuristic techniques give rise to results that are not completely correct. In practice, post-matching human expert effort is needed to obtain a correct set of correspondences. We study this post-matching phase with the goal of reducing the costly human effort. We formally model this human-assisted phase and introduce a process of matching reconciliation that incrementally leads to identifying the correct correspondences. We achieve the goal of reducing the involved human effort by exploiting a network of schemas that are matched against each other. We express the fundamental matching constraints present in the network in a declarative formalism, Answer Set Programming that in turn enables to reason about necessary user input. We demonstrate empirically that our reasoning and heuristic techniques can indeed substantially reduce the necessary human involvement.

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Targeted Feedback Collection Applied to Multi-Criteria Source Selection

Ríos

Paton

Fernandes

et al. 2017

Advances in Databases and Information Systems

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Abstract. A multi-criteria source selection (MCSS) scenario identifies, from a set of candidate data sources, the subset that best meets a user's needs. These needs are expressed using several criteria, which are used to evaluate the candidate data sources. A MCSS problem can be solved using multi-dimensional optimisation techniques that trade-off the different objectives. Sometimes we may have uncertain knowledge regarding how well the candidate data sources meet the criteria. In order to overcome this uncertainty, we may rely on end users or crowds to annotate the data items produced by the sources in relation to the selection criteria. In this paper, we introduce an approach called Targeted Feedback Collection (TFC), which aims to identify those data items on which feedback should be collected, thereby providing evidence on how the sources satisfy the required criteria. TFC targets feedback by considering the confidence intervals around the estimated criteria values. The TFC strategy has been evaluated, with promising results, against other approaches to feedback collection, including active learning, using real-world data sets.

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Incrementally improving dataspaces based on user feedback

Cited by 29 publications

References 53 publications

Enabling community-driven information integration through clustering

Enabling community-driven information integration through clustering

Minimizing Human Effort in Reconciling Match Networks

Targeted Feedback Collection Applied to Multi-Criteria Source Selection

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