Interactive User Feedback in Ontology Matching Using Signature Vectors

Cruz, Isabel F.; Stroe, Cosmin; Palmonari, Matteo

doi:10.1109/icde.2012.137

Cited by 27 publications

(35 citation statements)

References 7 publications

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“…Ontology matching approaches relying on the feedback provided by a single user are a precursor to multi-user systems. They include the work of Shi et al [21], Duan et al [9], Cruz et al [6], Noy et al [16], To et al [22], Jirkovsky et al [12] and Jiménez-Ruiz et al [11]. We describe first single-user approaches in two groups based on the type of candidate selection method they adopt, namely static vs. dynamic, and describe the multi-user approaches.…”

Section: Related Workmentioning

confidence: 99%

“…They include a visual analytics panel that supports users in the interactive matching task [6]. In comparison, we now refine both the measures adopted in the candidate selection step and the feedback propagation function.…”

Section: Single User Feedback With Static Candidate Selectionmentioning

confidence: 99%

“…We experimentally evaluated several feedback propagation methods, including a method used in our previous work [6], a method based on learning similarity scores with a multiple linear regression model, and a method based on our quality measures. For our experiments, we use this last method, which we call Quality Agreement (QA) Propagation, because it achieves the best trade-off between speed and robustness.…”

Section: Quality-based Feedback Propagationmentioning

confidence: 99%

“…Several previous approaches to ontology matching assume that feedback is given by individual users [9,21,6,11,16,22,12]. Only one of these approaches considers the possibility of user validation errors, yet it does not propose a method to overcome those errors [11].…”

Section: Introductionmentioning

confidence: 99%

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Pay-As-You-Go Multi-user Feedback Model for Ontology Matching

Cruz

Loprete

Palmonari

et al. 2014

Lecture Notes in Computer Science

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Abstract.Using a pay-as-you-go strategy, we allow for a community of users to validate or invalidate mappings obtained by an automatic ontology matching system using consensus for each mapping. The ultimate objectives are effectiveness-improving the quality of the obtained alignment (set of mappings) measured in terms of F-measure as a function of the number of user interactionsand robustness-making the system as much as possible impervious to user validation errors. Our strategy consists of two major steps: candidate mapping selection, which ranks mappings based on their perceived quality so that users are presented first with those mappings with lowest quality, and feedback propagation, which seeks to validate or invalidate those mappings that are perceived to be similar to the mappings already presented to the users for validation. The purpose of these two strategies is twofold: achieve greater improvements earlier and minimize overall user interaction. There are three important features of our approach: the use of a dynamic ranking mechanism to adapt to the new conditions after each user interaction, the presentation of each mapping for validation more than once-revalidation-because of possible user errors, and the immediate propagation of the user input on a mapping without first achieving consensus for that mapping. We study extensively the effectiveness and robustness of our approach as several of these parameters change, namely the error and revalidation rates, as a function of the number of iterations, to provide conclusive guidelines for the design and implementation of multi-user feedback ontology matching systems.

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Section: Related Workmentioning

confidence: 99%

Section: Single User Feedback With Static Candidate Selectionmentioning

confidence: 99%

Section: Quality-based Feedback Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pay-As-You-Go Multi-user Feedback Model for Ontology Matching

Cruz

Loprete

Palmonari

et al. 2014

Lecture Notes in Computer Science

Self Cite

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“…We also provide an optimal polynomial-question and tractable time learning and verification algorithms for two subclasses of the class qhorn with upper constant limits on a query's causal density. I. F. Cruz, C. Stroe, and M.J.Palmonari,have proposed, "Interactive user feedback in ontology matching using signature vectors," [5]When compared to a gold standard of the set of mappings that are generated using an automatic ontology matching process which is neither complete nor are the individual mappings always correct. Particularly as given the explosion in thesize, number and lastly the complexity of available ontologies, the domain experts no longer have the capability to create ontology mappings without considerable effort.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Interpretation Using Ambiguous Queries

Surana¹

2017

IJRASET

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-Preliminary interpretation supports exploratory search in large databases. The user interacts with it by specifying probability distributions over attributes, which then expresses imprecise conditions about the entities of interest. Preliminary interpretation helps the user on the right query conditions by addressing three key challenges: 1) Efficiently computing results for an imprecise query. 2) Gives out the result of the sensitivity value of individual and combines Queries. 3) Suggesting ideas for respective attributes for the user. Keywords: Interactive data exploration and discovery, Probability Query, Imprecise Queries, Sensitivity Analysis. I. INTRODUCTIONThe main notion of preliminary interpretation using ambiguous queries is helping the user with imprecise queries. The existing system is not able to handle the uncertainty of ambiguous queries, whereas proposed system allows user to express the uncertainty through the probabilistic value. We propose this project, a new approach for exploratory searching large databases. This project provides a novel method to interactively compose imprecise database queries with probabilistic conditions, while providing constant feedback to the user about the most likely results and the potential benefit and risk of each condition. This method is designed to accommodate uncertainty and imprecision in user-provided query conditions through two major technical contributions: A novel notion of sensitivity to quantify the impact of uncertainty on the query result. Fast algorithms for calibrated probability estimation that can adapt to a user-specified real-time constraint on system response time. To illustrate the need for imprecise queries with probabilistic conditions, consider the following example motivated by collaboration with the Cornell Lab of Ornithology. Through hugely successful citizen science projects such as the Lab has collected more than 100 million reports of bird sightings, adding tens of millions annually. It wants to leverage this resource to help less experienced birders identify the species of a bird they observed. Assume each observation in the database specifies properties of the bird (e.g., species, size, color) and the observation event (e.g., location, weather, habitat, and features).

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