1,582 research outputs found

    Avoiding Alignment-based Conservativity Violations through Dialogue

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    A number of ontology matching techniques have been proposed that rely on full disclosure of their ontological models prior to the construction of the alignment. However, within open and opportunistic environments, such approaches may not always be pragmatic or even acceptable (due to privacy concerns). Several studies have focussed on collaborative, decentralised approaches to ontology alignment, where agents negotiate the acceptability of correspondences (i.e. mappings between corresponding entities in different ontologies) acquired from past encounters, or try to ascertain novel correspondences on the fly. However, such approaches can lead to logical flaws that may undermine their utility. In this paper, we extend a dialogical approach to correspondence negotiation, whereby agents not only exchange details of possible correspondences, but also identify potential violations to the so-called conservativity principle, where novel but undesirable entailments between named concepts in one of the input ontologies emerge. We present a formal model of the dialogue, and show how \conservativity violations can be repaired (using an existing correspondence repair system) during the dialogue through the exchange of repairs. We then illustrate how agents negotiate over possible correspondences and repairs by means of a walkthrough example

    Empirical study of Gene Ontology based Microarray clustering.

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    Nonparametric Bayesian Modeling for Automated Database Schema Matching

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    The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models

    An Intelligent Online Shopping Guide Based On Product Review Mining

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    This position paper describes an on-going work on a novel recommendation framework for assisting online shoppers in choosing the most desired products, in accordance with requirements input in natural language. Existing feature-based Shopping Guidance Systems fail when the customer lacks domain expertise. This framework enables the customer to use natural language in the query text to retrieve preferred products interactively. In addition, it is intelligent enough to allow a customer to use objective and subjective terms when querying, or even the purpose of purchase, to screen out the expected products

    Ontology matching: state of the art and future challenges

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    shvaiko2013aInternational audienceAfter years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue some further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field

    Data-Driven Shape Analysis and Processing

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    Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

    State of the Art Automatic Ontology Matching

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