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Full-fledged semantic indexing and querying model designed for seamless integration in legacy RDBMS
Published 2018“…We then provide a general keyword query model with specially tailored query processing algorithms built on top of SemIndex, in order to produce semantic-aware results, allowing the user to choose the results' semantic coverage and expressiveness based on her needs. …”
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Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying
Published 2023“…Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. …”
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A Parallel Neural Networks Algorithm for the Clique Partitioning Problem
Published 2002“…Given a graph G = (V, E), a clique is a complete subgraph in G. …”
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Brain Source Localization in the Presence of Leadfield Perturbations
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Assessment of static pile design methods and non-linear analysis of pile driving
Published 2006“…The pile/soil interaction system is described by a mass/spring/dashpot system where the properties of each component are derived from rigorous analytical solutions or finite element analysis. The outcome of this research is an algorithm that can be used to predict pile displacement and driving stresses. …”
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SemIndex+: A semantic indexing scheme for structured, unstructured, and partly structured data
Published 2018“…We provide a general keyword query model allowing the user to choose the results’ semantic coverage and expressiveness based on her needs. …”
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CFD Based Airfoil Shape Optimization for Aerodynamic Drag Reduction
Published 2012Get full text
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A hybrid approach for XML similarity
Published 2007“…Owing to an unparalleled increasing use of the XML standard, developing efficient techniques for comparing XML-based documents becomes essential in information retrieval (IR) research. Various algorithms for comparing hierarchically structured data, e.g. …”
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SemIndex: Semantic-Aware Inverted Index
Published 2017“…We also provide an extended query model and related processing algorithms with the help of SemIndex. …”
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Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study
Published 2024“…Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.…”
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Approximate XML structure validation based on document–grammar tree similarity
Published 2015“…In this paper, we propose an original method for measuring the structural similarity between an XML document and an XML grammar (DTD or XSD), considering their most common operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes (e.g., ?, ∗, MinOccurs, MaxOccurs, etc.). …”
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Approximate XML structure validation technical report
Published 2014“…In this paper, we propose an original method for measuring the structural similarity between an XML document and an XML grammar (DTD or XSD), considering their most common operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes (e.g., ?, *, MinOccurs, MaxOccurs, etc.). …”
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Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis
Published 2023“…Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. …”
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Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis
Published 2023“…<p>Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. …”