A Feature Selection Approach for Anchor Evaluation in Ontology Mapping

Schadd, Frederik C.; Roos, Nico

doi:10.1007/978-3-319-11716-4_14

Cited by 3 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the many possible SI measures that could be used, we adopt Thornton's SI [52], a competitive measure for assessing class separation originally introduced for supervised classification problems [4], [10], [23]. Also called the geometric separability index (GSI), Thornton's measure has seen wide application in health, robotics, geology and other fields [42], [14], [49], [54], [47]. We use the following simple unsupervised modification of the GSI [52], computed on the merged training and test samples for the batch: We performed a simulation of HMM-FLDA that emulates the experimental study presented in Section IV, but with ground truth event states.…”

Section: B Post-baseline Adaptation: Sequential Transfer Learningmentioning

confidence: 99%

Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes

She¹,

Zhai²,

Henao³

et al. 2021

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution. Methods: We introduce an adaptive transfer learning algorithm to classify and segment events from non-stationary multi-channel temporal data. Using a multivariate hidden Markov model (HMM) and Fisher's linear discriminant analysis (FLDA) the algorithm adaptively adjusts to shifts in distribution over time. The proposed algorithm is unsupervised and learns to label events without requiring a priori information about true event states. The procedure is illustrated on experimental data collected from a cohort in a human viral challenge (HVC) study, where certain subjects have disrupted wake and sleep patterns after exposure to an H1N1 influenza pathogen. Results: Simulations establish that the proposed adaptive algorithm significantly outperforms other event classification methods. When applied to early time points in the HVC data, the algorithm extracts sleep/wake features that are predictive of both infection and infection onset time. Conclusion:The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring. Significance: Our integrated multisensor signal processing and transfer learning method is applicable to many ambulatory monitoring applications. 1 Index Terms-Covariate shift, domain adaptation, wearable sensors, human viral challenge study, digital health, early detection of viral infection.

show abstract

Section: B Post-baseline Adaptation: Sequential Transfer Learningmentioning

confidence: 99%

Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes

She¹,

Zhai²,

Henao³

et al. 2021

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

Section: A Baseline Training: Hmm Initial Segmentationmentioning

confidence: 99%

Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes

She,

Zhai,

Henao

et al. 2020

Preprint

View full text Add to dashboard Cite

To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution. Methods: We introduce an adaptive transfer learning algorithm to classify and segment events from non-stationary multi-channel temporal data. Using a multivariate hidden Markov model (HMM) and Fisher's linear discriminant analysis (FLDA) the algorithm adaptively adjusts to shifts in distribution over time. The proposed algorithm is unsupervised and learns to label events without requiring a priori information about true event states. The procedure is illustrated on experimental data collected from a cohort in a human viral challenge (HVC) study, where certain subjects have disrupted wake and sleep patterns after exposure to a H1N1 influenza pathogen. Results: Simulations establish that the proposed adaptive algorithm significantly outperforms other event classification methods. When applied to early time points in the HVC data the algorithm extracts sleep/wake features that are predictive of both infection and infection onset time. Conclusion:The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring. Significance: Our integrated multisensor signal processing and transfer learning method is applicable to many ambulatory monitoring applications. 1 Index Terms-Covariate shift, domain adaptation, wearable sensors, human viral challenge study, digital health, early detection of viral infection.

show abstract

Background knowledge in ontology matching: A survey

Portisch

Hladik

Paulheim

2022

View full text Add to dashboard Cite

Ontology matching is an integral part for establishing semantic interoperability. One of the main challenges within the ontology matching operation is semantic heterogeneity, i.e. modeling differences between the two ontologies that are to be integrated. The semantics within most ontologies or schemas are, however, typically incomplete because they are designed within a certain context which is not explicitly modeled. Therefore, external background knowledge plays a major role in the task of (semi-) automated ontology and schema matching. In this survey, we introduce the reader to the general ontology matching problem. We review the background knowledge sources as well as the approaches applied to make use of external knowledge. Our survey covers all ontology matching systems that have been presented within the years 2004–2021 at a well-known ontology matching competition together with systematically selected publications in the research field. We present a classification system for external background knowledge, concept linking strategies, as well as for background knowledge exploitation approaches. We provide extensive examples and classify all ontology matching systems under review in a resource/strategy matrix obtained by coalescing the two classification systems. Lastly, we outline interesting and yet underexplored research directions of applying external knowledge within the ontology matching process.

show abstract

A Feature Selection Approach for Anchor Evaluation in Ontology Mapping

Cited by 3 publications

References 18 publications

Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes

Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes

Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes

Background knowledge in ontology matching: A survey

Contact Info

Product

Resources

About