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Creating and Exploring Semantic Annotation for Behaviour Analysis
Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation exten...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163329/ https://www.ncbi.nlm.nih.gov/pubmed/30142956 http://dx.doi.org/10.3390/s18092778 |
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author | Yordanova, Kristina Krüger, Frank |
author_facet | Yordanova, Kristina Krüger, Frank |
author_sort | Yordanova, Kristina |
collection | PubMed |
description | Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label, enabling further reasoning. In this paper, we present a novel approach to semantic annotation by means of plan operators. We provide a step by step description of the workflow to manually creating the ground truth annotation. To validate our approach, we create semantic annotation of the Carnegie Mellon University (CMU) grand challenge dataset, which is often cited, but, due to missing and incomplete annotation, almost never used. We show that it is possible to derive hidden properties, behavioural routines, and changes in initial and goal conditions in the annotated dataset. We evaluate the quality of the annotation by calculating the interrater reliability between two annotators who labelled the dataset. The results show very good overlapping (Cohen’s [Formula: see text] of 0.8) between the annotators. The produced annotation and the semantic models are publicly available, in order to enable further usage of the CMU grand challenge dataset. |
format | Online Article Text |
id | pubmed-6163329 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61633292018-10-10 Creating and Exploring Semantic Annotation for Behaviour Analysis Yordanova, Kristina Krüger, Frank Sensors (Basel) Article Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label, enabling further reasoning. In this paper, we present a novel approach to semantic annotation by means of plan operators. We provide a step by step description of the workflow to manually creating the ground truth annotation. To validate our approach, we create semantic annotation of the Carnegie Mellon University (CMU) grand challenge dataset, which is often cited, but, due to missing and incomplete annotation, almost never used. We show that it is possible to derive hidden properties, behavioural routines, and changes in initial and goal conditions in the annotated dataset. We evaluate the quality of the annotation by calculating the interrater reliability between two annotators who labelled the dataset. The results show very good overlapping (Cohen’s [Formula: see text] of 0.8) between the annotators. The produced annotation and the semantic models are publicly available, in order to enable further usage of the CMU grand challenge dataset. MDPI 2018-08-23 /pmc/articles/PMC6163329/ /pubmed/30142956 http://dx.doi.org/10.3390/s18092778 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yordanova, Kristina Krüger, Frank Creating and Exploring Semantic Annotation for Behaviour Analysis |
title | Creating and Exploring Semantic Annotation for Behaviour Analysis |
title_full | Creating and Exploring Semantic Annotation for Behaviour Analysis |
title_fullStr | Creating and Exploring Semantic Annotation for Behaviour Analysis |
title_full_unstemmed | Creating and Exploring Semantic Annotation for Behaviour Analysis |
title_short | Creating and Exploring Semantic Annotation for Behaviour Analysis |
title_sort | creating and exploring semantic annotation for behaviour analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163329/ https://www.ncbi.nlm.nih.gov/pubmed/30142956 http://dx.doi.org/10.3390/s18092778 |
work_keys_str_mv | AT yordanovakristina creatingandexploringsemanticannotationforbehaviouranalysis AT krugerfrank creatingandexploringsemanticannotationforbehaviouranalysis |