Journal article Open Access

Semi-Supervised Online Structure Learning for Composite Event Recognition

Evangelos Michelioudakis; Alexander Artikis; Georgios Paliouras


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph-cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph-cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "NCSR Demokritos", 
      "@type": "Person", 
      "name": "Evangelos Michelioudakis"
    }, 
    {
      "affiliation": "NCSR Demokritos", 
      "@type": "Person", 
      "name": "Alexander Artikis"
    }, 
    {
      "affiliation": "NCSR Demokritos", 
      "@type": "Person", 
      "name": "Georgios Paliouras"
    }
  ], 
  "headline": "Semi-Supervised Online Structure Learning for Composite Event Recognition", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2019-01-16", 
  "url": "https://zenodo.org/record/2541610", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.2541610", 
  "@id": "https://doi.org/10.5281/zenodo.2541610", 
  "@type": "ScholarlyArticle", 
  "name": "Semi-Supervised Online Structure Learning for Composite Event Recognition"
}
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