Journal article Open Access

# Semi-Supervised Online Structure Learning for Composite Event Recognition

Evangelos Michelioudakis; Alexander Artikis; Georgios Paliouras

### DataCite XML Export

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<identifier identifierType="DOI">10.5281/zenodo.2541610</identifier>
<creators>
<creator>
<creatorName>Evangelos Michelioudakis</creatorName>
<affiliation>NCSR Demokritos</affiliation>
</creator>
<creator>
<creatorName>Alexander Artikis</creatorName>
<affiliation>NCSR Demokritos</affiliation>
</creator>
<creator>
<creatorName>Georgios Paliouras</creatorName>
<affiliation>NCSR Demokritos</affiliation>
</creator>
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<titles>
<title>Semi-Supervised Online Structure Learning for Composite Event Recognition</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2019</publicationYear>
<dates>
<date dateType="Issued">2019-01-16</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="Text">Journal article</resourceType>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2541610</alternateIdentifier>
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<relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2541609</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020_datacron</relatedIdentifier>
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<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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<descriptions>
<description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/687591/">687591</awardNumber>
<awardTitle>Big Data Analytics for Time Critical Mobility Forecasting</awardTitle>
</fundingReference>
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