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- publication author me.
- publication author me.
- publication author erik_mannens.
- publication author me.
- publication creator me.
- publication creator me.
- publication creator erik_mannens.
- publication creator me.
- publication about Linked_Data.
- publication about RDF.
- publication author me.
- publication author me.
- publication author erik_mannens.
- publication author me.
- publication coparticipatesWith me.
- publication coparticipatesWith me.
- publication coparticipatesWith erik_mannens.
- publication coparticipatesWith me.
- publication type ScholarlyArticle.
- publication type Article.
- publication type Document.
- publication type Document.
- publication type Document.
- publication type Q386724.
- publication type CreativeWork.
- publication type Work.
- publication P50 me.
- publication P50 me.
- publication P50 erik_mannens.
- publication P50 me.
- publication maker me.
- publication maker me.
- publication maker erik_mannens.
- publication maker me.
- publication title "Data Analysis of Hierarchical Data for RDF Term Identification".
- publication isPartOf proceedings_of_the_joint_international_semantic_technology_conference.
- publication name "Data Analysis of Hierarchical Data for RDF Term Identification".
- publication label "Data Analysis of Hierarchical Data for RDF Term Identification".
- publication name "Data Analysis of Hierarchical Data for RDF Term Identification".
- publication topic Linked_Data.
- publication topic RDF.
- publication subject Linked_Data.
- publication subject RDF.
- publication authorList b0_b2392.
- publication topic Linked_Data.
- publication topic RDF.
- publication abstract "Generating Linked Data based on existing data sources requires the modeling of their information structure. This modeling needs the identification of potential entities, their attributes and the relationships between them and among entities. For databases this identification is not required, because a data schema is always available. However, for other data formats, such as hierarchical data, this is not always the case. Therefore, analysis of the data is required to support RDF term and data type identification. We introduce a tool that performs such an analysis on hierarchical data. It implements the algorithms, Daro and S-Daro, proposed in this paper. Based on our evaluation, we conclude that S-Daro offers a more scalable solution regarding run time, with respect to the dataset size, and provides more complete results.".
- publication datePublished "2016".
- publication mainEntityOfPage heyvaert_jist_2016.
- publication sameAs publication.
- publication isPrimaryTopicOf heyvaert_jist_2016.
- publication page heyvaert_jist_2016.