麻豆传媒AV

Citation

Schaible J, Gottron T & Scherp A (2016) TermPicker: Enabling the reuse of vocabulary terms by exploiting data from the linked open data cloud. In: Sack H, Blomqvist E, d'Aquin M, Ghidini C & Paolo Ponzetto S (eds.) The Semantic Web. Latest Advances and New Domains. ESWC 2016. Lecture Notes in Computer Science, 9678. European Semantic Web Conference 2016, Heraklion, Greece, 29.05.2016-02.06.2016. Cham, Switzerland: Springer Verlag, pp. 101-117. https://doi.org/10.1007/978-3-319-34129-3_7

Abstract
Deciding which RDF vocabulary terms to use when modeling data as Linked Open Data (LOD) is far from trivial. In this paper, we propose TermPicker as a novel approach enabling vocabulary reuse by recommending vocabulary terms based on various features of a term. These features include the term’s popularity, whether it is from an already used vocabulary, and the so-called schema-level pattern (SLP) feature that exploits which terms other data providers on the LOD cloud use to describe their data. We apply Learning To Rank to establish a ranking model for vocabulary terms based on the utilized features. The results show that using the SLP-feature improves the recommendation quality by 29–36 % considering the Mean Average Precision and the Mean Reciprocal Rank at the first five positions compared to recommendations based on solely the term’s popularity and whether it is from an already used vocabulary.

Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)