Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/eswc2016/paper/research/222> ?p ?o. }
Showing items 1 to 10 of
10
with 100 items per page.
- 222 creator ansgar-scherp.
- 222 creator johann-schaible.
- 222 creator pedro-szekely.
- 222 type InProceedings.
- 222 label "Comparing Vocabulary Term Recommendations using Association Rules and Learning To Rank: A User Study".
- 222 sameAs 222.
- 222 abstract "When modeling Linked Open Data (LOD), choosing appropriate vocabulary terms to represent data entities and relations between data entities is difficult, because there are many vocabularies to choose from. Inappropriate choices lead to LOD that is difficult both to understand for humans as well as to automatically exploit by machines. We present an evaluation of approaches that try to alleviate this situation by recommending vocabulary terms based on how other data providers have used RDF classes and properties in the LOD cloud. Our user study compares the machine learning technique Learning to Rank (L2R), the classical data mining approach Association Rule mining (AR), and a baseline that does not provide any recommendations. Results show that utilizing AR, participants needed less time and less effort to model the data, which in the end resulted to be of better quality.".
- 222 hasAuthorList authorList.
- 222 isPartOf proceedings.
- 222 title "Comparing Vocabulary Term Recommendations using Association Rules and Learning To Rank: A User Study".