Enabling FAIR Research in Earth Science through Research Objects
Table 3: Similarity Evaluation for one document
['[BOLD] Similarity evaluated on same category  [BOLD] Similarty based on', '[BOLD] Similarity evaluated on same category  [BOLD] p@1', '[BOLD] Similarity evaluated on same category  [BOLD] p@5', '[BOLD] Similarity evaluated on same category  [BOLD] p@10', '[BOLD] Similarity evaluated on same category  [BOLD] p@15', '[BOLD] Similarity evaluated on same category  [BOLD] p@20']
The best approach in both versions of the experiment was the combination of main concepts (top 10) generated by the semantic enrichment and textual content of the research object (concepts and text), followed by the combination of all the semantic metadata except named entities and textual content (semantic metadata no NE and text). In general, the combination of semantic metadata plus text seems to produce better results than semantic metadata alone. One interesting observation is that using only semantic metadata the precision values, albeit smaller, are close to other approaches using it in combination with text content. This supports our claim that automatically generated semantic metadata can alleviate the lack of user-generated metadata like research object title or description. Finally, although precision can still be improved, the similarity values evaluated on neighbor categories are promising.