Scientific data are being generated at an ever-increasing rate. The Biomedical and Healthcare Data Discovery Index Ecosystem (bioCADDIE) is an NIH-funded Data Discovery Index that aims to provide a platform for researchers to locate, retrieve, and share research datasets. The bioCADDIE 2016 Dataset Retrieval Challenge was held to identify the most effective dataset retrieval methods. We aimed to assess the value of Medical Subject Heading (MeSH) term-based query expansion to improve retrieval. Our system, based on the open-source search engine, Elasticsearch, expands queries by identifying synonyms from the MeSH vocabulary and adding these to the original query. The number and relative weighting of MeSH terms is variable. The top 1000 search results for the 15 challenge queries were submitted for evaluation. After the challenge, we performed additional runs to determine the optimal number of MeSH terms and weighting. Our best overall score used five MeSH terms with a 1:5 terms:words weighting ratio, achieving an inferred normalized distributed cumulative gain (infNDCG) of 0.445, which was the third highest score among the 10 research groups who participated in the challenge. Further testing revealed our initial combination of MeSH terms and weighting yielded the best overall performance. Scores varied considerably between queries as well as with different variations of MeSH terms and weights. Query expansion using MeSH terms can enhance search relevance of biomedical datasets. High variability between queries and system variables suggest room for improvement and directions for further research.
Their best overall score used five MeSH terms with a 1:5 terms: words weighting ratio REF .
Query expansion using MeSH terms for dataset retrieval: OHSU at the bioCADDIE 2016 dataset retrieval challenge
Database: The Journal of Biological Databases and Curation
Database: The Journal of Biological Databases and Curation
Computer Science Medicine