14,839 research outputs found
Improving average ranking precision in user searches for biomedical research datasets
Availability of research datasets is keystone for health and life science
study reproducibility and scientific progress. Due to the heterogeneity and
complexity of these data, a main challenge to be overcome by research data
management systems is to provide users with the best answers for their search
queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we
investigate a novel ranking pipeline to improve the search of datasets used in
biomedical experiments. Our system comprises a query expansion model based on
word embeddings, a similarity measure algorithm that takes into consideration
the relevance of the query terms, and a dataset categorisation method that
boosts the rank of datasets matching query constraints. The system was
evaluated using a corpus with 800k datasets and 21 annotated user queries. Our
system provides competitive results when compared to the other challenge
participants. In the official run, it achieved the highest infAP among the
participants, being +22.3% higher than the median infAP of the participant's
best submissions. Overall, it is ranked at top 2 if an aggregated metric using
the best official measures per participant is considered. The query expansion
method showed positive impact on the system's performance increasing our
baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively.
Our similarity measure algorithm seems to be robust, in particular compared to
Divergence From Randomness framework, having smaller performance variations
under different training conditions. Finally, the result categorization did not
have significant impact on the system's performance. We believe that our
solution could be used to enhance biomedical dataset management systems. In
particular, the use of data driven query expansion methods could be an
alternative to the complexity of biomedical terminologies
Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness
We propose and study a novel supervised approach to learning statistical
semantic relatedness models from subjectively annotated training examples. The
proposed semantic model consists of parameterized co-occurrence statistics
associated with textual units of a large background knowledge corpus. We
present an efficient algorithm for learning such semantic models from a
training sample of relatedness preferences. Our method is corpus independent
and can essentially rely on any sufficiently large (unstructured) collection of
coherent texts. Moreover, the approach facilitates the fitting of semantic
models for specific users or groups of users. We present the results of
extensive range of experiments from small to large scale, indicating that the
proposed method is effective and competitive with the state-of-the-art.Comment: 37 pages, 8 figures A short version of this paper was already
published at ECML/PKDD 201
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