434 research outputs found
Theory and Practice of Data Citation
Citations are the cornerstone of knowledge propagation and the primary means
of assessing the quality of research, as well as directing investments in
science. Science is increasingly becoming "data-intensive", where large volumes
of data are collected and analyzed to discover complex patterns through
simulations and experiments, and most scientific reference works have been
replaced by online curated datasets. Yet, given a dataset, there is no
quantitative, consistent and established way of knowing how it has been used
over time, who contributed to its curation, what results have been yielded or
what value it has.
The development of a theory and practice of data citation is fundamental for
considering data as first-class research objects with the same relevance and
centrality of traditional scientific products. Many works in recent years have
discussed data citation from different viewpoints: illustrating why data
citation is needed, defining the principles and outlining recommendations for
data citation systems, and providing computational methods for addressing
specific issues of data citation.
The current panorama is many-faceted and an overall view that brings together
diverse aspects of this topic is still missing. Therefore, this paper aims to
describe the lay of the land for data citation, both from the theoretical (the
why and what) and the practical (the how) angle.Comment: 24 pages, 2 tables, pre-print accepted in Journal of the Association
for Information Science and Technology (JASIST), 201
A Progressive Visual Analytics Tool for Incremental Experimental Evaluation
This paper presents a visual tool, AVIATOR, that integrates the progressive
visual analytics paradigm in the IR evaluation process. This tool serves to
speed-up and facilitate the performance assessment of retrieval models enabling
a result analysis through visual facilities. AVIATOR goes one step beyond the
common "compute wait visualize" analytics paradigm, introducing a continuous
evaluation mechanism that minimizes human and computational resource
consumption
What-if analysis: A visual analytics approach to Information Retrieval evaluation
This paper focuses on the innovative visual analytics approach realized by the Visual Analytics Tool for Experimental Evaluation (VATE2) system, which eases and makes more effective the experimental evaluation process by introducing the what-if analysis. The what-if analysis is aimed at estimating the possible effects of a modification to an Information Retrieval (IR) system, in order to select the most promising fixes before implementing them, thus saving a considerable amount of effort. VATE2 builds on an analytical framework which models the behavior of the systems in order to make estimations, and integrates this analytical framework into a visual part which, via proper interaction and animations, receives input and provides feedback to the user. We conducted an experimental evaluation to assess the numerical performances of the analytical model and a validation of the visual analytics prototype with domain experts. Both the numerical evaluation and the user validation have shown that VATE2 is effective, innovative, and useful
The CLAIRE visual analytics system for analysing IR evaluation data
In this paper, we describe Combinatorial visuaL Analytics system for Information Retrieval Evaluation (CLAIRE), a Visual Analytics (VA) system for exploring and making sense of the performances of a large amount of Information Retrieval (IR) systems, in order to quickly and intuitively grasp which system configurations are preferred, what are the contributions of the different components and how these components interact together
A Relation Extraction Approach for Clinical Decision Support
In this paper, we investigate how semantic relations between concepts
extracted from medical documents can be employed to improve the retrieval of
medical literature. Semantic relations explicitly represent relatedness between
concepts and carry high informative power that can be leveraged to improve the
effectiveness of retrieval functionalities of clinical decision support
systems. We present preliminary results and show how relations are able to
provide a sizable increase of the precision for several topics, albeit having
no impact on others. We then discuss some future directions to minimize the
impact of negative results while maximizing the impact of good results.Comment: 4 pages, 1 figure, DTMBio-KMH 2018, in conjunction with ACM 27th
Conference on Information and Knowledge Management (CIKM), October 22-26
2018, Lingotto, Turin, Ital
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