EPIQUE is the first project where science evolution will be studied at such a large scale (over the entire datasets like the WoS or MedLine). From the viewpoint of philosophy of science, it allows testing theories on science evolution and nature which have been formulated only by considering a few canonical texts (the “great scientists of the pasts”, which introduces numerous biases) on a corpus that can reliably be seen as a plausible testimony of scientific activity. Preliminary results on a small part of the corpus already demonstrate that phylomemetic lattices reveal novel semantic insights about science evolution [CC13]. We are confident that taking into account the whole corpus will not only apply to other scientific fields but it will also more fundamentally reveal deeper understanding of inter-disciplinary evolution. Facing an ever growing corpus, we do not consider scientometric workflows as sequences of independent tasks on a given dataset, but we strive for a more integrated framework which allows end users to interact and control the whole process through high level languages and interfaces (e.g. for specifying the scientific field and time-range of interest, or any criteria about the corpus such as the country of the authors). The architecture of the project strongly relies on feedback loops between production of lattices and users such as philosophers of science, historians having reconstructed some small size semantic networks and other experts. This allows for the controlled production of phylomemies by assessing results via comparison with expert knowledge in the field and expert historians having reconstructed some small size semantic networks. We not only focus on the workflow itself, but we aim to come up with a system for producing, maintaining and adjusting phylomemetic lattices on demand. This brings the double opportunity to manage complex data more efficiently and to optimize the text mining workflow; e.g., compute only the required phylomemetic lattices, share (and save) computation among users, reuse workflow refinement strategies among users. The system, serving several users, will leverage on users’ experience to provide both unprecedented efficiency and new incentives to enrich users collaborations.
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