Quantifying knowledge from the perspective of information structurization.

Scientific literature, as the major medium that carries knowledge between scientists, exhibits explosive growth in the last century. Despite the frequent use of many tangible measures, to quantify the influence of literature from different perspectives, it remains unclear how knowledge is embodied and measured among tremendous scientific productivity, as knowledge underlying scientific literature is abstract and difficult to concretize. In this regard, there has laid a vacancy in the theoretical embodiment of knowledge for their evaluation and excavation. Here, for the first time, we quantify the knowledge from the perspective of information structurization and define a new measure of knowledge quantification index (KQI) that leverages the extent of disorder difference caused by hierarchical structure in the citation network to represent knowledge production in the literature. Built upon 214 million articles, published from 1800 to 2021, KQI is demonstrated for mining influential classics and laureates that are omitted by traditional metrics, thanks to in-depth utilization of structure. Due to the additivity of entropy and the interconnectivity of the network, KQI assembles numerous scientific impact metrics into one and gains interpretability and resistance to manipulation. In addition, KQI explores a new perspective regarding knowledge measurement through entropy and structure, utilizing structure rather than semantics to avoid ambiguity and attain applicability.

Scientific X-ray: Scanning and quantifying the idea evolution of scientific publications.

The rapid development of modern science nowadays makes it rather challenging to pick out valuable ideas from massive scientific literature. Existing widely-adopted citation-based metrics are not adequate for measuring how well the idea presented by a single publication is developed and whether it is worth following. Here, inspired by traditional X-ray imaging, which returns internal structure imaging of real objects along with corresponding structure analysis, we propose Scientific X-ray, a framework that quantifies the development degree and development potential for any scientific idea through an assembly of 'X-ray' scanning, visualization and parsing operated on the citation network associated with a target publication. We pick all 71,431 scientific articles of citation counts over 1,000 as high-impact target publications among totally 204,664,199 publications that cover 16 disciplines spanning from 1800 to 2021. Our proposed Scientific X-ray reproduces how an idea evolves from the very original target publication all the way to the up to date status via an extracted 'idea tree' that attempts to preserve the most representative idea flow structure underneath each citation network. Interestingly, we observe that while the citation counts of publications may increase unlimitedly, the maximum valid idea inheritance of those target publications, i.e., the valid depth of the idea tree, cannot exceed a limit of six hops, and the idea evolution structure of any arbitrary publication unexceptionally falls into six fixed patterns. Combined with a development potential index that we further design based on the extracted idea tree, Scientific X-ray can vividly tell how further a given idea presented by a given publication can still go from any well-established starting point. Scientific X-ray successfully identifies 40 out of 49 topics of Nobel prize as high-potential topics by their prize-winning papers in an average of nine years before the prizes are released. Various trials on articles of diverse topics also confirm the power of Scientific X-ray in digging out influential/promising ideas. Scientific X-ray is user-friendly to researchers with any level of expertise, thus providing important basis for grasping research trends, helping scientific policy-making and even promoting social development.