Neural-symbolic learning for knowledge base completion

A query answering task computes the prediction scores of ground queries inferred from a Knowledge Base (KB). Traditional symbolic-based methods solve this task using ‘exact’ provers. However, they are not very scalable and difficult to apply to current large KBs. Sub-symbolic methods have recently been proposed to address this problem. They require to be trained to learn the semantics of the symbolic representation and use it to make predictions about query answering. Such predictions may rely upon unknown rules over the given KB. Not all proposed sub-symbolic systems are capable of inducing rules from the KB; and even more challenging is the learning of rules that are human interpretable. Some approaches, e.g., those based on a Neural Theorem Prover (NTP), are able to address this problem but with limited scalability and expressivity of the rules that they can induce. We take inspiration from the NTP framework and propose three sub-symbolic architectures that solve the query answering task in a scalable manner while supporting the induction of more expressive rules. Two of these architectures, called Topical NTP (TNTP) and Topic-Subdomain NTP (TSNTP), address the scalability aspect. Trained representations of predicates and constants are clustered and the soft-unification of the backward chaining proof procedure that they use is controlled by these clusters. The third architecture, called Negation-as-Failure TSNTP (NAF TSNTP), addresses the expressivity of the induced rules by supporting the learning of rules with negation-as-failure. All these architectures make use of additional hyperparameters that encourage the learning of induced rules during training. Each architecture is evaluated over benchmark datasets with increased complexity in size of the KB, number of predicates and constants present in the KB, and level of incompleteness of the KB with respect to test sets. The evaluation measures the accuracy of query answering prediction and computational time. The former uses two key metrics, AUC_PR and HITS, adopted also by existing sub-symbolic systems that solve the same task, whereas the computational time is in terms of CPU training time. The evaluation performance of our systems is compared against that of existing state-of-the-art sub-symbolic systems, showing that our approaches are indeed in most cases more accurate in solving query answering tasks, whilst being more efficient in computational time. The increased accuracy in some tasks is specifically due to the learning of more expressive rules, thus demonstrating the importance of increased expressivity in rule induction.Open Acces

Scientists' bounded mobility on the epistemic landscape

Despite persistent efforts in revealing the temporal patterns in scientific careers, little attention has been paid to the spatial patterns of scientific activities in the knowledge space. Here, drawing on millions of papers in six disciplines, we consider scientists' publication sequence as "walks" on the quantifiable epistemic landscape constructed from large-scale bibliometric corpora by combining embedding and manifold learning algorithms, aiming to reveal the individual research topic dynamics and association between research radius with academic performance, along their careers. Intuitively, the visualization shows the localized and bounded nature of mobile trajectories. We further find that the distributions of scientists' transition radius and transition pace are both left-skewed compared with the results of controlled experiments. Then, we observe the mixed exploration and exploitation pattern and the corresponding strategic trade-off in the research transition, where scientists both deepen their previous research with frequency bias and explore new research with knowledge proximity bias. We further develop a bounded exploration-exploitation (BEE) model to reproduce the observed patterns. Moreover, the association between scientists' research radius and academic performance shows that extensive exploration will not lead to a sustained increase in academic output but a decrease in impact. In addition, we also note that disruptive findings are more derived from an extensive transition, whereas there is a saturation in this association. Our study contributes to the comprehension of the mobility patterns of scientists in the knowledge space, thereby providing significant implications for the development of scientific policy-making.Comment: article paper, 47 pages, 29 figures, 4 table

Science as Exploration in a Knowledge Landscape: Tracing Hotspots or Seeking Opportunity?

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in deep learning techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists' topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress.Comment: 23 pages, 10 figures, 1 tabl

La representación de la cortesía en la serie de televisión Sueño en el Pabellón Rojo (Wang Fulin, 1987)

La tesis doctoral —titulada La representación de la cortesía en la serie de televisión Sueño en el Pabellón Rojo (Wang Fulin, 1987)— responde el deseo de dar a conocer la cortesía en la cultura tradicional china estudiando una serie de televisión que adapta una novela clásica china del siglo XVIII. Tomamos la serie de televisión Sueño en el Pabellón Rojo de 1987 como objeto de estudio. Investigamos la producción de esta serie y la televisión china bajo el contexto social y cultural de la década de 1980 y estudiamos las teorías sobre la cortesía y sus distintas representaciones en esta serie de televisión, teniendo como referencia la sociedad feudal china del siglo XVIII. Analizamos la cortesía representada en los 36 episodios de la serie de televisión Sueño en el Pabellón Rojo para identificar las representaciones generales de la cortesía en el siglo XVIII de la antigua China. Tenemos en cuenta la novela Sueño en el Pabellón Rojo de Cao Xueqin, los estudios de la Rojología, la trayectoria del director Wang Fulin, el grupo de producción de la serie de televisión, entre otros..

Three dimensional photonic Dirac points in metamaterials

Topological semimetals, representing a new topological phase that lacks a full bandgap in bulk states and exhibiting nontrivial topological orders, recently have been extended to photonic systems, predominantly in photonic crystals and to a lesser extent, metamaterials. Photonic crystal realizations of Dirac degeneracies are protected by various space symmetries, where Bloch modes span the spin and orbital subspaces. Here, we theoretically show that Dirac points can also be realized in effective media through the intrinsic degrees of freedom in electromagnetism under electromagnetic duality. A pair of spin polarized Fermi arc like surface states is observed at the interface between air and the Dirac metamaterials. These surface states show linear k-space dispersion relation, resulting in nearly diffraction-less propagation. Furthermore, eigen reflection fields show the decomposition from a Dirac point to two Weyl points. We also find the topological correlation between a Dirac point and vortex/vector beams in classic photonics. The theoretical proposal of photonic Dirac point lays foundation for unveiling the connection between intrinsic physics and global topology in electromagnetism.Comment: 15 pages, 5 figure