We consider the problem of finding plausible knowledge that is missing from a
given ontology, as a generalisation of the well-studied taxonomy expansion
task. One line of work treats this task as a Natural Language Inference (NLI)
problem, thus relying on the knowledge captured by language models to identify
the missing knowledge. Another line of work uses concept embeddings to identify
what different concepts have in common, taking inspiration from cognitive
models for category based induction. These two approaches are intuitively
complementary, but their effectiveness has not yet been compared. In this
paper, we introduce a benchmark for evaluating ontology completion methods and
thoroughly analyse the strengths and weaknesses of both approaches. We find
that both approaches are indeed complementary, with hybrid strategies achieving
the best overall results. We also find that the task is highly challenging for
Large Language Models, even after fine-tuning.

本文介绍了一个用于评估本体完成方法的基准，并对两种方法的优势和弱点进行了深入分析。我们发现这两种方法确实相辅相成，混合策略取得了最佳的整体效果。我们还发现，即使经过微调，本任务对大型语言模型来说仍然具有很高的挑战性。

自然语言推理和概念嵌入式本体补完分析

Ontology Completion with Natural Language Inference and Concept  Embeddings: An Analysis

Physics-Informed Neural Networks (PINN) are neural networks encoding the
problem governing equations, such as Partial Differential Equations (PDE), as a
part of the neural network. PINNs have emerged as a new essential tool to solve
various challenging problems, including computing linear systems arising from
PDEs, a task for which several traditional methods exist. In this work, we
focus first on evaluating the potential of PINNs as linear solvers in the case
of the Poisson equation, an omnipresent equation in scientific computing. We
characterize PINN linear solvers in terms of accuracy and performance under
different network configurations (depth, activation functions, input data set
distribution). We highlight the critical role of transfer learning. Our results
show that low-frequency components of the solution converge quickly as an
effect of the F-principle. In contrast, an accurate solution of the high
frequencies requires an exceedingly long time. To address this limitation, we
propose integrating PINNs into traditional linear solvers. We show that this
integration leads to the development of new solvers whose performance is on par
with other high-performance solvers, such as PETSc conjugate gradient linear
solvers, in terms of performance and accuracy. Overall, while the accuracy and
computational performance are still a limiting factor for the direct use of
PINN linear solvers, hybrid strategies combining old traditional linear solver
approaches with new emerging deep-learning techniques are among the most
promising methods for developing a new class of linear solvers.

本篇论文探讨了 PINN 作为线性求解器的潜力，在 Poisson 方程中评估了不同参数下的表现和精度，并且阐述了传递学习的关键作用。同时，论文也提出了将 PINN 与传统线性求解器相结合的方法，以解决高频解的问题，并表明该混合策略在发展具有潜力的新型线性求解器方面具有前景。