The Polkadot ecosystem is a disruptive and highly complex multi-chain
architecture that poses challenges in terms of data analysis and
communicability. Currently, there is a lack of standardized and holistic
approaches to retrieve and analyze data across parachains and applications,
making it difficult for general users and developers to access ecosystem data
consistently. This paper proposes a conceptual framework that includes a domain
ontology called POnto (a Polkadot Ontology) to address these challenges. POnto
provides a structured representation of the ecosystem's concepts and
relationships, enabling a formal understanding of the platform. The proposed
knowledge-oriented approach enhances integration and communicability, enabling
a wider range of users to participate in the ecosystem and facilitating the
development of AI-based applications. The paper presents a case study
methodology to validate the proposed framework, which includes expert feedback
and insights from the Polkadot community. The POnto ontology and the roadmap
for a query engine based on a Controlled Natural Language using the ontology,
provide valuable contributions to the growth and adoption of the Polkadot
ecosystem in heterogeneous socio-technical environments.

提出了一个概念框架，包括一个称为 POnto 的领域本体论，以解决 Polkadot 生态系统中的数据分析和可通信性方面的挑战，并通过知识导向的方法增强集成和可通信性，在异构社会技术环境中为 Polkadot 生态系统的增长和采用做出了有价值的贡献。

一个面向知识的方法来增强 Polkadot 生态系统中的集成和沟通能力

A Knowledge-Oriented Approach to Enhance Integration and Communicability  in the Polkadot Ecosystem

Conscious states (states that there is something it is like to be in) seem
both rich or full of detail, and ineffable or hard to fully describe or recall.
The problem of ineffability, in particular, is a longstanding issue in
philosophy that partly motivates the explanatory gap: the belief that
consciousness cannot be reduced to underlying physical processes. Here, we
provide an information theoretic dynamical systems perspective on the richness
and ineffability of consciousness. In our framework, the richness of conscious
experience corresponds to the amount of information in a conscious state and
ineffability corresponds to the amount of information lost at different stages
of processing. We describe how attractor dynamics in working memory would
induce impoverished recollections of our original experiences, how the discrete
symbolic nature of language is insufficient for describing the rich and
high-dimensional structure of experiences, and how similarity in the cognitive
function of two individuals relates to improved communicability of their
experiences to each other. While our model may not settle all questions
relating to the explanatory gap, it makes progress toward a fully physicalist
explanation of the richness and ineffability of conscious experience: two
important aspects that seem to be part of what makes qualitative character so
puzzling.

通过信息论动力系统的视角看待意识的复杂性和难以言表性，阐述工作记忆中的吸引子动力学如何导致我们原始体验的贫瘠回忆，语言的不足和两个人的认知功能相似性如何提高沟通能力，从而解释思维定势，使得我们对定性特征是什么产生了疑惑。

现象意识状态丰富性和难以言喻性的来源

Sources of Richness and Ineffability for Phenomenally Conscious States

The notions of subgraph centrality and communicability, based on the
exponential of the adjacency matrix of the underlying graph, have been
effectively used in the analysis of undirected networks. In this paper we
propose an extension of these measures to directed networks, and we apply them
to the problem of ranking hubs and authorities. The extension is achieved by
bipartization, i.e., the directed network is mapped onto a bipartite undirected
network with twice as many nodes in order to obtain a network with a symmetric
adjacency matrix. We explicitly determine the exponential of this adjacency
matrix in terms of the adjacency matrix of the original, directed network, and
we give an interpretation of centrality and communicability in this new
context, leading to a technique for ranking hubs and authorities. The matrix
exponential method for computing hubs and authorities is compared to the well
known HITS algorithm, both on small artificial examples and on more realistic
real-world networks. A few other ranking algorithms are also discussed and
compared with our technique. The use of Gaussian quadrature rules for
calculating hub and authority scores is discussed.

本研究将邻接矩阵的指数中的子图中心性和可通性的概念扩展到有向网络，并将它们应用于排名中心枢和权威。这种扩展是通过二分法实现的，具体地，将有向网络映射到具有两倍节点数的二分无向网络，以得到具有 对称邻接矩阵的网络。然后，将中心性和可共性在这个新的上下文中进行解释，提出一种用于排名中心枢和权威的技术，并讨论与其它排名算法的比较。