This book provides a definition and study of a knowledge representation and reasoning formalism stemming from conceptual graphs, while focusing on the computational properties of this formalism.

Review on the Knowledge Graph in Robotics Domain--知识图谱在机器人领域的应用论文

Guerci教授的认知雷达入门书籍， Cognitive Radar：The Knowledge-Aided Fully Adaptive Approach

Knowledge Sharp This is a knowledge repository made for college student to share their own: Project experience higher degree persuit Course information 这个专案分享台湾国内各大专院校有毕业专题的校系，各组学生的个人与组经验, 目的是希望透过传承经验，提供毕业校友一个作品的线上查询管到, 并让学弟妹有相关于专题制作上的经验可以咨询 More Provision Programming and Web Design Tutorial Team Original Team: CCUMISKM @ National Cung Chen University * [Veck Hsiao](fbukevin@gmail.com) * [Ra

Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict what drugs are likely to target proteins involved with both diseases X and Y?—a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries—a flexible but tractable subset of first-order logic—on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.

该书详细地介绍了无线通信的基本概念

计算机视觉Github开源论文

基于部分知识证明的全模拟茫然传输，魏晓超，赵川， 茫然传输是一个重要的密码学基础工具,其可以被用来构造许多密码学方案,例如两方安全计算协议。在恶意模型下构造基于茫然传输的密

所有CK组件都可以在和！ 使用具有通用JSON API的集体知识工作流框架来统一AI以进行协作实验和优化 请注意，Caffe2已移至 GitHub源代码树，因此此处的某些软件包可能无法正常工作。 介绍 在将大部分的“研究”时间都花在了AI创新上之后，而不是在处理众多且不断变化的AI引擎，它们的API以及整个软件和硬件堆栈之后，我们决定采用另一种方法。 我们开始添加现有的AI框架，包括 ， ， ， ， ， CNTK和MVNC 。 非侵入式开源集体知识工作流框架（CK） 。 CK允许使用JSON API将各种版本的AI框架以及库，编译器，工具，模型和数据集作为统一的和可重用的组件插入，从而在Linux，Windows，MacOS和Android上自动化和自定义其安装（而不是使用ad- hoc脚本），并提供简单的JSON API进行常见操作（例如预测和培训）（请参阅demo ）。

随着大数据时代的到来，知识工程受到了广泛关注，如何从海量的数据中提取有用的知识，是 大数据分析的关键。知识图谱技术提供了一种从海量文本和图像中抽取结构化知识的手段，从而具有 广阔的应用前景。本文首先简要回顾知识图谱的历史，探讨知识图谱研究的意义。其次，介绍知识图 谱构建的关键技术，包括实体关系识别技术、知识融合技术、实体链接技术和知识推理技术等。然后， 给出现有开放的知识图谱数据集的介绍。最后，给出知识图谱在情报分析中的应用案例。