基于知识图谱的互补项目推荐

在缺乏用户交互互补项目方面数据的情况下,将用户对项目的偏好融合到只考虑项目关系的互补项目推荐中,提高推荐模型的性能。提出一种基于知识图谱的互补项目推荐方法,在用户历史交互项目集中推测用户交互的互补项目,基于知识图谱提取用户对互补项目的偏好,利用图像与文本学习项目之间的互补关系,最后基于神经网络实现二者的共同学习。提出的方法在Amazon数据集上与次优的基线方法相比,ACC提升了7%,precision提升了3%,这说明提出的方法性能优异。该算法共同学习用户对项目的偏好与项目之间的互补关系,提升了推荐性能。     

基于ECC-ZKP的农产品供应链可控身份管理与认证模型

基于区块链的农产品供应链中,当参与主体数量规模越来越大时,节点与企业之间的1：1匹配关系将造成节点数量众多,从而造成网络开销大、共识效率低等问题。针对这些问题,引入中间件使得节点与企业之间1：1关系变为1：n的关系,对所涉及到的企业身份管理以及相互之间的认证,提出一种基于ECC-ZKP（elliptic curve cryptosystem-zero—knowledge proof）的可控身份管理与认证模型,实现身份的可控管理和完成可信交易前所需要的身份认证。通过模型分析和仿真实验分析结果表明,该模型能够提供较高的安全性,能够有效进行身份的管理和认证,且节点与企业之间1：n关系相比较于1：1关系减少了网络开销并提高了共识效率。     

概念表示增强的知识追踪模型

知识追踪模型以学习者的历史学习行为数据作为输入,通过概念表示来描述学习者的概念掌握状态,从而预测学习者未来的学习表现。然而在概念的外延表示方面,当前知识追踪研究的概念外延信息被限制在一阶相关的范畴内,无法表征概念的一阶以上外延信息。为了解决这一问题,提出方法首先使用图结构描述概念内涵信息及其相互关系;其次使用图神经网络的池化操作等提取概念的外延表示,这保证了概念的外延信息来源于多阶相关关系;再与概念的内涵表示进行融合;最后预测学习者未来的答题情况。为了验证该模型的有效性和效率,选取了四个主流知识追踪模型作为对比模型,在四个常用的知识追踪数据集上进行实验。结果表明,提出模型在若干评价指标上均取得了一定的优势,说明了它的有效性;在模型性能方面,提出模型达到最优评价指标所需的迭代次数最少,说明了它的效率;在实际应用方面,以该模型为基础实现了一个智能学习平台,在三门线下课程的教学过程中判断和预测学习者未来答题情况,取得了优于其他知识追踪模型的表现。     

Potential applications of three-way multidimensional scaling and related techniques to integrate knowledge from multiple experts

Theknowledge transfer problem in artificial intelligence consists of finding effective ways to elicit information from a human expert and represent it in a form suitable for use by an expert system. One approach to formalizing and guiding this knowledge transfer process for certain types of expert systems is to use psychometric scaling methods to analyze data on how the human expert compares or groups solutions. For example, Butler and Corter [1] obtained judgments of thesubstitutability of solutions from an expert, then analyzed the resulting data via techniques for fitting trees and extended trees [2]. The expert's interpretation of certain aspects of the solutions were directly encoded as production rules, allowing rapid prototyping. In this paper we consider the problem of combining information from multiple experts. We propose the use of three-way or individual differences multidimensional scaling, tree-fitting, and unfolding models to analyze two types of data obtainable from the multiple experts: judgments of the substitutability of pairs of solutions, and judgments of the appropriateness of specific solutions to specific problems. An application is described in which substitutability data were obtained from three experts and analyzed using the SINDSCAL program [3] for three-way multidimensional scaling [4].     

DETECTOR: A knowledge-based system for injection molding diagnostics

A knowledge-based system (KBS) for diagnosis of multiple defects in injection molding is presented. The general scheme for knowledge representation based on fuzzy set theory has been shown useful in representing inexact and incomplete information for developing the KBS. An optimality criterion is created for selecting a simple and best cover to explain the given problem. An efficient search algorithm for finding such cover is also discussed.     

What can the history of AI learn from the history of science?

There have been few attempts, so far, to document the history of artificial intelligence. It is argued that the historical sociology of scientific knowledge can provide a broad historiographical approach for the history of AI, particularly as it has proved fruitful within the history of science in recent years. The article shows how the sociology of knowledge can inform and enrich four types of project within the history of AI; organizational history; AI viewed as technology; AI viewed as cognitive science and historical biography. In the latter area the historical treatments of Darwin and Turing are compared to warn against the pitfalls of rational reconstructions of the past.     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

融合知识图谱的网络安全违法行为识别系统

《网络安全法》是我国第一部关于网络安全的综合立法,与大众的互联网生活息息相关.因此,一款面向大众的《网络安全法》智能违法行为识别系统有助于规范互联网行为.然而,现有智能违法行为识别系统构建方法难以适应《网络安全法》,这是由于:首先,现有方法需要专业司法语言进行交互,不适应普通大众的语言体系.其次,现有方法需要大量的案例训练模型,不适应案例匮乏的《网络安全法》.针对这些问题,本文提出了一个面向《网络安全法》的智能违法行为识别系统.该系统主要利用知识图谱技术解决上述问题,在构建网络安全法知识图谱的基础上,通过将普通用户的自然语言与知识图谱中的违法事件实体和违法主体实体进行实体链接的方式获得更强的特征,提高违法行为识别系统在训练集较为匮乏的条件下的准确度.通过在真实数据集上的实验,表明了提出的系统的准确度有明显提高.     

Efficient design of SIW filters with knowledge‐embedded space mapping technique

This article presents an optimization technique for the design of substrate‐integrated waveguide (SIW) filters using knowledge‐embedded space mapping. An effective coarse model is proposed to represent the SIW filter. The proposed coarse model can be analyzed in the available commercial software ADS. The embedded knowledge includes not only formulas but also extracted design curves, which help to build the mapping between the coarse and fine models. The effectiveness of the proposed method is demonstrated through a design example of a six‐pole SIW filter. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

Taking advantage of collective knowledge in emergency response systems

When large groups work on a theme, they have the potential to produce a lot of useful knowledge, regardless of whether they are acting in a coordinated manner or individually. Spontaneously generated information has received much attention in recent years, as organizations and businesses discover the power of crowds. New technologies, such as blogs, Twitter, wikis, photo sharing, collaborative tagging and social networking sites, enable the creation and dissemination of content in a relatively simple way. As a result, the aggregate body of knowledge is growing at an accelerated rate. Many organizations are looking for ways to harness this power, which is being called collective intelligence. Research has shown that it is possible to obtain high quality results from collectively produced work.In this paper, we consider the domain of emergency response. Research has shown that individuals respond quickly and massively to emergencies, and that they try to help with the situation. Thus, it seems like a logical step to attempt to harness collective knowledge for emergency management. Disaster relief groups and field command frequently suffer from lack of up to date information, which may be critical in a rapidly evolving situation. Some of this information could be generated by the crowd at large, enabling more effective response to the situation. In this paper, we discuss the possibilities for the introduction of collective knowledge in disaster relief and present architecture and examples of how this could be accomplished.