共查询到20条相似文献，搜索用时 31 毫秒
1.
《Information processing & management》2019,56(6):102066
The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making.In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme.The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph.This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime.We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis. 相似文献
2.
3.
基于现行数据隐私问题日益严重,如何防止数据挖掘过程中隐私信息的泄漏,将是一个重要的研究议题。本文主要针对关联规则挖掘技术,从安全多方计算方面探讨隐私信息的保护,提出适用于挖掘水平分割数据的保护机制。方法设计采用集中式挖掘,过程中加入信息安全技术以确保参与单位的数据隐私,以求在隐私保护和知识获取间取得一个平衡。 相似文献
4.
租糙集理论作为一种处理不完备信息的有利工具,巳广泛应用于人工智能的许多领域,特别是数据挖掘和知识发现领域.文章将基于粗糙集理论的数据挖掘技术应用于钢结构缺陷模式识别的知识发现,在智能诊断的知识自动获取方面取得了较好的效果. 相似文献
5.
【目的/意义】中医医案中包含的隐性知识具有巨大的价值,通过数据挖掘技术对中医医案进行分析,挖掘
临床医案中的隐性知识,有利于知名中医个人知识经验的传承和中医理论知识的积累和拓展。【方法/过程】收集中
医医案中患者的主诉和现病史,并对主诉和现病史进行数据清洗,通过基于cart算法的数据挖掘技术挖掘中医医案
中的隐性知识,探究病症与患者症候各个属性之间的关系。【结果/结论】本文以胃脘痛为例,发现了胃脘痛与患者
症候各个属性之间的相关程度,为数据挖掘技术在中医医案隐性知识挖掘研究提供借鉴。【创新/局限】本文采用的
cart算法判断胃脘痛与患者属性之间的相关性与医案中确诊结果进行比较,得出该方法相关性准确率高于ID3算
法、C4.5算法和SLIQ算法,判断相关性最高。 相似文献
6.
7.
Tupasela A 《Public understanding of science (Bristol, England)》2007,16(1):63-78
Despite recent evidence that suggests that knowledge production within the medical community is increasingly based on knowledge-making coalitions or what some have called the co-production of knowledge, there remains a strong expert led policy agenda in many countries in relation to human genome research. This article reports on the role of experts in defining the scope of discussion in relation to the biomedical use of human tissue sample collections or biobanks in Finland using the case of the Genome Information Center. It is argued that the rhetorical strategies should not be understood simply as subversive tactics, but rather as ways of engaging the public within particular contexts of creating commercial expectations and visions which are easier to negotiate from a policy perspective. 相似文献
8.
【目的/意义】作为医疗与管理科学领域最为重要的研究课题之一,在线健康社区智能诊疗在我国“互联网+
医疗”新业态发展背景下扮演重要角色。【方法/过程】本文从本体论与CBR视角出发,构建基于知识库与案例库的
在线健康社区诊疗解决方案自动推理模型;通过八爪鱼采集器获取“好大夫在线”疾病科普以及医患问答数据构建
本体,并利用文本分析挖掘出可解释的疾病知识及解决方案,分别实现知识库与案例库的半自动构建;以成年人先
天性心脏病为例,将 ACHD-AP进行形式化定义,采用推理引擎对患者案例进行风险分类及划分至对应的疾病知
识库,并实现诊疗解决方案的自动推理。【结果/结论】研究显示,本文诊疗解决方案推理结论与专家建议相似度较
高,且OntoQA评估下的知识库以及案例库本体层次结构关系合理。【创新/局限】基于知识库与案例库的诊疗解决
方案自动推理模型为后续在线健康社区实现智能诊疗以及服务模式的创新提供了方法上的参考。 相似文献
9.
[目的/意义]有效融合引文网络中的引用关系和文本属性等多元数据,增强文献节点间的语义关联,从而为数据挖掘和知识发现等任务提供有力的支撑。[方法/过程]提出了一种引文网络的知识表示方法,先利用神经网络模型学习引文网络中的k阶邻近结构;然后使用doc2vec模型学习标题、摘要等文本属性;最后给出了基于向量共享的交叉学习机制用于多元数据融合。[结果/结论]通过面向干细胞领域的CNKI引文数据集的测试,在链路预测上取得了较好的性能,证明了方法的有效性和科学性。 相似文献
10.
针对医学诊断知识获取问题，提出了基于Rough Sets理论的知识获取方法，利用该理论对数据进行分析，推理出可能规则，并提出了一种概率优化规则。通过实例分析，具体说明了该方法的实现步骤，包括连续信息系统的离散化、信息系统的约简、决策规则提取、决策模型生成等，讨论了知识处理的完整过程，能够有效地解决专家系统中知识获取的瓶颈问题。为人工智能技术在医学诊断领域的应用提供了新的思路。 相似文献
11.
利用Cite SpaceⅡ对从Web of Science数据库检索到的1 156条数据为基础,通过关键词共现网络分析发现,团队知识共享的前沿是:"团队领导"问题、"软件实现"问题、"专家系统"问题、"网络"问题、"健康医学"领域的应用问题"、开发成熟度"问题。通过引文作者网络图谱分析发现:Polanyi M在《隐性维度》、CohenWM《吸收能力:基于学习与创新的新视角》、Nonaka I的《组织知识创造的动态理论》、Grant RM《基于知识理论的企业》等对"知识特性""、知识创造与转化"、"组织学习"以及"组织绩效"等方面的研究等构成了团队知识共享的基础。 相似文献
12.
数据挖掘在知识管理中的应用 总被引：1，自引：0，他引：1
数据挖掘是数据库和信息决策领域的一个理论前沿,是知识发现的核心部分。本文从数据挖掘的基本概念入手,介绍了数据挖掘常用的技术和方法,在此基础上,对数据挖掘技术在知识管理中的应用作了深入的论述。 相似文献
13.
专家系统是人工智能中最重要的也是最活跃的一个应用领域,它实现了人工智能从理论研究走向实际应用,从一般推理策略探讨转向运用专门知识的重大突破。本文按照时间顺序,将专家系统划分为20世纪80年代以前、20世纪80年代、20世纪90年代、2000年后4个阶段。文章利用文献计量学的方法,分析了专家系统发展的历程、发展方向和趋势,指出当前正是专家系统的发展期,多种专家系统投入商业化运行,需要解决知识获取瓶颈、匹配冲突等问题,为了解和掌握专家系统的学科结构、演化过程、发展方向等提供了独特的视角和知识。 相似文献
14.
针对创新社区日益增长的海量信息阻碍了用户对知识进行有效获取和创造的现状,将模糊形式概念分析（FFCA）理论应用于创新社区领先用户的个性化知识推荐研究。首先识别出创新社区领先用户并对其发帖内容进行文本挖掘得到用户——知识模糊形式背景,然后构建带有相似度的模糊概念格对用户偏好进行建模,最后基于模糊概念格和协同过滤的推荐算法为领先用户提供个性化知识推荐有序列表。以手机用户创新社区为例,验证了基于FFCA的领先用户个性化知识推荐方法的可行性,有助于满足用户个性化知识需求,促进用户更好地参与社区知识创新。 相似文献
15.
[目的/意义]随着大数据和人工智能时代的来临，基于数据驱动的医疗辅助决策以及医疗健康知识挖掘受到人们的极大关注。医疗知识图谱是医疗信息分析和知识服务的基础，在医疗人工智能和医疗信息检索中发挥着重要作用。[方法/过程]本文以医疗领域的实际应用需求为出发点，从医疗大数据获取、医疗实体及关系标注、医疗实体识别、医疗实体链接、医疗实体关系挖掘、中文医疗知识图谱表示和存储等关键技术入手，提出了多数据源融合的医疗知识图谱构建的理论框架。[结果/结论]面向医疗领域的知识图谱构建是一项非常重要的基础任务，同时也是人工智能领域的重要发展方向。 相似文献
16.
The creation of expert systems is one way in which knowledge is codified. In creating an expert system, in general there are three aspects to the codification process: creating a model of the knowledge to be codified; creating the language in which to express the model; and writing messages representing the knowledge in that language. These aspects have different relative importance in different contexts. Referring to four case studies, the paper argues further that codification through creating expert systems is not uniformly successful and part of the variation has to do with the nature of the knowledge, or intellectual process or activity being codified, and the difficulty in creating the model. Activities with fixed goals and linear processes lend themselves very well to this form of codification. Processes of categorization and analogy, such as fault detection and repair, are partially successful, tending more often to create systems for experts. Processes involving balancing conflicting goals have, to date, tended to be unsuccessfully codified. These process types involve knowledge or understanding at deeper and deeper levels of abstraction of the overall processes and overarching goals. The more abstract and less concrete the knowledge involved in the task, the more difficult it is to codify it. 相似文献
17.
Judith Simon 《Ethics and Information Technology》2010,12(4):343-355
In this paper I use philosophical accounts on the relationship between trust and knowledge in science to apprehend this relationship
on the Web. I argue that trust and knowledge are fundamentally entangled in our epistemic practices. Yet despite this fundamental
entanglement, we do not trust blindly. Instead we make use of knowledge to rationally place or withdraw trust. We use knowledge
about the sources of epistemic content as well as general background knowledge to assess epistemic claims. Hence, although
we may have a default to trust, we remain and should remain epistemically vigilant; we look out and need to look out for signs
of insincerity and dishonesty in our attempts to know. A fundamental requirement for such vigilance is transparency: in order
to critically assess epistemic agents, content and processes, we need to be able to access and address them. On the Web, this
request for transparency becomes particularly pressing if (a) trust is placed in unknown human epistemic agents and (b) if
it is placed in non-human agents, such as algorithms. I give examples of the entanglement between knowledge and trust on the
Web and draw conclusions about the forms of transparency needed in such systems to support epistemically vigilant behaviour,
which empowers users to become responsible and accountable knowers. 相似文献
18.
针对当前竞争情报分析算法无法有效地处理结构类型多样的Web评论信息,研究并设计了基于Web评论挖掘的动态竞争情报分析算法进行语义层面的情报分析,获取深层次的动态竞争情报。该算法主要由评论内容识别与抽取、语义关联挖掘、文本语义极性分析3个部分组成。实验结果表明,该算法能够显著地提高情报分析的准确率和效率,实现深层次的评论挖掘和情报知识发现。 相似文献
19.
机会发现及其在情报学方面的应用 总被引：2，自引：0，他引：2
从知识发现角度提出的机会发现作为一个新的研究领域,不仅为稀有潜在信息的研究奠定基础,更拓展了数据分析与挖掘的视角。本文从机会发现的产生背景、研究现状与实现方法出发,探讨机会发现与知识发现、情报学的联系,以及其在情报学中的应用前景。 相似文献
20.
[目的/意义]为从大数据中发现潜在规律和提取有用的知识,并有效解决图书馆在个性化智慧服务中存在的问题。[方法/过程]面对读者的智慧阅读需求,本文研究了图书馆在个性化智慧服务中应注意的问题,构建了基于大数据挖掘与知识发现的图书馆智慧服务体系,并论述了智慧图书馆各服务层的功能。[结果/结论]该体系可实现资源和服务的高度融合,显著减少系统资源的占用,能够依据读者需求智慧化地定制服务,并为读者提供个性化智慧阅读服务。 相似文献