基于知识图谱的医疗问答系统研究

随着人工智能技术的不断发展,越来越多的自然语言处理技术应用到医疗行业。如何从海量医疗数据中提炼信息,并根据用户的问题给出针对性较强的回答,是推进医疗智能化的关键问题。文章研究利用BiLstm+CRF模型处理医疗领域问答相关数据,基于图数据库Neo4j构建一个医疗知识图谱,并在此基础上构建一个问答系统,实现医疗知识的自动问答服务。实验结果表明,该系统可以为用户提出的问题查找匹配准确答案并返回给用户。     

隐含语义索引在农业技术问答系统中的应用

利用隐含语义索引技术设计了一个问答系统,在系统中利用隐含语义索引理论进行查询问题和数据库中的候选问题的相似度计算.主要是通过构造一个语义矩阵,进行奇异值分解消除"噪音"进行实现的.这样更清晰地表示出了词之间的语义相关性,使本系统可以接受被自然语言描述的问题.最后,对整个系统进行实验测试并对测试结果进行了分析,发现本系统比一般的基于VSM等方法实现的系统表现出了明显的优势.     

基于蜜蜂知识图谱智能问答系统

针对农业信息化在蜜蜂领域缺失的问题,提出了一定规模的蜜蜂领域知识图谱的构建。首先,通过爬虫程序获取到真实有效的数据集,再经过相似度计算进行知识融合。然后,利用图数据库Neo4j对知识进行存储,完成蜜蜂领域的知识图谱的构建。最后,通过命名实体识别和问句意图识别任务分解智能问答,并通过Flask框架搭建可视化的页面,最终实现了智能问答系统。实验结果表明,所设计的智能问答系统在蜜蜂知识问答领域,在一定程度上弥补该领域的空白,也为农业信息化落地提供了思路。     

融合知识图谱和语义匹配的医疗问答系统

问答系统是自然语言处理领域中的一项重要任务,常应用于医疗服务。传统的问答系统通过知识图谱的实体关系匹配返回相应的尾实体作为答案,然而,倘若实体或关系无法识别,又或者在知识图谱中并不存在相应的实体关系,问答将无法继续进行。为了解决这一问题,建立一种融合知识图谱和语义匹配模型的中文医疗问答混合系统。当所提问题无法在知识图谱中进行实体关系匹配时,该模型能继续从问答对数据集中找到最相似的问题,并返回相应结果作为答案。在语义匹配模型方面,结合中文医疗相似问题对,在Sentence-BERT模型上进行微调训练,并引入双曲空间中的距离度量函数对句子对进行相似度度量。结果表明：在整体性能方面,所提模型相较于BERT这类大语言模型精度能提升7.16%;在度量能力方面,双曲度量相较于通用欧氏空间度量,如余弦度量,最高能有2.28%的精度提升和1.58%的F₁值提升。     

G2S:基于语义块的知识图谱问答语义解析

问题意图理解是知识图谱问答的主要任务之一,语义解析是当前理解问题意图的主流方法.其主要挑战是如何充分利用知识图谱上下文理解问句中的隐含实体或关系,以及时间、排序和聚合等复杂约束条件等意图.为了应对这些挑战,本文提出了一种基于语义块的知识图谱问答语义解析框架——Graph-to-Segment,框架中的语义解析模型结合了基于规则的准确度和基于深度学习的覆盖度,实现了问题到语义块序列的解析和语义查询图的构造.框架将问题意图使用基于语义块的语义查询图表示,将问题的语义解析建模为语义块序列生成任务,采用编码器-解码器神经网络模型实现问题到语义块序列的解析,然后通过语义块组装形成语义查询图.同时,结合知识图谱中的上下文信息,模型使用图神经网络学习问题的表示,改进隐含实体或关系的语义解析效果.在两个知识图谱问答数据集上的实验表明,模型性能达到了良好的效果.     

基于结构化问句实例的自动问答系统

研究了一种基于结构化问句实例分析问句的方法，设计了应用该方法时的各种语义知识及其表示，用Xml文档来管理领域知识，在这种知识结构上设计了一种答案抽取的方法。在此基础上，开发了BAQS的原型系统。实验表明方法可行，准确率和召回率可分别达到82．05％和91．95％。对问答系统的设计具有借鉴意义和继续深入研究的价值。     

基于知识图谱的营销服务智能问答系统设计

针对营销服务智能问答系统存在问答耗时长、问答准确率低和平均可接受率低的问题,设计了基于知识图谱的营销服务智能问答系统。系统硬件通过知识图谱结构,获取营销服务领域的知识卡片,采用文本相似度计算模块,实现营销服务语义搜索;系统软件在LSTM基础上选择属性,获得推理规则,构建营销服务的知识图谱,在相同数据条件下使营销服务智能问答系统回答更多的问题,完成营销服务智能问答系统的设计。实验结果表明,所设计系统的问答耗时短、问答准确率和平均可接受率较高。     

CMMB知识问答

问：CMMB节目是加密播出还是不加密播出，要获得CMMB服务需要办理什么手续？ 答：从现在开始到2008年底为试验播出阶段，在试播期间提供免费的广播电视节目。     

基于甲状腺知识图谱的自动问答系统的设计与实现

随着人们生活水平的提高,甲状腺结节类疾病日渐成为当代人的一种常见疾病,而中国国内医疗资源分布不均,造成了大医院人满为患,医生名下病人多,病人看病时间长等问题,许多病人想要看病在花费大量金钱的同时还要耗费大量时间.随着互联网技术以及计算机技术的发展,越来越多的病人为了节省时间,在赶往医院前,往往会在网络上对自己的病症进行相关查询,所以市面上出现了医生网上答诊和病人在线问诊的医疗咨询系统,一对一为病人回答问题.此举将医生提供给不能定时提问的网上咨询的患者的同时、会造成医院内医生资源更加紧缺的状况,且网上在线系统大多仅包括导诊流程,即帮助病人在前往医院确诊前对自己的病症有初步了解,并未涉及到病种诊断治疗,无法达到节约病人的时间的目的.因此,针对上述状况,本文选取甲状腺结节类病种数据为研究对象,对甲状腺真实数据进行重点的分析,创建甲状腺知识图谱,基于该知识图谱,设计并实现一个面向甲状腺诊疗的自动问答系统,本系统可以有效地回答病人在甲状腺类疾病方面的用药以及检查等方面的问题,节约病人问诊时间的同时,医生可以使用该系统对患者以及处方等信息进行相关查询,更加快速便捷,节约了医生的时间.     

基于中医药知识图谱的智能问答与用药推荐系统

将知识图谱与知识卡片相结合,使得用户既可以阅读文字以获取详细解释,又可以通过视觉的图形来感知知识本体之间的相关性,文字与图形相辅相成,配合得当。本智能问答系统是将无序的用户语料信息,进行科学有序的整理,通过CRF分词技术处理、提取自然语言关键词信息,并基于知识图谱基本原理获得反馈给用户的最终答案。作为用药的辅助推荐信息,以知识图谱和属性列表同时呈现。     

基于.NET的答疑系统研究与实现

随着网络技术的发展，网络答疑系统在网络教学中起着十分重要的作用。在分析目前的一些答疑系统的基础上，提出了一个比较全面的答疑系统模型，并对系统进行了功能模块的划分和详细分析。该系统充分结合学科知识库和FAQ库，灵活采用多种形式进行答疑；最后在．NET下使用流行的网络开发技术，实现了系统的主要功能，同时对一些关键技术进行了介绍。     

Ontology‐lexicon–based question answering over linked data

Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD‐5 benchmark and exhibits promising results.     

Local relation network with multilevel attention for visual question answering

With the tremendous success of the visual question answering (VQA) tasks, visual attention mechanisms have become an indispensable part of VQA models. However, these attention-based methods do not consider any relationship among regions, which is crucial for the thorough understanding of the image by the model. We propose local relation networks for generating context-aware image features for each image region, which contain information on the relationship among the other image regions. Furthermore, we propose a multilevel attention mechanism to combine semantic information from the LRNs and the original image regions, rendering the decision of the model more reasonable. With these two measures, we improve the region representation and achieve better attentive effect and VQA performance. We conduct numerous experiments on the COCO-QA dataset and the largest VQA v2.0 benchmark dataset. Our model achieves competitive results, proving the effectiveness of our proposed LRNs and multilevel attention mechanism through visual demonstrations.     

Visual question answering model based on visual relationship detection

visual question answering (VQA) is a learning task involving two major fields of computer vision and natural language processing. The development of deep learning technology has contributed to the advancement of this research area. Although the research on the question answering model has made great progress, the low accuracy of the VQA model is mainly because the current question answering model structure is relatively simple, the attention mechanism of model is deviated from human attention and lacks a higher level of logical reasoning ability. In response to the above problems, we propose a VQA model based on multi-objective visual relationship detection. Firstly, the appearance feature is used to replace the image features from the original object, and the appearance model is extended by the principle of word vector similarity. The appearance features and relationship predicates are then fed into the word vector space and represented by a fixed length vector. Finally, through the concatenation of elements between the image feature and the question vector are fed into the classifier to generate an output answer. Our method is benchmarked on the DQAUAR data set, and evaluated by the Acc WUPS@0.0 and WUPS@0.9.     

电信运营商知识图谱技术体系研究及应用实践

知识图谱在电信运营商业务及运维场景的知识搜索、工单处理、智能服务、故障分析等领域发挥着重要作用,以中国移动业务运营场景为依托,体系化地研究了知识图谱技术体系,基于应用场景、服务对象可分为问答检索类知识图谱、工单分析类知识图谱、系统运维类知识图谱和业务运营类知识图谱,并介绍其技术架构、业务架构及构建方法,引出知识图谱演进与发展的规划目标。     

基于交通知识的移动智能问答系统

交通知识与人的生命安全息息相关。针对如何方便快捷的获取交通知识,设计并实现了以即时通讯软件微信为人机交互媒介的移动智能自动问答系统。首先,对文本进行特征向量提取,并对同义词进行归一化,消除同义词对查询准确率的干扰;然后,综合词频和词性信息计算文本关键特征的权值;最后采用BM25模型计算问题与知识库中文本信息的相似度,返回与问题最相似的答案。实验表明,本系统的移动性强,人机交互友好,查询准确度高。     

改进的多模型融合技术在客服问答系统上的应用

随着人工智能技术的发展,越来越多的公司采用机器客服代替人工客服。但若采用传统关键词模型,则机器客服准确率难以提高;若采用深度学习模型进行训练,则又面临用户问题是短文本时,模型训练和预测效果不佳的问题。针对这些问题,通过深入研究和多次试验,提出一种融合关键词模型和基于字向量的深度学习模型的算法。最后实现了模型的训练和预测,在与传统算法的准确率对比方面展现了优势。     

藏文百科知识问答系统的设计与研究

目前国内的研究基本上都是中文自动问答系统的研究,关于藏文问答系统的研究还处于探索阶段,基于此本文计划参照中英文知识问答系统的设计方法,建立藏文百科知识库,在句法分析的基础上,设计藏文百科知识的自动问答系统.     

Application of graph neural network and feature information enhancement in relation inference of sparse knowledge graph

At present, knowledge embedding methods are widely used in the field of knowledge graph (KG) reasoning, and have been successfully applied to those with large entities and relationships. However, in research and production environments, there are a large number of KGs with a small number of entities and relations, which are called sparse KGs. Limited by the performance of knowledge extraction methods or some other reasons (some common-sense information does not appear in the natural corpus), the relation between entities is often incomplete. To solve this problem, a method of the graph neural network and information enhancement is proposed. The improved method increases the mean reciprocal rank (MRR) and Hits@3 by 1.6% and 1.7%, respectively, when the sparsity of the FB15K-237 dataset is 10%. When the sparsity is 50%, the evaluation indexes MRR and Hits@10 are increased by 0.8% and 1.8%, respectively.     

Semantic guided knowledge graph for large-scale zero-shot learning

Zero-shot learning has received growing attention, which aims to improve generalization to unseen concepts. The key challenge in zero-shot tasks is to precisely model the relationship between seen and unseen classes. Most existing zero-shot learning methods capture inter-class relationships via a shared embedding space, leading to inadequate use of relationships and poor performance. Recently, knowledge graph-based methods have emerged as a new trend of zero-shot learning. These methods use a knowledge graph to accurately model the inter-class relationships. However, the currently dominant method for zero-shot learning directly extracts the fixed connection from off-the-shelf WordNet, which will inherit the inherent noise in WordNet. In this paper, we propose a novel method that adopts class-level semantic information as a guidance to construct a new semantic guided knowledge graph (SG-KG), which can correct the errors in the existing knowledge graph and accurately model the inter-class relationships. Specifically, our method includes two main steps: noise suppression and semantic enhancement. Noise suppression is used to eliminate noise edges in the knowledge graph, and semantic enhancement is used to connect two classes with strong relations. To promote high efficient information propagation among classes, we develop a novel multi-granularity fusion network (MGFN) that integrates discriminative information from multiple GCN branches. Extensive experiments on the large-scale ImageNet-21K dataset and AWA2 dataset demonstrate that our method consistently surpasses existing methods and achieves a new state-of-the-art result.