This paper presents BigCilin, the first Chinese open-domain knowledge graph with fine-grained hypernym-hyponym re-lations which are extracted automatically from multiple sources for Chinese named entities. With the fine-grained hypernym-hyponym relations, BigCilin owns flexible semantic hierarchical structure. Since the hypernym-hyponym paths are automati-cally generated and one entity may have several senses, we provide a path disambi-guation solution to map a hypernym-hyponym path of one entity to its one sense on the condition that the path and the sense express the same meaning. In order to conveniently access our BigCilin Knowle-dge graph, we provide web interface in two ways. One is that it supports querying any Chinese named entity and browsing the extracted hypernym-hyponym paths surro-unding the query entity. The other is that it gives a top-down browsing view to illust-rate the overall hierarchical structure of our BigCilin knowledge graph over some sam-pled entities.
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概念图是一种特定类型的知识图表,在语义搜索中发挥着重要作用。现有概念图施工方法通常从正式文本中提取高频繁,粗粒度和时间不变的概念。然而,在实际应用中,有必要以不断发展的方式提取少频繁,细粒度和时变的概念知识并建立分类法。在本文中,我们介绍了在阿里巴巴实施和部署概念图的方法。具体而言,我们提出了一个叫做Alicg的框架,它能够通过对准共识方法,b)用新颖的低资源短语挖掘方法挖掘长尾概念来提取细粒度概念,C)更新图形基于隐式和显式用户行为动态通过概念分布估计方法。我们在阿里巴巴UC浏览器部署了框架。广泛的离线评估以及在线A / B测试证明了我们的方法的功效。
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开放信息提取是一个重要的NLP任务,它针对从非结构化文本中提取结构化信息的目标,而无需限制关系类型或文本域。该调查文件涵盖了2007年至2022年的开放信息提取技术,重点是以前的调查未涵盖的新模型。我们从信息角度来源提出了一种新的分类方法,以适应最近的OIE技术的开发。此外,我们根据任务设置以及当前流行的数据集和模型评估指标总结了三种主要方法。鉴于全面的审查,从数据集,信息来源,输出表格,方法和评估指标方面显示了几个未来的方向。
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外部知识(A.K.A.侧面信息)在零拍摄学习(ZSL)中起着关键作用,该角色旨在预测从未出现在训练数据中的看不见的类。已被广泛调查了几种外部知识,例如文本和属性,但他们独自受到不完整的语义。因此,一些最近的研究提出了由于其高度富有效力和代表知识的兼容性而使用知识图表(千克)。但是,ZSL社区仍然缺乏用于学习和比较不同外部知识设置和基于不同的KG的ZSL方法的标准基准。在本文中,我们提出了六个资源,涵盖了三个任务,即零拍摄图像分类(ZS-IMGC),零拍摄关系提取(ZS-RE)和零拍KG完成(ZS-KGC)。每个资源都有一个正常的zsl基准标记和包含从文本到属性的kg的kg,从关系知识到逻辑表达式。我们已清楚地介绍了这些资源,包括其建设,统计数据格式和使用情况W.r.t.不同的ZSL方法。更重要的是,我们进行了一项全面的基准研究,具有两个通用和最先进的方法,两种特定方法和一种可解释方法。我们讨论并比较了不同的ZSL范式W.R.T.不同的外部知识设置,并发现我们的资源具有开发更高级ZSL方法的巨大潜力,并为应用KGS进行增强机学习的更多解决方案。所有资源都可以在https://github.com/china-uk-zsl/resources_for_kzsl上获得。
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Practices in the built environment have become more digitalized with the rapid development of modern design and construction technologies. However, the requirement of practitioners or scholars to gather complicated professional knowledge in the built environment has not been satisfied yet. In this paper, more than 80,000 paper abstracts in the built environment field were obtained to build a knowledge graph, a knowledge base storing entities and their connective relations in a graph-structured data model. To ensure the retrieval accuracy of the entities and relations in the knowledge graph, two well-annotated datasets have been created, containing 2,000 instances and 1,450 instances each in 29 relations for the named entity recognition task and relation extraction task respectively. These two tasks were solved by two BERT-based models trained on the proposed dataset. Both models attained an accuracy above 85% on these two tasks. More than 200,000 high-quality relations and entities were obtained using these models to extract all abstract data. Finally, this knowledge graph is presented as a self-developed visualization system to reveal relations between various entities in the domain. Both the source code and the annotated dataset can be found here: https://github.com/HKUST-KnowComp/BEKG.
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Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of knowledge graphs is an inevitable key step towards the realization of human-level machine intelligence. The results of this endeavor are Multi-modal Knowledge Graphs (MMKGs). In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques. We then systematically review the challenges, progresses and opportunities on the construction and application of MMKGs respectively, with detailed analyses of the strength and weakness of different solutions. We finalize this survey with open research problems relevant to MMKGs.
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机器学习方法尤其是深度神经网络取得了巨大的成功,但其中许多往往依赖于一些标记的样品进行训练。在真实世界的应用中,我们经常需要通过例如具有新兴预测目标和昂贵的样本注释的动态上下文来解决样本短缺。因此,低资源学习,旨在学习具有足够资源(特别是培训样本)的强大预测模型,现在正在被广泛调查。在所有低资源学习研究中,许多人更喜欢以知识图(kg)的形式利用一些辅助信息,这对于知识表示变得越来越受欢迎,以减少对标记样本的依赖。在这项调查中,我们非常全面地审查了90美元的报纸关于两个主要的低资源学习设置 - 零射击学习(ZSL)的预测,从未出现过训练,而且很少拍摄的学习(FSL)预测的新类仅具有可用的少量标记样本。我们首先介绍了ZSL和FSL研究中使用的KGS以及现有的和潜在的KG施工解决方案,然后系统地分类和总结了KG感知ZSL和FSL方法,将它们划分为不同的范例,例如基于映射的映射,数据增强,基于传播和基于优化的。我们接下来呈现了不同的应用程序,包括计算机视觉和自然语言处理中的kg增强预测任务,还包括kg完成的任务,以及每个任务的一些典型评估资源。我们最终讨论了一些关于新学习和推理范式的方面的一些挑战和未来方向,以及高质量的KGs的建设。
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Wikidata是一个经常更新,社区驱动和多语言知识图形。因此,Wikidata是实体联系的一个有吸引力的基础,这是最近发表论文的增加显而易见的。该调查侧重于四个主题:(1)存在哪些Wikidata实体链接数据集,它们是多么广泛使用,它们是如何构建的? (2)对实体联系数据集的设计进行Wikidata的特点,如果是的话,怎么样? (3)当前实体链接方法如何利用Wikidata的特定特征? (4)现有实体链接方法未开发哪种Wikidata特征?本次调查显示,当前的Wikidata特定实体链接数据集在其他知识图表中的方案中的注释方案中没有不同。因此,没有提升多语言和时间依赖数据集的可能性,是自然适合维基帽的数据集。此外,我们表明大多数实体链接方法使用Wikidata以与任何其他知识图相同的方式,因为任何其他知识图都缺少了利用Wikidata特定特征来提高质量的机会。几乎所有方法都使用标签等特定属性,有时是描述,而是忽略超关系结构等特征。因此,例如,通过包括超关系图嵌入或类型信息,仍有改进的余地。许多方法还包括来自维基百科的信息,这些信息很容易与Wikidata组合并提供有价值的文本信息,Wikidata缺乏。
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各种网络的部署(例如,事物互联网(IOT)和移动网络),数据库(例如,营养表和食品组成数据库)和社交媒体(例如,Instagram和Twitter)产生大量的多型食品数据,这在食品科学和工业中起着关键作用。然而,由于众所周知的数据协调问题,这些多源食品数据显示为信息孤岛,导致难以充分利用这些食物数据。食物知识图表提供了统一和标准化的概念术语及其结构形式的关系,因此可以将食物信息孤单转换为更可重复使用的全球数量数字连接的食物互联网以使各种应用有益。据我们所知,这是食品科学与工业中食品知识图表的第一个全面审查。我们首先提供知识图表的简要介绍,然后主要从食物分类,食品本体到食品知识图表的进展。粮食知识图表的代表性应用将在新的配方开发,食品可追溯性,食物数据可视化,个性化饮食推荐,食品搜索和质询回答,视觉食品对象识别,食品机械智能制造方面来概述。我们还讨论了该领域的未来方向,例如食品供应链系统和人类健康的食品知识图,这应该得到进一步的研究。他们的巨大潜力将吸引更多的研究努力,将食物知识图形应用于食品科学和工业领域。
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Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming and labor-intensive. In parallel, although there has been much research on named entity recognition and relation extraction based on distantly supervised learning, constructing a domain-specific knowledge graph from large collections of textual data without manual annotations is still an urgent problem to be solved. In response, we propose an integrated framework for adapting and re-learning knowledge graphs from one coarse domain (biomedical) to a finer-define domain (oncology). In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation. Consequently, no manual data annotation is required to train the model. We introduce a novel iterative training strategy to facilitate the discovery of domain-specific named entities and triples. Experimental results indicate that the proposed framework can perform domain adaptation and construction of knowledge graph efficiently.
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Natural Language Processing (NLP) is one of the core techniques in AI software. As AI is being applied to more and more domains, how to efficiently develop high-quality domain-specific language models becomes a critical question in AI software engineering. Existing domain-specific language model development processes mostly focus on learning a domain-specific pre-trained language model (PLM); when training the domain task-specific language model based on PLM, only a direct (and often unsatisfactory) fine-tuning strategy is adopted commonly. By enhancing the task-specific training procedure with domain knowledge graphs, we propose KnowledgeDA, a unified and low-code domain language model development service. Given domain-specific task texts input by a user, KnowledgeDA can automatically generate a domain-specific language model following three steps: (i) localize domain knowledge entities in texts via an embedding-similarity approach; (ii) generate augmented samples by retrieving replaceable domain entity pairs from two views of both knowledge graph and training data; (iii) select high-quality augmented samples for fine-tuning via confidence-based assessment. We implement a prototype of KnowledgeDA to learn language models for two domains, healthcare and software development. Experiments on five domain-specific NLP tasks verify the effectiveness and generalizability of KnowledgeDA. (Code is publicly available at https://github.com/RuiqingDing/KnowledgeDA.)
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Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.
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从传统上讲,放射科医生准备诊断笔记,并与转录师分享。然后,抄写员准备了指参考票据的初步格式报告,最后,放射科医生审查报告,纠正错误并签字。该工作流程在报告中导致重大延迟和错误。在当前的研究工作中,我们专注于NLP技术(例如信息提取(IE)和域特异性知识图(KG))的应用,以自动从放射科医生的命令中生成放射学报告。本文通过从现有的自由文本放射学报告的大型语料库中提取信息来重点介绍每个器官的KG构造。我们开发了一种信息提取管道,将基于规则的,基于模式和基于词典的技术与词汇语义特征相结合,以提取实体和关系。可以从kgs访问简化的丢失信息,以产生病理描述,并因此是放射学报告。使用语义相似性指标评估了生成的病理描​​述,该指标与金标准病理描述显示了97%的相似性。另外,我们的分析表明,我们的IE模块的性能要比放射学域的开放式工具更好。此外,我们还包括放射科医生的手动定性分析,该分析表明80-85%的生成报告是正确编写的,其余部分是正确的。
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知识图是一个必不可少的和趋势技术,具有在实体识别,搜索或问题应答中的优势。在执行命名实体识别任务的自然语言处理中有一种方法;但是,有很少的方法可以为特定于域的文本提供三元组。在本文中,已经努力开发一个可以将文本从给定教科书转换为可以用于可视化的三元组的系统,以便为知识图形和用于进一步应用程序的系统。初步评估和评估给出了有希望的结果,F1得分为82%。
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在科学研究中,该方法是解决科学问题和关键研究对象的必不可少手段。随着科学的发展,正在提出,修改和使用许多科学方法。作者在抽象和身体文本中描述了该方法的详细信息,并且反映该方法名称的学术文献中的关键实体称为方法实体。在大量的学术文献中探索各种方法实体有助于学者了解现有方法,为研究任务选择适当的方法并提出新方法。此外,方法实体的演变可以揭示纪律的发展并促进知识发现。因此,本文对方法论和经验作品进行了系统的综述,重点是从全文学术文献中提取方法实体,并努力使用这些提取的方法实体来建立知识服务。首先提出了本综述涉及的关键概念的定义。基于这些定义,我们系统地审查了提取和评估方法实体的方法和指标,重点是每种方法的利弊。我们还调查了如何使用提取的方法实体来构建新应用程序。最后,讨论了现有作品的限制以及潜在的下一步。
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为了减轻从头开始构建知识图(kg)的挑战,更一般的任务是使用开放式语料库中的三元组丰富一个kg,那里获得的三元组包含嘈杂的实体和关系。在保持知识代表的质量的同时,以新收获的三元组丰富一个公园,这是一项挑战。本文建议使用从附加语料库中收集的信息来完善kg的系统。为此,我们将任务制定为两个耦合子任务,即加入事件提取(JEE)和知识图融合(KGF)。然后,我们提出了一个协作知识图融合框架,以允许我们的子任务以交替的方式相互协助。更具体地说,探险家执行了由地面注释和主管提供的现有KG监督的JEE。然后,主管评估了探险家提取的三元组,并用高度排名的人来丰富KG。为了实施此评估,我们进一步提出了一种翻译的关系一致性评分机制,以对齐并将提取的三元组对齐为先前的kg。实验验证了这种合作既可以提高JEE和KGF的表现。
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In this paper, we present kogito, an open-source tool for generating commonsense inferences about situations described in text. kogito provides an intuitive and extensible interface to interact with natural language generation models that can be used for hypothesizing commonsense knowledge inference from a textual input. In particular, kogito offers several features for targeted, multi-granularity knowledge generation. These include a standardized API for training and evaluating knowledge models, and generating and filtering inferences from them. We also include helper functions for converting natural language texts into a format ingestible by knowledge models - intermediate pipeline stages such as knowledge head extraction from text, heuristic and model-based knowledge head-relation matching, and an ability to define and use custom knowledge relations. We make the code for kogito available at https://github.com/epfl-nlp/kogito along with thorough documentation at https://kogito.readthedocs.io.
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自动问题应答(QA)系统的目的是以时间有效的方式向用户查询提供答案。通常在数据库(或知识库)或通常被称为语料库的文件集合中找到答案。在过去的几十年里,收购知识的扩散,因此生物医学领域的新科学文章一直是指数增长。因此,即使对于领域专家,也难以跟踪域中的所有信息。随着商业搜索引擎的改进,用户可以在某些情况下键入其查询并获得最相关的一小组文档,以及在某些情况下从文档中的相关片段。但是,手动查找所需信息或答案可能仍然令人疑惑和耗时。这需要开发高效的QA系统,该系统旨在为用户提供精确和精确的答案提供了生物医学领域的自然语言问题。在本文中,我们介绍了用于开发普通域QA系统的基本方法,然后彻底调查生物医学QA系统的不同方面,包括使用结构化数据库和文本集合的基准数据集和几种提出的方​​法。我们还探讨了当前系统的局限性,并探索潜在的途径以获得进一步的进步。
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In this paper we show the surprising effectiveness of a simple observed features model in comparison to latent feature models on two benchmark knowledge base completion datasets, FB15K and WN18. We also compare latent and observed feature models on a more challenging dataset derived from FB15K, and additionally coupled with textual mentions from a web-scale corpus. We show that the observed features model is most effective at capturing the information present for entity pairs with textual relations, and a combination of the two combines the strengths of both model types.
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关于概念及其属性的常识知识(CSK)有助于AI应用程序。诸如ConceptNet之类的先前作品已经编译了大型CSK集合。但是,它们的表现力限制在主题性 - 预处理(SPO)的三联元中,对p和o的s和字符串的简单概念。与先前的作品相比,CSK断言具有精致的表现力和更好的精度和回忆。 Ascent ++通过用子组和方面捕获复合概念,以及用语义方面的主张来捕获复合概念。后者对于表达断言和进一步预选赛的时间和空间有效性至关重要。此外,Ascent ++将开放信息提取(OpenIE)与典型性和显着性分数的明智清洁和排名相结合。对于高覆盖范围,我们的方法挖掘到具有广泛的Web内容的大规模爬网C4中。通过人类判断的评估显示了上升++ Kb的卓越质量,以及对QA支持任务的外部评估强调了Ascent ++的好处。可以在https://ascentpp.mpi-inf.mpg.de/上访问Web界面,数据和代码。
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