A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

SHEL:a semantically enhanced hardware-friendly entity linking method

With the help of pre-trained language models,the accuracy of the entity linking task has made great strides in recent years.However,most models with excellent performance require fine-tuning on a large amount of training data using large pre-trained language models,which is a hardware threshold to accomplish this task.Some researchers have achieved competitive results with less training data through ingenious methods,such as utilizing information provided by the named entity recognition model.This paper presents a novel semantic-enhancement-based entity linking approach,named semantically enhanced hardware-friendly entity linking(SHEL),which is designed to be hardware friendly and efficient while maintaining good performance.Specifically,SHEL's semantic enhancement approach consists of three aspects:(1)semantic compression of entity descriptions using a text summarization model;(2)maximizing the capture of mention contexts using asymmetric heuristics;(3)calculating a fixed size mention representation through pooling operations.These series of semantic enhancement methods effectively improve the model's ability to capture semantic information while taking into account the hardware constraints,and significantly improve the model's convergence speed by more than 50%compared with the strong baseline model proposed in this paper.In terms of performance,SHEL is comparable to the previous method,with superior performance on six well-established datasets,even though SHEL is trained using a smaller pre-trained language model as the encoder.

Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

Link prediction,also known as Knowledge Graph Completion(KGC),is the common task in Knowledge Graphs(KGs)to predict missing connections between entities.Most existing methods focus on designing shallow,scalable models,which have less expressive than deep,multi-layer models.Furthermore,most operations like addition,matrix multiplications or factorization are handcrafted based on a few known relation patterns in several wellknown datasets,such as FB15k,WN18,etc.However,due to the diversity and complex nature of real-world data distribution,it is inherently difficult to preset all latent patterns.To address this issue,we proposeKGE-ANS,a novel knowledge graph embedding framework for general link prediction tasks using automatic network search.KGEANS can learn a deep,multi-layer effective architecture to adapt to different datasets through neural architecture search.In addition,the general search spacewe designed is tailored forKGtasks.We performextensive experiments on benchmark datasets and the dataset constructed in this paper.The results show that our KGE-ANS outperforms several state-of-the-art methods,especially on these datasets with complex relation patterns.

Semantic Link Network Based Knowledge Graph Representation and Construction

A knowledge graph consists of a set of interconnected typed entities and their attributes,which shows a better performance to organize,manage and understand knowledge.However,because knowledge graphs contain a lot of knowledge triples,it is difficult to directly display to researchers.Semantic Link Network is an attempt,and it can deal with the construction,representation and reasoning of semantics naturally.Based on the Semantic Link Network,this paper explores the representation and construction of knowledge graph,and develops an academic knowledge graph prototype system to realize the representation,construction and visualization of knowledge graph.

A case study on knowledge communication based on friends-list links in the science blogging community at Sciencenet.cn

Sciencenet.cn is the leading online portal serving the Chinese scientific community. This paper intends to analyze the interdisciplinary and intradisciplinary knowledge communication patterns based on friends-list links in the blog community at Sciencenet.cn by using hyperlink analysis and social network analysis. The major findings are: 1) More bloggers have an academic background in management science and life science; 2) there are some core actors in co-inlink network and co-outlink network, who take the lead in engaging with knowledge exchange activities and produce a great influence on interdisciplinary communication; 3) interactive relationships commonly exist between a blogger and those on his/her friends list, and the most linked-to blogs usually play a key role in generating interactive communication; 4) management science has the highest co-inlink count with life science or information science and it has the highest co-outlink count with life science or mathematical and physical science; 5) management science and life science have the greatest impact on information science and the interdisciplinary knowledge communication will also produce relatively significant influence on the development of information science discipline. It is our hope that this research can serve as a reference source for the future studies of academic virtual communities, and the development of mechanisms for facilitating increased engagement in knowledge exchange activities in academic virtual communities.

ISI Web of Knowledge集成检索平台评析

本文从资源整合与集成检索的角度出发,对 ISI Web of KnowledgeSM 平台的集成化信息检索机制作了一个全面的解析;同时指出其优势与不足之处,希望为我国图书情报机构打造成熟的、满足用户方便、快捷获取所需信息的检索平台提供一点借鉴。

Knowledge Map编码与定位算法

该文提出用Hash来对KM(Knowledge Map)中的领域方向进行编码,形成领域方向Island.提出KM概念向量与概念间关系类型的编码方法.基于上述编码,利用Chord对海量Island进行快速定位;并在Island的控制下,提出C-Location与R-Location对相同领域方向的KM进行快速定位.Island可使具有相同领域方向的KM自动聚集在一起.实例计算说明该方法可对KM进行有效的编码与精确的定位,并具有较强的灵活性.

Learning Context-based Embeddings for Knowledge Graph Completion

Purpose:Due to the incompleteness nature of knowledge graphs(KGs),the task of predicting missing links between entities becomes important.Many previous approaches are static,this posed a notable problem that all meanings of a polysemous entity share one embedding vector.This study aims to propose a polysemous embedding approach,named KG embedding under relational contexts(ContE for short),for missing link prediction.Design/methodology/approach:ContE models and infers different relationship patterns by considering the context of the relationship,which is implicit in the local neighborhood of the relationship.The forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors,which represent the contextual information of the relationship.Then,according to the position of the entity,the entity’s polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the relationship.Findings:ContE is a fully expressive,that is,given any ground truth over the triples,there are embedding assignments to entities and relations that can precisely separate the true triples from false ones.ContE is capable of modeling four connectivity patterns such as symmetry,antisymmetry,inversion and composition.Research limitations:ContE needs to do a grid search to find best parameters to get best performance in practice,which is a time-consuming task.Sometimes,it requires longer entity vectors to get better performance than some other models.Practical implications:ContE is a bilinear model,which is a quite simple model that could be applied to large-scale KGs.By considering contexts of relations,ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning,it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction tasks.Originality/value:ContE considers the contexts of entities in terms of their positions in triples and the relationships they link to.It decomposes a relation vector into two vectors,namely,forward impact vector and backward impact vector in order to capture the relational contexts.ContE has the same low computational complexity as TransE.Therefore,it provides a new approach for contextualized knowledge graph embedding.

World-Wide Semantic Web of Agriculture Knowledge

The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly describe the evolution of the LOD, the emerging world-wide semantic web （WWSW）, and explore the scalability and performance features Of the service oriented architecture that forms the foundation of the semantic technology platform developed at MIMOS Bhd., for addressing the challenges posed by the intelligent future internet. This paper＂ concludes with a review of the current status of the agriculture linked open data.

BioBroker: Knowledge Discovery Framework for Heterogeneous Biomedical Ontologies and Data

A large number of ontologies have been introduced by the biomedical community in recent years. Knowledge discovery for entity identification from ontology has become an important research area, and it is always interesting to discovery how associations are established to connect concepts in a single ontology or across multiple ontologies. However, due to the exponential growth of biomedical big data and their complicated associations, it becomes very challenging to detect key associations among entities in an inefficient dynamic manner. Therefore, there exists a gap between the increasing needs for association detection and large volume of biomedical ontologies. In this paper, to bridge this gap, we presented a knowledge discovery framework, the BioBroker, for grouping entities to facilitate the process of biomedical knowledge discovery in an intelligent way. Specifically, we developed an innovative knowledge discovery algorithm that combines a graph clustering method and an indexing technique to discovery knowledge patterns over a set of interlinked data sources in an efficient way. We have demonstrated capabilities of the BioBroker for query execution with a use case study on a subset of the Bio2RDF life science linked data.

Semantic-aware graph convolution network on multi-hop paths for link prediction

Knowledge graph(KG) link prediction aims to address the problem of missing multiple valid triples in KGs. Existing approaches either struggle to efficiently model the message passing process of multi-hop paths or lack transparency of model prediction principles. In this paper,a new graph convolutional network path semantic-aware graph convolution network(PSGCN) is proposed to achieve modeling the semantic information of multi-hop paths. PSGCN first uses a random walk strategy to obtain all-hop paths in KGs,then captures the semantics of the paths by Word2Sec and long shortterm memory(LSTM) models,and finally converts them into a potential representation for the graph convolution network(GCN) messaging process. PSGCN combines path-based inference methods and graph neural networks to achieve better interpretability and scalability. In addition,to ensure the robustness of the model,the value of the path thresholdKis experimented on the FB15K-237 and WN18RR datasets,and the final results prove the effectiveness of the model.

医学领域知识融合研究进展

医学领域知识融合旨在将分散在各个知识图谱或不同数据源中的医学知识进行整合,形成一个更全面的知识图谱,在提高知识质量、扩大规模、提高医学知识利用率和共享性等方面具有促进作用。围绕知识融合的问题和解决方案,首先系统地梳理了医学领域知识融合的定义、评价指标及数据集;分类讨论了知识融合过程中存在的问题与挑战;然后从问题、技术两个维度,综述了目前知识融合中实体对齐、实体链接任务各方法的优势与不足;详细讨论和总结了医学领域知识融合每一类问题的相关解决方案;最后,总结并展望了医学领域知识融合的发展方向。

面向保护和利用的大遗址本体与关联数据研究

大遗址与所在地的居民生活生产、城乡发展以及资源保护与利用等方面存在紧密关联,在我国快速的城镇化进程中,大遗址的保护和利用面临巨大压力。对大遗址知识的有效组织是大遗址保护和利用的前提。本体和关联数据技术为大遗址知识组织提供了新的方法及实现路径。文章首先结合文献调研,复用CIDOC-CRM和OWL-Time本体构建上位本体;然后通过构建顶层框架和抽取概念实例,获取大遗址知识概念和实例,并将其转化为大遗址本体的实体、对象属性和数据属性;最后基于关联数据技术,以西安片区大遗址为例,利用D2RQ发布大遗址知识的关联数据集。文章通过构建本体和发布关联数据实现了大遗址知识的语义关联和开放共享,为提升大遗址的保护和利用水平提供了数据支撑和知识服务。

k阶采样和图注意力网络的知识图谱表示模型

知识图谱表示(KGE)旨在将知识图谱中的实体和关系映射到低维度向量空间而获得其向量表示。现有的KGE模型只考虑一阶近邻,这影响了知识图谱中推理和预测任务的准确性。为了解决这一问题,提出了一种基于k阶采样算法和图注意力网络的KGE模型。k阶采样算法通过聚集剪枝子图中的k阶邻域来获取中心实体的邻居特征。引入图注意力网络来学习中心实体邻居的注意力值,通过邻居特征加权和得到新的实体向量表示。利用ConvKB作为解码器来分析三元组的全局表示特征。在WN18RR、FB15k-237、NELL-995、Kinship数据集上的评价实验表明,该模型在链接预测任务上的性能明显优于最新的模型。此外,还讨论了阶数k和采样系数b的改变对模型命中率的影响。

E-TUP:融合E-CP与TUP的联合知识图谱学习推荐方法

目前,大部分将知识图谱引入推荐系统的方法只是将已知的表层知识图谱实体进行引入,没有对图谱的内在关系进行预测和挖掘,因此无法利用知识图谱中的隐藏关系。针对上述问题,提出联合学习推荐模型E-TUP(enhance towards understanding of user preference),使用E-CP(enhance canonical polyadic)进行知识图谱补全并将完整信息进行传递。利用储存空间负采样方法,将优质负例三元组进行存储,并随训练过程进行更新,以提高知识图谱补全中负例三元组的质量。链接预测实验结果显示,储存空间方法使E-TUP模型链接预测准确率对比现有模型最高提升10.3%。在MovieLens-1m和DBbook2014数据集上进行推荐实验,在多个评价指标上取得最佳结果,对比现有模型实现最高5.5%的提升,表明E-TUP可以有效利用知识图谱中的隐藏关系提高模型推荐准确率。基于汽车维修数据进行推荐实验,结果表明E-TUP可以有效推荐相关知识。

融合关系层次结构的知识图谱嵌入

针对目前知识图谱嵌入方法大都侧重于三元组中的实体和关系信息,忽略了三元组之外与关系相关的丰富信息,提出一种融合关系层次结构信息的知识图谱嵌入方法CompGCN-RHS。在关系表示中融入关系的层次结构信息,将实体和关系联合进行嵌入学习,通过在聚合邻居节点信息时引入注意力机制来学习不同邻居节点对于中心节点的不同贡献。在数据集Sport上该方法的MRR、Hits@1分别提升2.2百分点和2.3百分点;在Location上分别提升了4.7百分点和6百分点,实验结果验证了该方法的有效性。

基于对比学习与语言模型增强嵌入的知识图谱补全

知识图谱是由各种知识或数据单元经过抽取等处理而组成的一种结构化知识库,用于描述和表示实体、概念、事实和关系等信息。自然语言处理技术的限制和各种知识或信息单元文本本身的噪声都会使信息抽取的准确性受到一定程度的影响。现有的知识图谱补全方法通常只考虑单一结构信息或者文本语义信息,忽略了整个知识图谱中同时存在的结构信息与文本语义信息。针对此问题,提出一种基于语言模型增强嵌入与对比学习的知识图谱补全(KGC)模型。将输入的实体和关系通过预训练语言模型获取实体和关系的文本语义信息,利用翻译模型的距离打分函数捕获知识图谱中的结构信息,使用2种用于对比学习的负采样方法融合对比学习来训练模型以提高模型对正负样本的表征能力。实验结果表明,与基于来自Transformеr的双向编码器表示的知识图谱补全(KG-BERT)模型相比,在WN18RR和FB15K-237数据集上该模型链接预测的排名小于等于10的三元组的平均占比(Hits@10)分别提升了31%和23%,明显优于对比模型。

基于图注意力网络的小样本知识图谱补全

提出了一种基于图注意力网络(graph attention network,GAT)的小样本知识图谱补全方法.该方法通过图注意力网络的注意力机制赋予邻居不同的权重,生成更强大的特征表示,通过匹配网络匹配查询集与参考集,选择相似性度量分数最高的候选实体作为补全后的尾实体.实验结果表明,图注意力网络模型对小样本知识图谱中缺失的链接能够进行有效的预测.

面向链接预测的知识图谱表示学习方法综述

作为人工智能的重要基石,知识图谱能够从互联网海量数据中抽取并表达先验知识,极大程度解决了智能系统认知决策可解释性差的瓶颈问题,对智能系统的构建与应用起关键作用.随着知识图谱技术应用的不断深化,旨在解决图谱欠完整性问题的知识图谱补全工作迫在眉睫.链接预测是针对知识图谱中缺失的实体与关系进行预测的任务,是知识图谱构建与补全中不可或缺的一环.要充分挖掘知识图谱中的隐藏关系,利用海量的实体与关系进行计算,就需要将符号化表示的信息转换为数值形式,即进行知识图谱表示学习.基于此,面向链接预测的知识图谱表示学习成为知识图谱领域的研究热点.从链接预测与表示学习的基本概念出发,系统性地介绍面向链接预测的知识图谱表示学习方法最新研究进展.具体从知识表示形式、算法建模方式两种维度对研究进展进行详细论述.以知识表示形式的发展历程为线索,分别介绍二元关系、多元关系和超关系知识表示形式下链接预测任务的数学建模.基于表示学习建模方式,将现有方法细化为4类模型:平移距离模型、张量分解模型、传统神经网络模型和图神经网络模型,并详细描述每类模型的实现方式与解决不同关系元数链接预测任务的代表模型.在介绍链接预测的常用的数据集与评判标准基础上,分别对比分析二元关系、多元关系和超关系3类知识表示形式下,4类知识表示学习模型的链接预测效果,并从模型优化、知识表示形式和问题作用域3个方面展望未来发展趋势.