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.

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

Hyperbolic hierarchical graph attention network for knowledge graph completion

Utilizing graph neural networks for knowledge embedding to accomplish the task of knowledge graph completion(KGC)has become an important research area in knowledge graph completion.However,the number of nodes in the knowledge graph increases exponentially with the depth of the tree,whereas the distances of nodes in Euclidean space are second-order polynomial distances,whereby knowledge embedding using graph neural networks in Euclidean space will not represent the distances between nodes well.This paper introduces a novel approach called hyperbolic hierarchical graph attention network(H2GAT)to rectify this limitation.Firstly,the paper conducts knowledge representation in the hyperbolic space,effectively mitigating the issue of exponential growth of nodes with tree depth and consequent information loss.Secondly,it introduces a hierarchical graph atten-tion mechanism specifically designed for the hyperbolic space,allowing for enhanced capture of the network structure inherent in the knowledge graph.Finally,the efficacy of the proposed H2GAT model is evaluated on benchmark datasets,namely WN18RR and FB15K-237,thereby validating its effectiveness.The H2GAT model achieved 0.445,0.515,and 0.586 in the Hits@1,Hits@3 and Hits@10 metrics respectively on the WN18RR dataset and 0.243,0.367 and 0.518 on the FB15K-237 dataset.By incorporating hyperbolic space embedding and hierarchical graph attention,the H2GAT model successfully addresses the limitations of existing hyperbolic knowledge embedding models,exhibiting its competence in knowledge graph completion tasks.

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.

Extrapolation over temporal knowledge graph via hyperbolic embedding

Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(events)that happened at different timestamps have different influences on future events,which can be attributed to a hierarchy among not only facts but also relevant entities.Therefore,it is crucial to pay more attention to important entities and events when forecasting the future.However,most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts,which may be affected by the diversity and variability of the evolution,and they might fail to attach importance to facts that matter.Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data,which is considered to be a solution for modelling hierarchical relations among facts.To this end,we propose ReTIN,a novel model integrating real-time influence of historical facts for TKG reasoning based on hyperbolic geometry,which provides low-dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG.Considering both real-time and global features of TKG boosts the adaptation of ReTIN to the ever-changing dynamics and inherent constraints.Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines.The ablation study further supports the value of exploiting temporal information.

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.

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.

RotatS:temporal knowledge graph completion based on rotation and scaling in 3D space

As the research of knowledge graph(KG)is deepened and widely used,knowledge graph com-pletion(KGC)has attracted more and more attentions from researchers,especially in scenarios of in-telligent search,social networks and deep question and answer(Q&A).Current research mainly fo-cuses on the completion of static knowledge graphs,and the temporal information in temporal knowl-edge graphs(TKGs)is ignored.However,the temporal information is definitely very helpful for the completion.Note that existing researches on temporal knowledge graph completion are difficult to process temporal information and to integrate entities,relations and time well.In this work,a rotation and scaling(RotatS)model is proposed,which learns rotation and scaling transformations from head entity embedding to tail entity embedding in 3D spaces to capture the information of time and rela-tions in the temporal knowledge graph.The performance of the proposed RotatS model have been evaluated by comparison with several baselines under similar experimental conditions and space com-plexity on four typical knowl good graph completion datasets publicly available online.The study shows that RotatS can achieve good results in terms of prediction accuracy.

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 Hit@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 Hit@10 are increased by 0.8%and 1.8%,respectively.

Knowledge Graph Embedding Based on Adaptive Negative Sampling

Knowledge graph embedding aims at embedding entities and relations in a knowledge graph into a continuous, dense, low-dimensional and realvalued vector space. Among various embedding models appeared in recent years, translation-based models such as TransE, TransH and TransR achieve state-of-the-art performance. However, in these models, negative triples used for training phase are generated by replacing each positive entity in positive triples with negative entities from the entity set with the same probability;as a result, a large number of invalid negative triples will be generated and used in the training process. In this paper, a method named adaptive negative sampling (ANS) is proposed to generate valid negative triples. In this method, it first divided all the entities into a number of groups which consist of similar entities by some clustering algorithms such as K-Means. Then, corresponding to each positive triple, the head entity was replaced by a negative entity from the cluster in which the head entity was located and the tail entity was replaced in a similar approach. As a result, it generated a set of high-quality negative triples which benefit for improving the effectiveness of embedding models. The ANS method was combined with the TransE model and the resulted model was named as TransE-ANS. Experimental results show that TransE-ANS achieves significant improvement in the link prediction task.

Uncertain knowledge graph embedding:an effective method combining multi-relation and multi-path

Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this problem.However,there are still unresolved issues.On the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information loss.On the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be addressed.To address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this paper.First,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is introduced.Second,the asymmetry in the UKG is defined.To embed asymmetric relation facts of UKG,a multi-relation embedding model is proposed.Finally,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE.The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.

Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph

The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to theuser based on the known historical interaction data of the target user. Furthermore,the combination of the recommended algorithm based on collaborative filtrationand other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model(CoFM) is one representative research. CoFM, a fusion recommendation modelcombining the collaborative filtering model FM and the graph embeddingmodel TransE, introduces the information of many entities and their relationsin the knowledge graph into the recommendation system as effective auxiliaryinformation. It can effectively improve the accuracy of recommendations andalleviate the problem of sparse user historical interaction data. Unfortunately,the graph-embedded model TransE used in the CoFM model cannot solve the1-N, N-1, and N-N problems well. To tackle this problem, a novel fusion recommendation model Joint Factorization Machines and TransH Model (JFMH) isproposed, which improves CoFM by replacing the TransE model with TransHmodel. A large number of experiments on two widely used benchmark data setsshow that compared with CoFM, JFMH has improved performance in terms ofitem recommendation and knowledge graph completion, and is more competitivethan multiple baseline methods.

Construction of well logging knowledge graph and intelligent identification method of hydrocarbon-bearing formation

Based on the well logging knowledge graph of hydrocarbon-bearing formation(HBF),a Knowledge-Powered Neural Network Formation Evaluation model(KPNFE)has been proposed.It has the following functions:(1)extracting characteristic parameters describing HBF in multiple dimensions and multiple scales;(2)showing the characteristic parameter-related entities,relationships,and attributes as vectors via graph embedding technique;(3)intelligently identifying HBF;(4)seamlessly integrating expertise into the intelligent computing to establish the assessment system and ranking algorithm for potential pay recommendation.Taking 547 wells encountered the low porosity and low permeability Chang 6 Member of Triassic in the Jiyuan Block of Ordos Basin,NW China as objects,80%of the wells were randomly selected as the training dataset and the remainder as the validation dataset.The KPNFE prediction results on the validation dataset had a coincidence rate of 94.43%with the expert interpretation results and a coincidence rate of 84.38%for all the oil testing layers,which is 13 percentage points higher in accuracy and over 100 times faster than the primary conventional interpretation.In addition,a number of potential pays likely to produce industrial oil were recommended.The KPNFE model effectively inherits,carries forward and improves the expert knowledge,nicely solving the robustness problem in HBF identification.The KPNFE,with good interpretability and high accuracy of computation results,is a powerful technical means for efficient and high-quality well logging re-evaluation of old wells in mature oilfields.

QA-KGNet:一种语言模型驱动的知识图谱问答模型

基于知识图谱的问答系统可以解析用户问题,已成为一种检索知识、自动回答所询问题的有效途径.知识图谱问答系统通常是利用神经程序归纳模型,将自然语言问题转化为逻辑形式,在知识图谱上执行该逻辑形式能够得到答案.然而,使用预训练语言模型和知识图谱的知识问答系统包含两个挑战:(1)给定问答(questionanswering, QA)上下文,需要从大型知识图谱(knowledge graph, KG)中识别相关知识;(2)对QA上下文和KG进行联合推理.基于此,提出一种语言模型驱动的知识图谱问答推理模型QA-KGNet,将QA上下文和KG连接起来形成一个工作图,使用语言模型计算给定QA上下文节点与KG节点的关联度,并使用多头图注意力网络更新节点表示.在Commonsense QA、OpenBookQA和Med QA-USMLE真实数据集上进行实验来评估QA-KGNet的性能,实验结果表明:QA-KGNet优于现有的基准模型,表现出优越的结构化推理能力.

NGDcrm:a numeric graph dependency-based conflict resolution method for knowledge graph

Knowledge graph(KG)conflict resolution is to solve knowledge conflicts problem in the construction of KG.Aiming at the problem of KG conflict resolution,a KG conflict resolution algorithm NGDcrm is proposed,which is a numeric graph dependency-based conflict resolution method.NGDcrm utilizes the dependency graph to perform arithmetic calculation and predicate comparison of numerical entity knowledge in the KG.NGDcrm first uses a parallel segmentation method to segment the KG;then,it extracts the features of the KG according to KG embedding;finally,it uses numerical graph dependencies to detect and correct the wrong facts in the KG based on the extracted features.The experimental results on real data show that NGDcrm is better than the state-of-the-art knowledge conflict resolution method.Among them,the AUC value of NGDcrm on the DBpedia dataset is 15.4%higher than the state-of-the-art method.

Research on knowledge reasoning of TCM based on knowledge graphs

With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing exponentially.Consequently,there is much attention on the collection of useful knowledge as well as its effective organization and expression.Knowledge graphs have thus emerged,and knowledge reasoning based on this tool has become one of the hot spots of research.This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning,and explores the significance of knowledge reasoning.Secondly,the mainstream knowledge reasoning methods,including knowledge reasoning based on traditional rules,knowledge reasoning based on distributed feature representation,and knowledge reasoning based on neural networks are introduced.Then,using stroke as an example,the knowledge reasoning methods are expounded,the principles and characteristics of commonly used knowledge reasoning methods are summarized,and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out.Finally,we summarize the problems faced in the development of knowledge reasoning in TCM,and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.

Deep Knowledge Tracing Embedding Neural Network for Individualized Learning

Knowledge tracing is the key component in online individualized learning,which is capable of assessing the users'mastery of skills and predicting the probability that the users can solve specific problems.Available knowledge tracing models have the problem that the assessments are not directly used in the predictions.To make full use of the assessments during predictions,a novel model,named deep knowledge tracing embedding neural network(DKTENN),is proposed in this work.DKTENN is a synthesis of deep knowledge tracing(DKT)and knowledge graph embedding(KGE).DKT utilizes sophisticated long short-term memory(LSTM)to assess the users and track the mastery of skills according to the users'interaction sequences with skill-level tags,and KGE is applied to predict the probability on the basis of both the embedded problems and DKT's assessments.DKTENN outperforms performance factors analysis and the other knowledge tracing models based on deep learning in the experiments.

Joint learning based on multi-shaped filters for knowledge graph completion

To solve the problem of missing many valid triples in knowledge graphs(KGs),a novel model based on a convolutional neural network(CNN)called ConvKG is proposed,which employs a joint learning strategy for knowledge graph completion(KGC).Related research work has shown the superiority of convolutional neural networks(CNNs)in extracting semantic features of triple embeddings.However,these researches use only one single-shaped filter and fail to extract semantic features of different granularity.To solve this problem,ConvKG exploits multi-shaped filters to co-convolute on the triple embeddings,joint learning semantic features of different granularity.Different shaped filters cover different sizes on the triple embeddings and capture pairwise interactions of different granularity among triple elements.Experimental results confirm the strength of joint learning,and compared with state-of-the-art CNN-based KGC models,ConvKG achieves the better mean rank(MR)and Hits@10 metrics on dataset WN18 RR,and the better MR on dataset FB15k-237.