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 ...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.展开更多
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 train...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.展开更多
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...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.展开更多
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 ...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.展开更多
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 lin...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.展开更多
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 meani...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.展开更多
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 ...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.展开更多
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 intere...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.展开更多
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...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.展开更多
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 ...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。展开更多
基金supported in part by the Beijing Natural Science Foundation under Grants L211020 and M21032in part by the National Natural Science Foundation of China under Grants U1836106 and 62271045in part by the Scientific and Technological Innovation Foundation of Foshan under Grants BK21BF001 and BK20BF010。
文摘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.
基金the Beijing Municipal Science and Technology Program(Z231100001323004)。
文摘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.
基金supported in part by the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-006.
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.:70973093)the Fundamental Research Funds for the Central Universities(Grant No.:201110401020006)
文摘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.
基金supported by the Key R&D Program Project of Zhejiang Province under Grant no.2019 C01004 and 2021C02004.
文摘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.
文摘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.
文摘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.
基金Supported by the National Natural Science Foundation of China(No.61876144).
文摘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.
文摘目前,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。