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1.
《Information processing & management》2022,59(1):102790
Entity alignment is an important task for the Knowledge Graph (KG) completion, which aims to identify the same entities in different KGs. Most of previous works only utilize the relation structures of KGs, but ignore the heterogeneity of relations and attributes of KGs. However, these information can provide more feature information and improve the accuracy of entity alignment. In this paper, we propose a novel Multi-Heterogeneous Neighborhood-Aware model (MHNA) for KGs alignment. MHNA aggregates multi-heterogeneous information of aligned entities, including the entity name, relations, attributes and attribute values. An important contribution is to design a variant attention mechanism, which adds the feature information of relations and attributes to the calculation of attention coefficients. Extensive experiments on three well-known benchmark datasets show that MHNA significantly outperforms 12 state-of-the-art approaches, demonstrating that our approach has good scalability and superiority in both cross-language and monolingual KGs. An ablation study further supports the effectiveness of our variant attention mechanism. 相似文献
2.
《Information processing & management》2022,59(1):102753
The development of digital technology promotes the construction of the Intangible cultural heritage (ICH) database but the data is still unorganized and not linked well, which makes the public hard to master the overall knowledge of the ICH. An ICH knowledge graph (KG) can help the public to understand the ICH and facilitate the protection of the ICH. However, a general framework of ICH KG construction is lacking now. In this study, we take the Chinese ICH (nation-level) as an example and propose a framework to build a Chinese ICH KG combining multiple data sources from Baike and the official website, which can extend the scale of the KG. Besides, the data of ICH grows daily, requiring us to design an efficient model to extract the knowledge from the data to update the KG in time. The built KG is based on the triple 〈entity, attribute, attribute value〉 and we introduce the attribute value extraction (AVE) task. However, the public Chinese ICH annotated AVE corpus is lacking. To solve that, we construct a Chinese ICH AVE corpus based on the Distant Supervision (DS) automatically rather than employing traditional manual annotation. Currently, AVE is usually seen as the sequence tagging task. In this paper, we take the ICH AVE as a node classification task and propose an AVE model BGC, combining the BiLSTM and graph attention network, which can fuse and utilize the word-level and character-level information by means of the ICH lexicon generated from the KG. We conduct extensive experiments and compare the proposed model with other state-of-the-art models. Experimental results show that the proposed model is of superiority. 相似文献
3.
《Information processing & management》2023,60(5):103447
Learning a continuous dense low-dimensional representation of knowledge graphs (KGs), known as knowledge graph embedding (KGE), has been viewed as the key to intelligent reasoning for deep learning (DL) and gained much attention in recent years. To address the problem that the current KGE models are generally ineffective on small-scale sparse datasets, we propose a novel method RelaGraph to improve the representation of entities and relations in KGs by introducing neighborhood relations. RelaGraph extends the neighborhood information during entity encoding, and adds the neighborhood relations to mine deeper level of graph structure information, so as to make up for the shortage of information in the generated subgraph. This method can well represent KG components in a vector space in a way that captures the structure of the graph, avoiding underlearning or overfitting. KGE based on RelaGraph is evaluated on a small-scale sparse graph KMHEO, and the MRR reached 0.49, which is 34 percentage points higher than that of the SOTA methods, as well as it does on several other datasets. Additionally, the vectors learned by RelaGraph is used to introduce DL into several KG-related downstream tasks, which achieved excellent results, verifying the superiority of KGE-based methods. 相似文献
4.
《Journal of The Franklin Institute》2023,360(12):9180-9197
The distributed estimation has important research significance in unmanned systems. This paper investigates the distributed estimation of unmanned surface vessel (USV) via multi-sensor collaboration and 3D object recognition, in which a Knowledge Graph (KG) is constructed to store and represent the estimation results. Kalman-consensus Filter (KCF) and convolutional neural network (CNN) are used to estimate the optimal states of objects, and recognise multiple classes of objects without designing detectors for each class of objects, respectively. The recognition efficiency is improved by dividing the data into pixel blocks whose value is the number of detection points, and a point cloud dataset in different locations and rotations is also provided. Experiments are proposed to show that our method can help the USV accurately perceive entities in the environment, which validates the effectiveness of the proposed algorithm. 相似文献
5.
《Information processing & management》2019,56(3):445-462
Generally, QA systems suffer from the structural difference where a question is composed of unstructured data, while its answer is made up of structured data in a Knowledge Graph (KG). To bridge this gap, most approaches use lexicons to cover data that are represented differently. However, the existing lexicons merely deal with representations for entity and relation mentions rather than consulting the comprehensive meaning of the question. To resolve this, we design a novel predicate constraints lexicon which restricts subject and object types for a predicate. It facilitates a comprehensive validation of a subject, predicate and object simultaneously. In this paper, we propose Predicate Constraints based Question Answering (PCQA). Our method prunes inappropriate entity/relation matchings to reduce search space, thus leading to an improvement of accuracy. Unlike the existing QA systems, we do not use any templates but generates query graphs to cover diverse types of questions. In query graph generation, we put more focus on matching relations rather than linking entities. This is well-suited to the use of predicate constraints. Our experimental results prove the validity of our approach and demonstrate a reasonable performance compared to other methods which target WebQuestions and Free917 benchmarks. 相似文献
6.
《Information processing & management》2023,60(3):103283
Text-enhanced and implicit reasoning methods are proposed for answering questions over incomplete knowledge graph (KG), whereas prior studies either rely on external resources or lack necessary interpretability. This article desires to extend the line of reinforcement learning (RL) methods for better interpretability and dynamically augment original KG action space with additional actions. To this end, we propose a RL framework along with a dynamic completion mechanism, namely Dynamic Completion Reasoning Network (DCRN). DCRN consists of an action space completion module and a policy network. The action space completion module exploits three sub-modules (relation selector, relation pruner and tail entity predictor) to enrich options for decision making. The policy network calculates probability distribution over joint action space and selects promising next-step actions. Simultaneously, we employ the beam search-based action selection strategy to alleviate delayed and sparse rewards. Extensive experiments conducted on WebQSP, CWQ and MetaQA demonstrate the effectiveness of DCRN. Specifically, under 50% KG setting, the Hits@1 performance improvements of DCRN on MetaQA-1H and MetaQA-3H are 2.94% and 1.18% respectively. Moreover, under 30% and 10% KG settings, DCRN prevails over all baselines by 0.9% and 1.5% on WebQSP, indicating the robustness to sparse KGs. 相似文献
7.
《Information processing & management》2022,59(6):103076
Knowledge graph representation learning (KGRL) aims to infer the missing links between target entities based on existing triples. Graph neural networks (GNNs) have been introduced recently as one of the latest trendy architectures serves KGRL task using aggregations of neighborhood information. However, current GNN-based methods have fundamental limitations in both modelling the multi-hop distant neighbors and selecting relation-specific neighborhood information from vast neighbors. In this study, we propose a new relation-specific graph transformation network (RGTN) for the KGRL task. Specifically, the proposed RGTN is the first pioneer model that transforms a relation-based graph into a new path-based graph by generating useful paths that connect heterogeneous relations and multi-hop neighbors. Unlike the existing GNN-based methods, our approach is able to adaptively select the most useful paths for each specific relation and to effectively build path-based connections between unconnected distant entities. The transformed new graph structure opens a new way to model the arbitrary lengths of multi-hop neighbors which leads to more effective embedding learning. In order to verify the effectiveness of our proposed model, we conduct extensive experiments on three standard benchmark datasets, e.g., WN18RR, FB15k-237 and YAGO-10-DR. Experimental results show that the proposed RGTN achieves the promising results and even outperforms other state-of-the-art models on the KGRL task (e.g., compared to other state-of-the-art GNN-based methods, our model achieves 2.5% improvement using H@10 on WN18RR, 1.2% improvement using H@10 on FB15k-237 and 6% improvement using H@10 on YAGO3-10-DR). 相似文献
8.
[目的/意义] 从跨语言视角探究如何更好地解决低资源语言的实体抽取问题。[方法/过程] 以英语为源语言,西班牙语和荷兰语为目标语言,借助迁移学习和深度学习的思想,提出一种结合自学习和GRU-LSTM-CRF网络的无监督跨语言实体抽取方法。[结果/结论] 与有监督的跨语言实体抽取方法相比,本文提出的无监督跨语言实体抽取方法可以取得更好的效果,在西班牙语上,F1值为0.6419,在荷兰语上,F1值为0.6557。利用跨语言知识在源语言和目标语言间建立桥梁,提升低资源语言实体抽取的效果。 相似文献
9.
An approach for measuring semantic similarity between Wikipedia concepts using multiple inheritances
《Information processing & management》2020,57(3):102188
Wikipedia provides a huge collaboratively made semi-structured taxonomy called Wikipedia category graph (WCG), which can be utilized as a Knowledge Graph (KG) to measure the semantic similarity (SS) between Wikipedia concepts. Previously, several Most Informative Common Ancestor-based (MICA-based) SS methods have been proposed by intrinsically manipulating the taxonomic structure of WCG. However, some basic structural issues in WCG such as huge size, branching factor and multiple inheritance relations hamper the applicability of traditional MICA-based and multiple inheritance-based approaches in it. Therefore, in this paper, we propose a solution to handle these structural issues and present a new multiple inheritance-based SS approach, called Neighborhood Ancestor Semantic Contribution (NASC). In this approach, firstly, we define the neighborhood of a category (a taxonomic concept in WCG) to define its semantic space. Secondly, we describe the semantic value of a category by aggregating the intrinsic IC-based semantic contribution weights of its semantically relevant multiple ancestors. Thirdly, based on our approach, we propose six different methods to compute the SS between Wikipedia concepts. Finally, we evaluate our methods on gold standard word similarity benchmarks for English, German, Spanish and French languages. The experimental evaluation demonstrates that the proposed NASC-based methods remarkably outperform traditional MICA-based and multiple inheritance-based approaches. 相似文献
10.
《Information processing & management》2022,59(4):102980
As a part of innovation in forecasting, scientific topic hotness prediction plays an essential role in dynamic scientific topic assessment and domain knowledge transformation modeling. To improve the topic hotness prediction performance, we propose an innovative model to estimate the co-evolution of scientific topic and bibliographic entities, which leverages a novel dynamic Bibliographic Knowledge Graph (BKG). Then, one can predict the topic hotness by using various kinds of topological entity information, i.e., TopicRank, PaperRank, AuthorRank, and VenueRank, along with pre-trained node embedding, i.e., node2vec embedding, and different pooling techniques. To validate the proposed method, we constructed a new BKG by using 4.5 million PubMed Central publications plus MeSH (Medical Subject Heading) thesaurus and witnessed the essential prediction improvement with extensive experiment outcomes over 10 years observations. 相似文献
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12.
《Information processing & management》2022,59(4):102946
Text classification is an important research topic in natural language processing (NLP), and Graph Neural Networks (GNNs) have recently been applied in this task. However, in existing graph-based models, text graphs constructed by rules are not real graph data and introduce massive noise. More importantly, for fixed corpus-level graph structure, these models cannot sufficiently exploit the labeled and unlabeled information of nodes. Meanwhile, contrastive learning has been developed as an effective method in graph domain to fully utilize the information of nodes. Therefore, we propose a new graph-based model for text classification named CGA2TC, which introduces contrastive learning with an adaptive augmentation strategy into obtaining more robust node representation. First, we explore word co-occurrence and document word relationships to construct a text graph. Then, we design an adaptive augmentation strategy for the text graph with noise to generate two contrastive views that effectively solve the noise problem and preserve essential structure. Specifically, we design noise-based and centrality-based augmentation strategies on the topological structure of text graph to disturb the unimportant connections and thus highlight the relatively important edges. As for the labeled nodes, we take the nodes with same label as multiple positive samples and assign them to anchor node, while we employ consistency training on unlabeled nodes to constrain model predictions. Finally, to reduce the resource consumption of contrastive learning, we adopt a random sample method to select some nodes to calculate contrastive loss. The experimental results on several benchmark datasets can demonstrate the effectiveness of CGA2TC on the text classification task. 相似文献
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《Information processing & management》2023,60(2):103245
Natural language inference (NLI) is an increasingly important task of natural language processing, and the explainable NLI generates natural language explanations (NLEs) in addition to label prediction, to make NLI explainable and acceptable. However, NLEs generated by current models often present problems that disobey of commonsense or lack of informativeness. In this paper, we propose a knowledge enhanced explainable NLI framework (KxNLI) by leveraging Knowledge Graph (KG) to address these problems. The subgraphs from KG are constructed based on the concept set of the input sequence. Contextual embedding of input and the graph embedding of subgraphs, is used to guide the NLE generation by using a copy mechanism. Furthermore, the generated NLEs are used to augment the original data. Experimental results show that the performance of KxNLI can achieve state-of-the-art (SOTA) results on the SNLI dataset when the pretrained model is fine-tuned on the augmented data. Besides, the proposed mechanism of knowledge enhancement and rationales utilization can achieve ideal performance on vanilla seq2seq model, and obtain better transfer ability when transferred to the MultiNLI dataset. In order to comprehensively evaluate generated NLEs, we design two metrics from the perspectives of the accuracy and informativeness, to measure the quality of NLEs, respectively. The results show that KxNLI can provide high quality NLEs while making accurate prediction. 相似文献
15.
《Information processing & management》2022,59(4):102965
Structural Magnetic Resonance Image (sMRI) and functional MRI (fMRI) are two of the most important modalities to unveil brain disorders for Alzheimer's disease (AD) analysis. Comprehensively utilizing both modalities is the way to ensure an accurate AD diagnosis. Currently, the most common computational approach to aid the AD diagnosis is to formulate the sMRI and fMRI into graphs and then extract discriminative features through Graph Neural Networks (GNNs). However, most GNNs rely heavily on the aggregation operation on each node, which exploits the local topological information from the neighborhood nodes but does not fully respect the characteristics of the global graph topology. Also, only a few works addressed the structural and functional coupling problem on the graphs. In this paper, a novel Riemannian manifold-based model, called Cross-Modal Riemannian Network (CMRN), is proposed to solve the above issues, which respects the global topologies and invariant characteristics of the sMRI and fMRI graphs by fully operating on the Riemannian Manifold. Furthermore, a novel cross-modal attention mechanism is proposed to enable the interactions between two modalities on the Riemannian manifold, which helps the model comprehensively utilize both modalities to identify the most discriminative information for AD diagnosis. Extensive experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate the effectiveness of the proposed method. 相似文献
16.
《Journal of The Franklin Institute》2023,360(12):8178-8199
In large-scale complex dynamical networks, it is significant to estimate the states of target nodes with only a part of measured nodes. Meanwhile, multilayer complex dynamical networks exist widely in society and engineering. Therefore, it has important theoretic meaning and practical value to study the state estimation of target nodes in multilayer complex dynamical networks with limited node measurements. In this paper, with the measurable state information of a portion of nodes in one layer in the multilayer complex dynamical network, the state estimation of target nodes in other layers is studied. First, we build the model of the multilayer complex dynamical network which includes some target nodes and sensor nodes. Second, auxiliary nodes are selected by using the maximum matching principle in graph theory to construct the augmented node set. Third, we discuss the relationship between the minimum number of auxiliary nodes and interlayer connection probability in the multilayer complex dynamical network. Forth, an appropriate functional state observer is designed with limited number of measured nodes according to a typical model-based algorithm. Finally, numerical simulations are given to demonstrate the accuracy of the proposed method. The proposed method can achieve the accurate estimation with less placement of observers and fewer computational costs in the multilayer complex dynamical network. 相似文献
17.
《Information processing & management》2020,57(5):102277
Graph neural networks (GNNs) have shown great potential for personalized recommendation. At the core is to reorganize interaction data as a user-item bipartite graph and exploit high-order connectivity among user and item nodes to enrich their representations. While achieving great success, most existing works consider interaction graph based only on ID information, foregoing item contents from multiple modalities (e.g., visual, acoustic, and textual features of micro-video items). Distinguishing personal interests on different modalities at a granular level was not explored until recently proposed MMGCN (Wei et al., 2019). However, it simply employs GNNs on parallel interaction graphs and treats information propagated from all neighbors equally, failing to capture user preference adaptively. Hence, the obtained representations might preserve redundant, even noisy information, leading to non-robustness and suboptimal performance. In this work, we aim to investigate how to adopt GNNs on multimodal interaction graphs, to adaptively capture user preference on different modalities and offer in-depth analysis on why an item is suitable to a user. Towards this end, we propose a new Multimodal Graph Attention Network, short for MGAT, which disentangles personal interests at the granularity of modality. In particular, built upon multimodal interaction graphs, MGAT conducts information propagation within individual graphs, while leveraging the gated attention mechanism to identify varying importance scores of different modalities to user preference. As such, it is able to capture more complex interaction patterns hidden in user behaviors and provide a more accurate recommendation. Empirical results on two micro-video recommendation datasets, Tiktok and MovieLens, show that MGAT exhibits substantial improvements over the state-of-the-art baselines like NGCF (Wang, He, et al., 2019) and MMGCN (Wei et al., 2019). Further analysis on a case study illustrates how MGAT generates attentive information flow over multimodal interaction graphs. 相似文献
18.
《Information processing & management》2023,60(3):103322
Using AI technology to automatically match Q&A pairs on online health platforms (OHP) can improve the efficiency of doctor-patient interaction. However, previous methods often neglected to fully exploit rich information contained in OHP, especially the medical expertise that could be leveraged through medical text modeling. Therefore, this paper proposes a model named MKGA-DM-NN, which first uses the named entities of the medical knowledge graph (KG) to identify the intention of the problem, and then uses graph embedding technology to learn the representation of entities and entity relationships in the KG. The proposed model also employs the relationship between entities in KG to optimize the hybrid attention mechanism. In addition, doctors' historical Q&A records on OHP are used to learn modeling doctors’ expertise to improve the accuracy of Q&A matching. This method is helpful to bridge the semantic gap of text and improve the accuracy and interpretability of medical Q&A matching. Through experiments on a real dataset from a Chinese well-known OHP, our model has been verified to be superior to the baseline models. The accuracy of our model is 4.4% higher than the best baseline model. The cost-sensitive error of our model is 13.53% lower than that of the best baseline model. The ablation experiment shows that the accuracy rate can be significantly improved by 8.72% by adding the doctor modeling module, and the cost-sensitive error can be significantly reduced by 17.27% by adding the medical KG module. 相似文献
19.
《Information processing & management》2023,60(2):103174
The majority of currently available entity alignment (EA) solutions primarily rely on structural information to align entities, which is biased and disregards additional multi-source information. To compensate for inadequate structural details, this article suggests the SKEA framework, which is a simple but flexible framework for Entity Alignment with cross-modal supervision of Supporting Knowledge. We employ a relational aggregate network to specifically utilize the details about the entity and its neighbors. To overcome the limitations of relational features, two multi-modal encode modules are being used to extract visual and textural information. A new set of potential aligned entity pairs are generated by SKEA in each iteration using the knowledge of two reference modalities, which can enhance the model’s supervision. It is important to note that the supporting information used in our framework does not participate in the network’s backpropagation, which considerably improves efficiency and differs dramatically from earlier work. In comparison to existing baselines, experiments demonstrate that our proposed framework can incorporate multi-aspect information efficiently and enable supervisory signals from other modalities to transmit to entities. The maximum performance improvement of 5.24% indicates our suggested framework’s superiority, especially for sparse KGs. 相似文献
20.
[目的/意义]探索基于知识图谱的网络社区学术资源深度聚合的理论和方法,为网络学术社区知识细粒度组织、知识服务实践提供思路引导和新视角。[方法/过程]首先梳理了知识图谱和学术资源聚合的研究进展,从价值需求主体的角度剖析网络社区学术资源聚合的应用价值;然后明确网络社区学术知识图谱的构建流程,构建出基于知识图谱的网络社区学术资源深度聚合框架,并介绍知识富关联关系提取方法;最后设计个体用户画像、语义智能检索、分面式导航三种应用模式。[结果/结论]知识图谱能够较好地应用于学术资源深度聚合,支持网络社区的高级知识服务应用,基于知识图谱的网络社区学术资源深度聚合框架对学术类网络社区平台优化资源配置、有效知识创新服务具有重要参考价值。 相似文献