Large-scale Knowledge Research Articles

Using graph neural network to conduct supplier recommendation based on large-scale supply chain

Driven by economic globalisation, various industries have developed a trend towards high specialisation and vertical division of labor, resulting in vast and intricate supply chain networks. However, unforeseen disasters can cause supply chain disruptions, subsequently impacting the regular production and operations of both upstream and downstream enterprises. To tackle this challenge, this study utilises Graph Neural Networks (GNNs) to synthesise graph structural data within the supply chain network, aiming to identify alternative suppliers to mitigate the impact of disruptions. We construct a large-scale knowledge graph to represent the realistic automotive supply chain network in China. Additionally, we propose a GNN-based framework that utilises information about interactions between buyers and suppliers to recommend alternative suppliers from the knowledge graph. Experimental results show that our approach significantly outperforms state-of-the-art GNN-based models, including Light-GCN and NGCF. Our research provides an intelligent and efficient perspective on supplier selection for the Chinese automobile industry.

Advanced Query Functionalities in Natural Logic Knowledge Bases

Natural logics are formal logics characterized by expressions that bear a resemblance to simplified natural language sentences. The inherent readability of natural logics renders them suitable also for large knowledge bases. Within the realm of natural logics, the rules of logical proof are applicable directly to the sentences constructed using this framework. This direct application ensures explainability of query inferences. In the paper we describe a specific natural logic named NaturaLog, suited for ontology-structured knowledge bases. We outline how NaturaLog can be effectively implemented into a database system to facilitate deductive querying. The primary focus of this paper lies in exploring various query functionalities and elucidating methods to achieve these capabilities, also when dealing with large-scale knowledge bases.

Beyond Entities: A Large-Scale Multi-Modal Knowledge Graph with Triplet Fact Grounding

Much effort has been devoted to building multi-modal knowledge graphs by visualizing entities on images, but ignoring the multi-modal information of the relation between entities. Hence, in this paper, we aim to construct a new large-scale multi-modal knowledge graph with triplet facts grounded on images that reflect not only entities but also their relations. To achieve this purpose, we propose a novel pipeline method, including triplet fact filtering, image retrieving, entity-based image filtering, relation-based image filtering, and image clustering. In this way, a multi-modal knowledge graph named ImgFact is constructed, which contains 247,732 triplet facts and 3,730,805 images. In experiments, the manual and automatic evaluations prove the reliable quality of our ImgFact. We further use the obtained images to enhance model performance on two tasks. In particular, the model optimized by our ImgFact achieves an impressive 8.38% and 9.87% improvement over the solutions enhanced by an existing multi-modal knowledge graph and VisualChatGPT on F1 of relation classification. We release ImgFact and its instructions at https://github.com/kleinercubs/ImgFact.

Improving Open-Domain Dialogue Response Generation with Multi-Source Multilingual Commonsense Knowledge

Knowledge-grounded Dialogue Response Generation (KRG) can facilitate informative and fidelity dialogues using external knowledge. Prior monolingual works can only use the knowledge of the corresponding native language. Thus, due to the prohibitive costs of collecting and constructing external knowledge bases, the limited scale of accessible external knowledge always constrains the ability of KRG, especially in low-resource language scenarios. To this end, we propose a new task, Multi-Source Multilingual Knowledge-Grounded Response Generation (MMKRG), which simultaneously uses multiple knowledge sources of different languages. We notice that simply combining knowledge of different languages is inefficient due to the Cross-Conflict issue and Cross-Repetition issue. Thus, we propose a novel approach MMK-BART, which uses a simple but elegant Estimate-Cluster-Penalize mechanism to overcome the mentioned issues and adopts the multilingual language model mBART as the backbone. Meanwhile, based on the recent multilingual corpus XDailyDialog, we propose an MMKRG dataset MMK-DailyDialog, which has been aligned to the large-scale multilingual commonsense knowledge base ConceptNet and supports four languages (English, Chinese, German, and Italian). Extensive experiments have verified the effectiveness of our dataset and approach in monolingual, cross-lingual, and multilingual scenarios.

Video summarization via knowledge-aware multimodal deep networks

Video summarization has unprecedented importance in facilitating the rapid browsing, retrieval, and comprehension of large numbers of videos. Benefiting from possessing rich prior knowledge of the raw video and the capability to filter less crucial frames by employing multimodal information, humans can condense a lengthy video into a compact and reasonable video summary. However, existing automated video summarization approaches struggle to determine which shots in a video are significant concurrently and robustly, which is detrimental to the generation of high-quality summaries. To improve the quality of video summaries further, drawing inspiration from human abilities, we propose a novel video summarization approach based on a knowledge-aware multimodal network (KAMN). In particular, we present a knowledge-based encoder to obtain the corresponding representation for each frame. This representation is composed of captured descriptive content and affections, which are retrieved from large-scale external knowledge bases. Owing to these knowledge bases, rich implicit knowledge is provided to better understand the viewed video. Moreover, to integrate the visual, audio, and implicit knowledge features more effectively and to identify valuable information across different modalities further, we design a fusion module to learn these multimodal feature relationships more thoroughly. KAMN operates in both unsupervised and supervised training modes. Objective quantitative experiments and subjective user studies were conducted using four publicly available datasets. The results verified the effectiveness of the proposed modules and demonstrated the superior performance yielded by our framework.

High-Risk HPV Cervical Lesion Potential Correlations Mining over Large-Scale Knowledge Graphs

Lesion prediction, a very important aspect of cancer disease prediction, is an important marker for patients before they become cancerous. Currently, traditional machine learning methods are gradually applied in disease prediction based on patient vital signs data. Accurate prediction requires a large amount and high quality of data, however, the difficulty in obtaining and incompleteness of electronic medical record (EMR) data leads to certain difficulties in disease prediction by traditional machine learning methods. Secondly, there are many factors that contribute to the development of cervical lesions, some risk factors are directly related to it while others are indirectly related to them. In addition, risk factors have an interactive effect on the development of cervical lesions; it does not occur in isolation, a large-scale knowledge graph is constructed base on the close relationships among risk factors in the literature, and new potential key risk factors are mined based on common risk factors through a subgraph mining method. Then lesion prediction algorithm is proposed to predict the likelihood of lesions in patients base on the set of key risk factors. Experimental results show that the circumvents the problems of large number of missing values in EMR data and discovered key risk factors that are easily ignored but have better prediction effect. Therefore, The method had better accuracy in predicting cervical lesions.

Efficiency Improvisation of Large-Scale Knowledge Systems in Feature Determination using Proposed HVGAN Architecture

Knowledge Management (KM) is crucial for efficient information retrieval and forms the backbone of implementing an Information System (IS). Efficiently extracting meaningful trends from headlines aids in visualising demographic situations and addressing emergency handling needs. Understanding Information Resource Management (IRM) and Knowledge Resource Management (KRM) concepts benefits end users. This proposed KRM concept organises knowledge, ensuring global resource control through a neural decision framework. Traditional methods like Structural Equation Modelling (SEM) have higher computational steps, whereas neural network strategies in KM reduce steps and improve data prediction accuracy. The paucity of input data is a common challenge, which can be overcome by using Generative Adversarial Network (GAN) architecture for probabilistic generative processes. This research evaluates acquisition, spreading, vulnerability, and application using GAN-based knowledge systems that mitigate data scarcity through generative methods. The proposed Hierarchical Vanilla Generative Adversarial Network (HVGAN) utilises gradient functions and hierarchical computation algorithms to achieve desired knowledge extraction with lower time complexity and higher accuracy. Implementation improves efficiency in accessing stored knowledge, yielding an AUC score of 96% compared to other GAN architectures.

Knowledge-based dynamic prompt learning for multi-label disease diagnosis

Pretrained language models (PLMs) have been developed rapidly which establish impressive performance on many open-domain downstream tasks. However, conducting these pretrained models directly without additional network architectures on special domain tasks like multi-label disease diagnosis cannot perform well. Recently, prompt learning has been a new paradigm in PLM field which is more convenient and well-performed than the traditional fine-tuning approach for different domain tasks. However, prompt engineering is challenging because it takes time and experience. In this paper, we propose a new prompt learning method named Knowledge-based Dynamic PrompT (KBDPT) to deal with these problems. Firstly, we import medical knowledge into PLMs by prompt templates which make results of the disease diagnosis more reasonable and qualified. Compared with the fine-tuning approach, this method needs fewer trainable parameters and less training data but achieve better performance. Secondly, unlike most existing pre-defined prompt methods, KBDPT dynamically generates prompts based on personal medical information and a large-scale medical knowledge graph, which can provide more valuable guidance information for disease diagnosis. Lastly, the proposed model also ensembles multiple prompts from all possible diseases to introduce more knowledge and obtain differential diagnosis results. Experiments of multi-label disease diagnosis are conduct on three real-world EMR datasets. Results demonstrate that our model can be used in various pretrained models and outperform both classical deep learning methods and fine-tuning PLMs. The source code of our proposed model has been released at: https://github.com/loxs123/KBDPT.

A knowledge graph-based method for drug-drug interaction prediction with contrastive learning.

Precisely predicting Drug-Drug Interactions (DDIs) carries the potential to elevate the quality and safety of drug therapies, protecting the well-being of patients, and providing essential guidance and decision support at every stage of the drug development process. In recent years, leveraging large-scale biomedical knowledge graphs has improved DDI prediction performance. However, the feature extraction procedures in these methods are still rough. More refined features may further improve the quality of predictions. To overcome these limitations, we develop a knowledge graph-based method for multi-typed DDI prediction with contrastive learning (KG-CLDDI). In KG-CLDDI, we combine drug knowledge aggregation features from the knowledge graph with drug topological aggregation features from the DDI graph. Additionally, we build a contrastive learning module that uses horizontal reversal and dropout operations to produce high-quality embeddings for drug-drug pairs. The comparison results indicate that KG-CLDDI is superior to state-of-the-art models in both the transductive and inductive settings. Notably, for the inductive setting, KG-CLDDI outperforms the previous best method by 17.49% and 24.97% in terms of AUC and AUPR, respectively. Furthermore, we conduct the ablation analysis and case study to show the effectiveness of KG-CLDDI. These findings illustrate the potential significance of KG-CLDDI in advancing DDI research and its clinical applications. The codes of KG-CLDDI are available at https://github.com/jianzhong123/KG-CLDDI.

대규모 언어 모델과 지식 그래프 기반 도메인 특화 상담 챗봇 플랫폼

대규모 언어 모델과 지식 그래프 기반 도메인 특화 상담 챗봇 플랫폼

Knowledge Graph Construction and Representation Method for Potato Diseases and Pests

Potato diseases and pests have a serious impact on the quality and yield of potatoes, and timely prevention and control of potato diseases and pests is essential. A rich knowledge reserve of potato diseases and pests is one of the most important prevention and control measures; however, valuable knowledge is buried in the massive data of potato diseases and pests, making it difficult for potato growers and managers to obtain and use it in a timely manner and to develop the potential of knowledge. Therefore, this paper explores the construction method of a knowledge graph for automatic knowledge extraction, which extracts the knowledge of potato diseases and pests scattered in heterogeneous data from multiple sources, organises it into a semantically related knowledge base, and provides potato growers with professional knowledge and timely guidance to effectively prevent and control potato diseases and pests. In this paper, a data corpus on potato diseases and pests, called PotatoRE, is first constructed. Then, a model of ALBert-BiLSTM-Self_Att-CRF is designed to extract knowledge from the corpus to form a triplet structure, which is imported into the Neo4j graph database for storage and visualisation. Furthermore, the performance of the model constructed in this paper is compared and verified using the datasets PotatoRE and People’s Daily. The results show that compared to the SOTA models of ALBert BiLSTM-CRF and ALBert BiGRU-CRF, the accuracy of our model has been improved by 2.92% and 3.12%, respectively, using PotatoRE. Compared to the Bert BiLSTM-CRF model on two datasets, our model not only improves the accuracy, recall, and F1 values, but also has a higher efficiency. The model in this paper solves the problem of the difficult recognition of nested entities. On this basis, through comparative experiments, the TransH model is used to effectively represent the constructed knowledge graph, which lays the foundation for achieving inference, extension, and automatic updating of the knowledge base. The achievements of the thesis have made certain contributions to the automatic construction of large-scale knowledge bases.

LMKG: A large-scale and multi-source medical knowledge graph for intelligent medicine applications

Medical Knowledge Graph (KG) has shown great potential in various healthcare scenarios, such as drug recommendation and clinical decision support system. The factors that determine the role of a medical KG in practical applications are the scale, coverage, and quality of the medical knowledge it can provide. Most existing medical KGs are extracted from a single or a few information sources. However, medical knowledge extracted from insufficient information sources is usually highly incomplete or even biased, which results in a lack of data completeness and may lessen their effectiveness in real-world scenarios. Besides, the coverage of entity and relation types is inadequate in most previous works, which also might restrict their potential usage in future applications. In this paper, we build a unified system that can extract and manage medical knowledge from heterogeneous information sources. We first employ named entity recognition and relation extraction methods to extract knowledge triplets from medical texts. Then we propose a hierarchical entity alignment framework for further knowledge refinement. Based on our system, we construct a large-scale, high-quality, multi-source, and multi-lingual medical KG named LMKG, which includes 13 entity types and 17 relation types, and contains 403,784 entity and 1,225,097 relation instances. We conduct extensive experiments to evaluate the quality of LMKG. Experimental results show that LMKG can effectively enhance the performance of both upstream and downstream intelligent medicine applications. We have publicly released the KG resources and corresponding management service interface to facilitate research and applications in the medical field.

Hierarchical pattern-based complex query of temporal knowledge graph

The complex query is an essential type of knowledge graph query, which aims to deal with the complex relationships among multiple triples involved in query problems. However, the existing query methods often directly search the target entities in large knowledge graphs, resulting in unsatisfactory query results. In addition, most of the methods focus on static knowledge graphs, ignoring the critical semantic information contained in time elements in query questions. Therefore, this paper proposes a complex query model for temporal knowledge graphs based on the hierarchical pattern. Firstly, aiming at the multi-level semantic relations in complex query problems, we use the entity mapping method to generate the corresponding query graphs. This method can accurately express the semantic relations by learning the constraints, such as time and quantity, in the query problem by manual rules. In addition, to reduce the impact of noisy data on query results, a complex query algorithm based on a hierarchical pattern is proposed. By decomposing the large-scale temporal knowledge graph into smaller knowledge sub-graphs, the search range of target results is effectively reduced. Finally, extensive experiments demonstrate that our model has certain advantages in executing complex queries.

Entity Summarization via Exploiting Description Complementarity and Salience.

Entity summarization is a novel and efficient way to understand real-world facts and solve the increasing information overload problem in large-scale knowledge graphs (KG). Existing studies mainly rely on ranking independent entity descriptions as a list under a certain scoring standard such as importance. However, they often ignore the relatedness and even semantic overlap between individual descriptions. This may seriously interfere with the contribution judgment of descriptions for entity summarization. Actually, the entity summary is a whole to comprehensively integrate the main aspects of entity descriptions, which could be naturally treated as a set. Unfortunately, the exploration of these set characteristics for entity summarization is still an open issue with great challenges. To that end, we draw inspiration from a set completion perspective and propose an entity summarization method with complementarity and salience (ESCS) to deeply exploit description complementarity and salience in order to form a summary set for the target entity. Specifically, we first generate entity description representations with textual features in the description embedding module. For the purpose of learning complementary relationships within the entire summary set, we devise a bi-directional long short-term memory structure to capture global complementarity for each summary in the summary complementarity learning module. Meanwhile, in order to estimate the salience of individual descriptions, we calculate similarities between semantic embeddings of the target entity and its property-value pairs in the description salience learning module. Next, with a joint learning stage, we can optimize ESCS from a set completion perspective. Finally, a summary generation strategy is designed to infer the entire summary set step-by-step for the target entity. Extensive experiments on a public benchmark have clearly demonstrated the effectiveness of ESCS and revealed the potential of set completion in entity summarization task.

Knowledge Base Question Answering via Semantic Analysis

Knowledge Question Answering is one of the important research directions in the field of robot intelligence. It is mainly based on background knowledge to analyze users’ questions and generate answers. It is one of the important application methods of knowledge graph technology. Compared with the traditional expert system of question and answer, it has the advantage of a large-scale background knowledge base and the traceability and interpretability of the question-answering process. Compared with the current ChatGPT (Chat Generative Pre-trained Transformer) technology, it has advantages in the proprietary segmentation field. Aiming at the problem of the accuracy of existing knowledge question-answering methods being low, this paper studies the method of semantic analysis for knowledge question-answering under the support of a knowledge database, proposes a knowledge question-answering method based on the superposition of multiple neural network models, and conducts experimental verification on the publicly available NLPCC2016KBQA(Knowledge Q&A Tasks in the 2016 Natural Language Processing and Chinese Computing Conference) data set. The experimental results show that the F1 value of this method is higher than that of the baseline model.

HPRE: Leveraging hierarchy-aware paired relation vectors for knowledge graph embedding

Knowledge graphs exhibit a typical hierarchical structure and find extensive applications in various artificial intelligence domains. However, large-scale knowledge graphs need to be completed, which limits the performance of knowledge graphs in downstream tasks. Knowledge graph embedding methods have emerged as a primary solution to enhance knowledge graph completeness. These methods aim to represent entities and relations as low-dimensional vectors, focusing on handling relation patterns and multi-relation types. Researchers need to pay more attention to the crucial feature of hierarchical relationships in real-world knowledge graphs. We propose a novel knowledge graph embedding model called Hierarchy-Aware Paired Relation Vectors Knowledge Graph Embedding (HPRE) to bridge this gap. By leveraging the power of 2D coordinates, HPRE adeptly model relation patterns, multi-relation types, and hierarchical features in the knowledge graph. Specifically, HPRE employs paired relation vectors to capture the distinct characteristics of head and tail entities, facilitating a better fit for relational patterns and multi-relation scenarios. Additionally, HPRE employs angular coordinates to differentiate entities at various levels of the hierarchy, effectively representing the hierarchical nature of the knowledge graph. The experimental results show that the HPRE model can effectively learn the hierarchical features of the knowledge graph and achieve state-of-the-art experimental results on multiple real-world datasets for the link prediction task.

Sweet Apple, company? or food? Adjective-centric commonsense knowledge acquisition with taxonomy-guided induction

How to enable intelligent machines to possess human commonsense knowledge is one of the central concerns of artificial intelligence. Consequently, a series of commonsense knowledge bases have been designed and constructed by scholars. However, as an important kind of commonsense knowledge, adjective-centric commonsense knowledge (a crucial type of adjective-concept pair such as (blue, sky), (eatable, food)) is far from satisfactory due to the lack of existing knowledge bases and limited acquisition methods. In this paper, we concentrate on automatically constructing large-scale adjective-centric commonsense knowledge bases and propose an effective framework to achieve the goal. The framework mainly contains a filtering module to remove unreasonable inputs, a clustering module, and a conceptualization module to obtain adjective-concept pairs and an evaluation module to assess their plausibility. Extensive automatic and human evaluation results demonstrate the effectiveness of our method, and we finally harvest over 200 k adjective-centric commonsense knowledge, where 81.56% of the implicit commonsense knowledge is not covered by WebChild.11WebChild is a large-scale commonsense knowledge base, which contains triples that connect nouns with adjectives via fine-grained relations. Moreover, we also show that our mined adjective-centric commonsense knowledge can benefit the downstream query conceptualization task.

OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge Graphs

Different knowledge graphs for the same domain are often uniquely housed on the Web. Effectively linking entities from different graphs is critical for building an open and comprehensive knowledge graph. However, linking entities across different sources has thus far faced various challenges, including the increasingly large-scale volume of the data, the heterogeneity of the graphs, and the ambiguity of real-world entities. To address them, we propose a unified framework LinKG. Specifically, we decouple the problem into different linking tasks based on the unique properties of each type of entity. To link word sequence based entities, we propose an LSTM-based method to capture word dependencies. To link entities of large scale, we utilize the hashing technique and convolutional neural networks for scalable and accurate linking. To link ambiguous entities, we propose heterogeneous graph attention networks to leverage heterogeneous structural information. Finally, to validate the design choices of different LinKG modules, we characterize the relationships between different tasks based on the single-domain and multi-domain transfer models. Extensive experiments demonstrate the effectiveness of LinKG with an overall F1-score of 95.15%, based on which we deploy and release the Open Academic Graph (OAG)—the largest publicly available heterogeneous academic graph to date.

Relation extraction in Chinese using attention-based bidirectional long short-term memory networks.

Relation extraction is an important topic in information extraction, as it is used to create large-scale knowledge graphs for a variety of downstream applications. Its goal is to find and extract semantic links between entity pairs in natural language sentences. Deep learning has substantially advanced neural relation extraction, allowing for the autonomous learning of semantic features. We offer an effective Chinese relation extraction model that uses bidirectional LSTM (Bi-LSTM) and an attention mechanism to extract crucial semantic information from phrases without relying on domain knowledge from lexical resources or language systems in this study. The attention mechanism included into the Bi-LSTM network allows for automatic focus on key words. Two benchmark datasets were used to create and test our models: Chinese SanWen and FinRE. The experimental results show that the SanWen dataset model outperforms the FinRE dataset model, with area under the receiver operating characteristic curve values of 0.70 and 0.50, respectively. The models trained on the SanWen and FinRE datasets achieve values of 0.44 and 0.19, respectively, for the area under the precision-recall curve. In addition, the results of repeated modeling experiments indicated that our proposed method was robust and reproducible.

Exploration on the Development Path of Journalism and Communication Education in China in the Context of ChatGPT

After entering 2023, the cultivation of high-level talents in news communication has once again become a topic of great concern to the education circles around the world. The further development and update of artificial intelligence technologies such as ChatGPT have impacted the social cognitive system. Compared with other industries, the news communication industry has the closest business relationship with ChatGPT, and the news communication industry plays a major role in the production, circulation and service of large-scale knowledge content. Therefore, this paper discusses the path exploration of the development of journalism and communication education in the context of ChatGPT from the perspectives of digital development model, project-based teaching scenario and "publicity" in journalism and communication education.