Application Of Knowledge Graph Research Articles

Application of Knowledge Graph in Water Conservancy Education Resource Organization under the Background of Big Data

The key to improving the readability and usage of educational resources is their orderly arrangement and integration. Knowledge graphs, which are a large-scale form of knowledge engineering, are an effective tool for managing and organizing educational resources. The water conservancy’s educational big data is separated into three tiers of objectives–courses–knowledge units based on the connotation level of self-directed learning. Combined with the idea of Outcome-based Education(OBE), the goal-oriented knowledge graph structure of water conservancy disciplines and graph creation method is proposed. The focus is the error accumulation problem brought about by the traditional relational extraction method of Named Entity Recognition based on rules or sequence labeling. We first complete this objective, and then the relationship classification is performed according to the water conservancy disciplines entity and relations joint extraction (WDERJE) model, on which the prompt mechanism design is based. Think of the entity-relationship extraction task as a sequence-to-sequence generation task, and take the structured extraction language to unify the coding entity extraction and relationship extraction structures. The evaluation results of the WDERJE model show that the F_0.5 value of each entity extraction is above 0.76, and the cumulative extraction relationship triple is nearly 180,000. The graph fully optimizes the organization and management of water conservancy education resources and effectively improves the readability and utilization rate of water conservancy teaching resources.

PANC: Prototype Augmented Neighbor Constraint instance completion in knowledge graphs

Great attention has been devoted to knowledge graph completion techniques with the wide application of knowledge graph. Previous works on knowledge graph completion mainly consider predicting missing one element for given two elements in a triplet, such as (h, r, ?). However, this task lacks of reasonable consideration between entities and relations, such as (Italy, acted in, ?), which may cause the meaningless predictions. Recent attempt solves this problem by redefining a new task-instance completion, which generates and evaluates reasonable relation-tail pairs for given head entity, such as (h, ?, ?). In this work, we propose a novel Prototype Augmented Neighbor Constraint instance completion model called PANC, which consists of two modules-prototype filter and neighbor aggregation grader. A kind of coarse-grained information prototype is utilized in filters to generate more candidate relation-tail pairs and neighbor aggregation is introduced into grader so as to enhance entity embedding and constrain the combination between head entity and candidate pairs. The experiments show that our PANC outperforms the state-of-the-art instance completion techniques on two real-world datasets FB15k and JF17k. And the ablation results verify the effectiveness of the modules in our proposed PANC.

Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications

Knowledge graph has become a cutting-edge technology for linking and integrating heterogeneous, cross-domain datasets to address critical scientific questions. As big data has become prevalent in today's scientific analysis, semantic data repositories that can store and manage large knowledge graph data have become critical in successfully deploying spatially explicit knowledge graph applications. This paper provides a comprehensive evaluation of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. There are three types of semantic data repositories: (1) triple store solutions (RDF4j, Fuseki, GraphDB, Virtuoso), (2) property graph databases (Neo4j), and (3) an Ontology-Based Data Access (OBDA) approach (Ontop). Experiments were conducted to compare each repository's efficiency (e.g., query response time) in handling geometric, topological, and spatial-semantic related queries. The results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries. The OBDA solution, Ontop, has the second-best query performance in spatial and complex queries and the best storage efficiency, requiring the least data-to-RDF conversion efforts. Other triple store solutions suffer from various issues that cause performance bottlenecks in handling spatial queries, such as inefficient memory management and lack of proper query optimization.

Application of knowledge graph in power system fault diagnosis and disposal: A critical review and perspectives

Knowledge graph (KG) has good knowledge expression ability and interpretation, and its application to power system fault diagnosis and disposal can effectively integrate data of the whole life cycle of equipment and form a novel knowledge-driven operation and maintenance management mode. This is crucial to assist dispatchers in fault disposal and effectively improve the power system emergency handling capability and dispatch intelligence level. This paper conducts a systematic review and summary of the application of KG in power system fault diagnosis and disposal, so as to provide an adequate and comprehensive guide for further research in this field. Firstly, the definition, status, and classification of KGs are systematically described, and the general process of KG construction is sorted out. Secondly, the basic framework, construction process, key technologies, and typical practices of KG for power system fault diagnosis and disposal are summarized and reviewed in detail. Ultimately, several challenges, opportunities, and perspectives of KG in this field are carefully presented.

Research on application of knowledge graph for aircraft maintenance

Aircraft maintenance technology is related to flight safety. It is not only a significant guarantee for the safety of passengers’ lives and property but also an important means for airlines to reduce costs and improve profitability. This paper focuses on aircraft maintenance decision-making, analyzes the current situation and defects of maintenance decision-making, applies knowledge graph technology to this field, and constructs aircraft maintenance knowledge graph; through the character analysis of sample data and experimental comparison, the key factors affecting the accuracy of aircraft maintenance decisions are found; few-shot knowledge reasoning technology is introduced to improve the maintenance strategy reasoning algorithm. In the 737NG aircraft maintenance decision-making system of an airline, the feasibility and effectiveness of the method are verified, which provides strong support for flight safety.

Research Review of the Knowledge Graph and its Application in Power System Dispatching and Operation

With the construction of a new power system and the proposal of a double carbon goal, power system operation data are growing explosively, and the optimization of power system dispatching operation is becoming more and more complex. Relying on traditional pure manual dispatching is difficult to meet the dispatching needs. The emerging knowledge graph technology in the field of the artificial intelligence technology is one of the effective methods to solve this problem. Because the topological structure of the power system itself is consistent with the relational structure of graph theory, through the establishment of a relevant knowledge graph, the real operating state of the power system can be restored to the maximum extent by effectively preserving the correlation implicit in the data. Meanwhile, expressing the hidden knowledge in the power system dispatching operation in the form of a knowledge graph has become the focus of research at home and abroad. This study summarizes the development of the knowledge graph technology from the aspects of knowledge extraction, knowledge representation learning, knowledge mining, knowledge reasoning, knowledge fusion, and the application of knowledge graph and introduces the application and prospect of knowledge graph in the power system dispatching operation from the aspects of the auxiliary optimization decision, vertical risk control, operation mode analysis, optimization model improvement experience, and super regulation parameters.

Neurally-Guided Semantic Navigation in Knowledge Graph

In this big data era, knowledge becomes increasingly linked, along with the rapid growth in data volume. Connected knowledge is naturally represented and stored as knowledge graphs, which are of more and more importance for many frontier research areas such as machine intelligence. Effectively finding relations between entities in a large knowledge graph plays a key role in many knowledge graph applications, as the most valuable part of a knowledge graph is its rich connectedness, which captures rich information about the objects in the real world. However, due to the intrinsic complexity of real-world knowledge, finding semantically close relations by navigation in a large knowledge graph is very challenging. Canonical graph exploration methods inevitably result in combinatorial explosion especially when the paths connecting two entities are long: the search space is <inline-formula><tex-math notation="LaTeX">$O(d^l)$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> is the average graph node degree and <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula> is the length of the path. In this paper, we will systematically study the semantic navigation problem for large knowledge graphs. Inspired by AlphaGo, which was overwhelmingly successful in the game Go, we designed an efficient semantic navigation method based on a well-tailored Monte Carlo Tree Search algorithm with the unique characteristics of knowledge graphs considered. Extensive experiments on different real-life knowledge bases show that our method is not only effective but also very efficient.

Development and application of a field knowledge graph and search engine for pavement engineering

Integrated, timely data about pavement structures, materials and performance information are crucial for the continuous improvement and optimization of pavement design by the engineering research community. However, at present, pavement structures, materials and performance information in China are relatively isolated and cannot be integrated and managed. This results in a waste of a large amount of effective information. One of the significant development trends of pavement engineering is to collect, analyze, and manage the knowledge assets of pavement information to realize intelligent decision-making. To address these challenges, a knowledge graph (KG) is adopted, which is a novel and effective knowledge management technology and provides an ideal technical method to realize the integration of information in pavement engineering. First, a neural network model is used based on the principle of deep learning to obtain knowledge. On this basis, the relationship between knowledge is built from siloed databases, data in textual format and networks, and the knowledge base. Second, KG-Pavement is presented, which is a flexible framework that can integrate and ingest heterogeneous pavement engineering data to generate knowledge graphs. Furthermore, the index and unique constraints on attributes for knowledge entities are proposed in KG-Pavement, which can improve the efficiency of internal retrieval in the system. Finally, a pavement information search engine based on a knowledge graph is constructed to realize information interaction and target information matching between a webpage server and graph database. This is the first successful application of knowledge graphs in pavement engineering. This will greatly promote knowledge integration and intelligent decision-making in the domain of pavement engineering.

A Study on the Connotation Remodeling of Science and Technology Vocabulary Based on the Application of Knowledge Graphs

Disruptive Technology is also often translated as disruptive technology or breakthrough technology, which was first proposed by Professor Christensen of Harvard Business School in 1990s. Nowadays, the competition among countries is increasingly fierce, especially in the field of science and technology, which often leads to the backwardness and impoverishment of the whole industry due to the technological gap. Today, with the rapid consumption of social resources, social and economic development urgently needs disruptive technologies to promote industrial transformation, optimize economic structure and improve people’s quality of life. Countries that master disruptive technologies can open up new industrial development paths and lay a foundation for unique technological advantages, thus bringing huge technological dividends to national economic development. Visuwords is a knowledge graph application platform, through which the meaning and semantic association of words can be visualized. This study takes the word “famished” as an example to analyze the relationship between its new connotation and original meaning to verify its feasibility at the communication level. Today’s social science knowledge in the general public needs to be disseminated from the original relatively obscure and academic to an easily acceptable form. This study applies the idea of scientific popularization to a knowledge graph visualization tool, while using the connotation of linguistic ideas to analyze and deconstruct specific wordsThe study combines the connotation concept of linguistics with the disciplinary tools in the field of knowledge graph, and specifically analyzes the role of scientific terms, especially some abbreviations, in the mass science communication.

Construction and application of knowledge graph for fault diagnosis of turbine generator set based on ontology

Abstract Aiming at the problems of complex structure and multi-source heterogeneity, imperfect knowledge representation, single knowledge extraction method, and difficulty of sharing and reuse in the knowledge field of turbine generator set fault diagnosis. The construction of knowledge graph is studied from multiple dimensions such as experts, fault characteristics, diagnosis techniques, research results, and solutions in the field for fault diagnosis knowledge of turbine generator set. And an ontology model of fault diagnosis knowledge for the turbine generator set is constructed. The entities, attributes, and relationships of the fault diagnosis knowledge graph for the turbine generator set are represented based on the model. The knowledge graph data are stored by the Neo4j graph database. The problems caused by multi-source and heterogeneous knowledge, fuzzy knowledge, and difficulty sharing, was solved in this field. The knowledge search system and automated quiz system based on knowledge graph are developed using the B/S framework. Many functions are realized by the knowledge graph, such as knowledge correlation, intelligent retrieval, visual display, and automated quiz, which improves the service and sharing ability of fault diagnosis knowledge for turbine generator set. Finally, the effectiveness and superiority of the system are verified by an example of a turbine generator set.

Joint semantics and data-driven path representation for knowledge graph reasoning

Reasoning on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering. The path-based reasoning models can leverage much information over paths other than pure triples in the KG but face several challenges. Firstly, all the existing path-based methods are data-driven, lacking explainability, namely how the path representations and the reasoning results are obtained with human-understandable explanations. Besides, some approaches either consider only relational paths or ignore the heterogeneity between entities and relations both in paths, which cannot capture the rich semantics of paths well. To address the above challenges, in this work, we propose a novel joint semantics and data-driven path representation that balances explainability and generalization in the framework of KG embedding. Specifically, we inject horn rules to obtain the condensed paths through a transparent and explainable path composition procedure. The entity converter is designed to transform entities along paths into the representations in the semantic level similar to relations for reducing the heterogeneity between entities and relations. The KGs, both with and without type information, are considered. Our proposed model is evaluated on two classes of tasks: link prediction and path query answering. The experimental results show that our model obtains significant performance gains over several state-of-the-art baselines.

Application of Knowledge Graph in Intelligent Support of Battlefield Environment Data

Knowledge graph; Battlefield environment data; Intelligent security

An Accurate Matching Query Method of Natural Language Knowledge Graph Based on Hierarchical Graph Topological Sequence

In recent years, although the application of knowledge graph in natural language processing has made some progress, there are still some key problems to be solved, especially the matching query problem in natural language knowledge graph. Since the basic data model of knowledge graph is graph, the matching query problem in natural language knowledge graph is usually transformed into graph matching query problem. However, at present, the traditional graph matching technology applied in knowledge graph consumes too much time and has low query efficiency, which cannot meet the needs of users for large-scale natural language knowledge graph query. Based on the full analysis of the defects of the traditional graph matching technology applied in the knowledge graph, according to the characteristics of the natural language knowledge graph, in order to improve the query efficiency, we propose an accurate matching query method of graph hierarchical topological sequence based on the graph model of knowledge graph. Through experiment analysis, compared with the traditional graph model matching algorithm applied in knowledge graph query, this method can quickly filter the unqualified knowledge graph candidate sets, effectively reduce the number of knowledge graph candidate sets, and make it have more advantages in matching efficiency and time performance. In addition, compared with two algorithms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GIndex</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FG-Index</i> , this method has better performance in index construction time, average size of candidate set and average running time of online update.

Research on the Application of Knowledge Graph in Constructing Ecological Chain of Supply of Lifelong Learning Resource Base

Research on the Application of Knowledge Graph in Constructing Ecological Chain of Supply of Lifelong Learning Resource Base

Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review.

Background.There is an increasing trend to represent domain knowledge in structured graphs, which provide efficient knowledge representations for many downstream tasks. Knowledge graphs are widely used to model prior knowledge in the form of nodes and edges to represent semantically connected knowledge entities, which several works have adopted into different medical imaging applications.Methods.We systematically searched over five databases to find relevant articles that applied knowledge graphs to medical imaging analysis. After screening, evaluating, and reviewing the selected articles, we performed a systematic analysis.Results.We looked at four applications in medical imaging analysis, including disease classification, disease localization and segmentation, report generation, and image retrieval. We also identified limitations of current work, such as the limited amount of available annotated data and weak generalizability to other tasks. We further identified the potential future directions according to the identified limitations, including employing semisupervised frameworks to alleviate the need for annotated data and exploring task-agnostic models to provide better generalizability.Conclusions.We hope that our article will provide the readers with aggregated documentation of the state-of-the-art knowledge graph applications for medical imaging to encourage future research.

Summary and Evaluation of the Application of Knowledge Graphs in Education 2007–2020

Since 2007, knowledge graphs, an important research tool, have been applied to education and many other disciplines. This paper firstly overviews the application of knowledge graphs in education and then samples the knowledge graph applications in CSSCI- (Chinese Social Sciences Citation Index-) indexed journals in the past two years. These samples were classified and analyzed in terms of research institute, data source, visualization software, and analysis perspective. Next, the situation of knowledge graph applications in education was summarized and evaluated in detail. Furthermore, the authors discussed and assessed the normalization of knowledge graph applications in education. The results show that in the past 15 years, knowledge graphs have been widely used in education. The academia has reached a consensus on the paradigm of the research tool: examining the hotspots, topics, and trends in the related fields from the angles of keyword cooccurrence network (KCN), time zone map, clustering network, and literature/author cocitation, with the aid of CiteSpace and other visualization software and text analysis. However, there is not yet a thorough understanding of the limitations of the visualization software. The relevant research should be improved in terms of scientific level, normalization level, and quality.

Social engineering in cybersecurity: a domain ontology and knowledge graph application examples

Social engineering has posed a serious threat to cyberspace security. To protect against social engineering attacks, a fundamental work is to know what constitutes social engineering. This paper first develops a domain ontology of social engineering in cybersecurity and conducts ontology evaluation by its knowledge graph application. The domain ontology defines 11 concepts of core entities that significantly constitute or affect social engineering domain, together with 22 kinds of relations describing how these entities related to each other. It provides a formal and explicit knowledge schema to understand, analyze, reuse and share domain knowledge of social engineering. Furthermore, this paper builds a knowledge graph based on 15 social engineering attack incidents and scenarios. 7 knowledge graph application examples (in 6 analysis patterns) demonstrate that the ontology together with knowledge graph is useful to 1) understand and analyze social engineering attack scenario and incident, 2) find the top ranked social engineering threat elements (e.g. the most exploited human vulnerabilities and most used attack mediums), 3) find potential social engineering threats to victims, 4) find potential targets for social engineering attackers, 5) find potential attack paths from specific attacker to specific target, and 6) analyze the same origin attacks.

Drug Repurposing for the Treatment of COVID-19: A Knowledge Graph Approach.

Identifying effective drug treatments for COVID‐19 is essential to reduce morbidity and mortality. Although a number of existing drugs have been proposed as potential COVID‐19 treatments, effective data platforms and algorithms to prioritize drug candidates for evaluation and application of knowledge graph for drug repurposing have not been adequately explored. A COVID‐19 knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, symptoms and their linkages is developed. An algorithm is developed to extract hidden linkages connecting drugs and COVID‐19 from the knowledge graph, to generate and rank proposed drug candidates for repurposing as treatments for COVID‐19 by integrating three scores for each drug: motif scores, knowledge graph PageRank scores, and knowledge graph embedding scores. The knowledge graph contains over 48 000 nodes and 13 37 000 edges, including 13 563 molecules in the DrugBank database. From the 5624 molecules identified by the motif‐discovery algorithms, ranking results show that 112 drug molecules had the top 2% scores, of which 50 existing drugs with other indications approved by health administrations reported. The proposed drug candidates serve to generate hypotheses for future evaluation in clinical trials and observational studies.

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape.

Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was “covid-19 knowledge graph”. In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.

Research on Entity Label Value Assignment Method in Knowledge Graph

The lack of entity label values is one of the problems faced by the application of Knowledge Graph. The method of automatically assigning entity label values still has shortcomings, such as costing more resources during training, leading to inaccurate label value assignment because of lacking entity semantics. In this paper, oriented to domain-specific Knowledge Graph, based on the situation that the initial entity label values of all triples are completely unknown, an Entity Label Value Assignment Method (ELVAM) based on external resources and entropy is proposed. ELVAM first constructs a Relationship Triples Cluster according to the relationship type, and randomly extracts the triples data from each cluster to form a Relationship Triples Subset; then collects the extended semantic text of the entities in the subset from the external resources to obtain nouns. Information Entropy and Conditional Entropy of the nouns are calculated through Ontology Category Hierarchy Graph, so as to obtain the entity label value with moderate granularity. Finally, the Label Triples Pattern of each Relationship Triples Cluster is summarized, and the corresponding entity is assigned the label value according to the pattern. The experimental results verify the effectiveness of ELVAM in assigning entity label values in Knowledge Graph.