Anomaly detection of unstructured big data via semantic analysis and dynamic knowledge graph construction

With the development of technology, Earth Science has entered a new era. Continuous research has generated a large amount of Earth Science data, including a significant amount of semi-structured and unstructured data, which contain information about locations, geographical concepts, geological characteristics of mineral deposits, and relationships. Efficient management of these Earth Science data is crucial for the development of digital earth systems, rational planning of resource industries, and resource security. By representing entities, relationships, and attributes through graph structures, knowledge graphs capture and present concepts and facts about the real world, facilitating efficient data management. However, due to the highly specialized and complex nature of Earth Science data and disciplinary differences, the methods used to construct general-purpose knowledge graphs cannot be directly applied to building knowledge graphs in the field of geological science. Therefore, this paper summarizes a “pipeline” approach to constructing an Earth Science knowledge graph in order to clarify the complete construction process and reduce barriers between data and technology. This approach divides the construction of the Earth Science knowledge graph into two parts and designs functional modules under each part to specify the construction process of the knowledge graph. In addition to proposing this approach, a knowledge graph of iron ore deposits is automatically constructed by integrating geographic and geological data related to iron ore deposits using deep learning techniques. The systematic approach presented in this paper reduces the threshold for constructing geological science knowledge graphs, provides methodological support for specific disciplines or research objects in Earth Science, and also lays the foundation for the construction of large-scale Earth Science knowledge graphs that combine crowdsourcing and expert decision-making, as well as the development of intelligent question-answering systems and intelligent decision-making systems covering the entire field of Earth Science.

With the rapid development of intelligent coal mine, the field of coal mine also ushered in a large number of heterogeneous data information. Aiming at the data information existing in the field of coal mine, a coal mine safety knowledge atlas centered on coal mine accident cases is constructed. The construction of knowledge graph mainly uses crawler technology to obtain text data of coal mine, Albert-BilSTM-CRF model to realize entity recognition, Albert-BilSTM-ATT to complete relation extraction, and obtain triplet form in the field of coal mine safety. Finally, the obtained triplet form is imported into Neo4J graph database. Form a structured knowledge graph.

Since the causes of aviation accidents and risks are complicated, concealed, unpredictable and difficult to be investigated, in order to achieve the efficient organization and knowledge sharing of the historical cases of aviation risk events, this paper put forward the method of constructing vertical knowledge graph for aviation risk field. Firstly, the data-driven incremental construction technology is used to build aviation risk event ontology model. Secondly, the pattern-based knowledge mapping mechanism, which transform structured data into RDF (Resource Description Framework) data for storage, is proposed. And then the application, update and maintenance of the knowledge graph are described. Finally, knowledge graph construction system in aviation risk field is developed; and the data from American Aviation Safety Reporting System (ASRS) is used as an example to verify the rationality and validity of the knowledge graph construction method. Practice has proved that the construction of knowledge graph has a guiding significance for the case information organization and sharing on the field of aviation risk.

Due to increased entrepreneurial awareness and policy incentives, the number of enterprises is growing and the relation between enterprises are complex. This poses challenges for enterprise managers and financial practitioners in terms of efficient enterprises supervision and discovery of hidden relation between enterprises. As one of the powerful tools of knowledge engineering, knowledge graph can solve this problem to a certain extent. However, the utilization rate of unstructured data is low in the construction of the enterprise knowledge graph. In addition, in most research work, an aspect of constructing the knowledge graph is emphasized in isolation, the entire processing flow is ignored. In order to solve this problem, this paper uses the deep learning model to propose a process and method for building a Chinese enterprise knowledge graph based on unstructured data on the web, and realizes the application of retrieval and question answering.

In the process of military informatization, there is data isolation, lack of associated organizations, and the problem that data is difficult to use effectively. To solve the above problems, a military knowledge graph construction technology based on open source and multiple data is proposed. Firstly, the military application advantages of knowledge graph are analyzed. Then, based on the internet multi-source data, the military knowledge graph is constructed, and the key process of constructing the military knowledge graph is described in detail. Finally, the knowledge graph construction process is completed. The key technologies in the paper are briefly introduced to provide reference for the application of knowledge graph technology in the military field. Keywords open data; knowledge graph; data fusio; graph construction.

Kidney stone disease (KSD) is a common urological disorder with an increasing incidence worldwide. The extensive knowledge about KSD is dispersed across multiple databases, challenging the visualization and representation of its hierarchy and connections. This paper aims at constructing a disease-specific knowledge graph for KSD to enhance the effective utilization of knowledge by medical professionals and promote clinical research and discovery. Text parsing and semantic analysis were conducted on literature related to KSD from PubMed, with concept annotation based on biomedical ontology being utilized to generate semantic data in RDF format. Moreover, public databases were integrated to construct a large-scale knowledge graph for KSD. Additionally, case studies were carried out to demonstrate the practical utility of the developed knowledge graph. We proposed and implemented a Kidney Stone Disease Knowledge Graph (KSDKG), covering more than 90 million triples. This graph comprised semantic data extracted from 29,174 articles, integrating available data from UMLS, SNOMED CT, MeSH, DrugBank and Microbe-Disease Knowledge Graph. Through the application of three cases, we retrieved and discovered information on microbes, drugs and diseases associated with KSD. The results illustrated that the KSDKG can integrate diverse medical knowledge and provide new clinical insights for identifying the underlying mechanisms of KSD. The KSDKG efficiently utilizes knowledge graph to reveal hidden knowledge associations, facilitating semantic search and response. As a blueprint for developing disease-specific knowledge graphs, it offers valuable contributions to medical research.

This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.

Knowledge graph (KG) is one of key technologies for intelligently answering questions, which can reduce customer service’s costs and improve its self-service capabilities. However, the description of questions is often ambiguous, and the operation and maintenance of online KG based QA services introduces a high cost. To address the above issues, this paper proposes a semantic enhancement based dynamic construction of domain knowledge graph for answering questions. We first employ a model combining LSTM and CRF to identify entities, and then propose a semantic enhancement method based on topic comparison to introduce external knowledge. We employ heuristic rules to get optimal answers, and then periodically update the global KG according to the integer linear programming solver’s results. Our approach can achieve a high precise answering results with a low response delay by accurately recognizing entities, automatically mapping domain knowledge to the KG, and online updating the KG. The experimental results show that our approach compared with the traditional method improves the precision, recall and F-measure by 6.41%, 16.46% and 11.17%, respectively.

<p>With the development of digital education, colleges and universities are facing challenges in managing and utilizing digital resources. As an effective way of knowledge organization and representation, Knowledge graph can transform digital resources into structured entities, attributes and relationship forms, and provide personalized learning support and resource recommendation. By analyzing the characteristics, construction status and existing problems of digital resources in vocational colleges, this paper puts forward the overall plan and methods of digital resources knowledge graph construction, including data collection and sorting, knowledge graph construction, verification and optimization, and gives the general process design of knowledge graph construction; Then, this paper discusses the typical application scenarios of digital resources knowledge graph in vocational colleges, such as learning resource recommendation, path planning, teaching assistance and resource sharing; Finally, the challenges in this field, including data quality, knowledge graph updating and maintenance, privacy and security, are discussed, and the future trends and research directions are prospected. The research results are of great significance for promoting the informatization and intelligent development of vocational college education.</p> <p> </p>

Medical knowledge graph can potentially help knowledge discovery from clinical data, assisting clinical decision making and personalized treatment recommendation. This paper proposes a framework for automated medical knowledge graph construction based on semantic analysis. The framework consists of a number of modules including a medical ontology constructor, a knowledge element generator, a structured knowledge dataset generator, and a graph model constructor. We also present the implementation and application of the constructed knowledge graph with the framework for personalized treatment recommendation. Our experiment dataset contains 886 patient records with hypertension. The result shows that the application of the constructed knowledge graph achieves dramatic accuracy improvements, demonstrating the effectiveness of the framework in automated medical knowledge graph construction and application.

Technologies are increasingly taking advantage of the explosion in the amount of data generated by social multimedia (e.g., web searches, ad targeting, and urban computing). In this paper, we propose a multi-view learning framework for presenting the construction of a new urban movement knowledge graph, which could greatly facilitate the research domains mentioned above. In particular, by viewing GPS trajectory data from temporal, spatial, and spatiotemporal points of view, we construct a knowledge graph of which nodes and edges are their locations and relations, respectively. On the knowledge graph, both nodes and edges are represented in latent semantic space. We verify its utility by subsequently applying the knowledge graph to predict the extent of user attention (high or low) paid to different locations in a city. Experimental evaluations and analysis of a real-world dataset show significant improvements in comparison to state-of-the-art methods.

Web pages about finance and economics on the Internet contain vast amounts of data with descriptions of various entities and their relationships. These HTML pages contain semi-structured as well as structured data, which can construct graphable knowledge acquisition. This paper proposes a semi-automatic construction method of financial knowledge graph based on Web. Populated template is used to construct the bait template for the data source. Knowledge graph is constructed by extracting triples data from multiple data sources by enticing templates. In addition, this paper designs an application system to build the knowledge graph for the financial field, which can significantly reduce the human participation and realize the construction of the knowledge graph quickly.

An educational knowledge graph provides students and teachers with detailed knowledge organization and a clear concept structure by extracting knowledge points and relationships from textbooks. A high-fidelity knowledge graph is essential for precise teaching and personalized learning. However, as an important step in knowledge graph construction, coreference resolution is often ignored or left to the end. This neglect leads to a loss in the high fidelity of the knowledge graph and may also cause clearly underestimated focuses and fewer associations between knowledge points, although the ratio of the pronouns to the entire corpus is very small (less than 5‰). In this paper, a rule and semantic-based method is proposed to resolve coreference in the knowledge graph constructed from a biology textbook. Compared with the other three algorithms, it has a better precision ratio and recall ratio. By comparing the two knowledge graphs constructed before and after coreference resolution, it can be seen that the focus has changed significantly to better align with the text, and the associations between the knowledge points are more consistent with intuition. This outcome suggests that coreference resolution improves the high fidelity of an educational knowledge graph.

Fault diagnosis is crucial for repairing aircraft and ensuring their proper functioning. However, with the higher complexity of aircraft, some traditional diagnosis methods that rely on experience are becoming less effective. Therefore, this paper explores the construction and application of an aircraft fault knowledge graph to improve the efficiency of fault diagnosis for maintenance engineers. Firstly, this paper analyzes the knowledge elements required for aircraft fault diagnosis, and defines a schema layer of a fault knowledge graph. Secondly, with deep learning as the main method and heuristic rules as the auxiliary method, fault knowledge is extracted from structured and unstructured fault data, and a fault knowledge graph for a certain type of craft is constructed. Finally, a fault question-answering system based on a fault knowledge graph was developed, which can accurately answer questions from maintenance engineers. The practical implementation of our proposed methodology highlights how knowledge graphs provide an effective means of managing aircraft fault knowledge, ultimately assisting engineers in identifying fault roots accurately and quickly.

Since first coined by Google in 2012, knowledge graph has received extensive attention from both industry and academia, and has been widely used in many scenarios with success, e.g. information retrieval, online recommendation, question-answering, and so on. However, traditional centralized construction of knowledge graph faces many challenges, such as laborious and time-consuming, vulnerable to manipulation or tampering, lacking scrutiny, among others. Therefore, in this paper, we propose a novel decentralized knowledge graph construction method by means of crowdsourcing, and the business logic of crowdsourcing is implemented by blockchain-powered smart contracts to guarantee the transparency, integrity, and auditability. On this basis, the decentralized knowledge graph is used for a deep recommender system, and case studies validate the effectiveness of the system. This paper is aimed at providing a novel decentralized approach for constructing knowledge graph and serving as reference and guidance for future research and practical applications of knowledge graph.