Developing a scientific knowledge graph through conceptual linking of academic classifications

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.

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.

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.

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.

<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>

Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite—demonstrated here in the domain of polymer nanocomposite materials science—offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.

We introduce a multi-task setup of identifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called SciIE with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.

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.

In recent years, with the spread and popularization of health knowledge, more and more people have begun to participate in fitness exercises to strengthen their bodies and prevent diseases. However, due to the lack of fitness knowledge base and the imperfection of fitness recommendation algorithm, fitness enthusiasts cannot obtain accurate fitness knowledge. Therefore, how to recommend personalized content for users according to their preferences has become a practical topic. Therefore, based on the knowledge graph technology, this paper constructs the scientific fitness knowledge graph, and proposes a model R-CKGAT that integrates collaborative knowledge embedding, user preference propagation and knowledge graph attention mechanism. Experimental results show that compared with MF, CKE and other baseline algorithms, the AUC and ACC values of the proposed algorithm in the scientific fitness data set are better than those baseline algorithms. The AUC and ACC of the model were 92.76% and 88.67% correspondingly.

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.

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.

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.

Arabic Knowledge Graph Construction: A close look in the present and into the future

Knowledge graph technology belongs to the field of artificial intelligence. It is widely used in semantic search and intelligent question answering. Construction of Uyghur's knowledge graph has the great value of Uyghur information processing and Uyghur application software development. Firstly, this paper describes the definition and structure of the knowledge graph, then it reviews the related research and construction process of the Uyghur's knowledge graph, and analyses the characteristics of word formation of Uighur entities in depth, focuses on the related concepts and methods of knowledge extraction, knowledge fusion, and knowledge update, Introduced the Uyghur semantic disambiguation technology in detail, Finally, looking forward to the future works.