Correction to: Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

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.

By extensively mining system data and integrating with artificial intelligence means, knowledge graph can be exploited in various tasks of power communication network, effectively prompting the efficiency and performance of maintenance. One of the pivotal step of the knowledge graph construction is the named entity recognition. Abundant semantic features extracted from corpus can directly improve the accuracy of resulting concepts in knowledge graph. However, existing entity recognition method is mainly based on conventional word embedding technique such as Word2Vec, which still focuses on information within single word. In this paper, we propose to construct knowledge graph with the most recently proposed BERT-BiLSTM-CRF. This model can fully consider contextual information over words and extract more semantic features for further procedures. Our experimental results on realistic maintenance data of power communication networks proved the efficacy of BERT-BiLSTM-CRF model in the construction of knowledge graph. With the assistance of knowledge graph, we build applications for two typical maintenance scenarios, process standardization and fault disposal instruction, respectively. The knowledge graph has shown promising prospect as a novel auxiliary mechanism to power communication networks.

Traditional shield machine fault diagnosis methods rely on engineers’ experience and unstructured maintenance data, lacking a logically clear fault diagnosis knowledge base. Creating a fault knowledge graph can better organise, store, and manage complex fault information, aiding the development of automated fault diagnosis. However, current methods struggle with joint learning tasks for entity recognition and relation extraction, especially with polysemy and relation overlap. This paper proposes a novel XLNet-BiLSTM-LSTM model for knowledge extraction. The pre-trained XLNet model uses dynamic word vectors to serialise the text, making the contextual semantic representation more accurate. The Bi-directional Long Short-Term Memory (BiLSTM) encoding layer captures deep contextual features of the text. The LSTM decoding layer handles complex contextual dependencies and long-distance relationships, enabling joint decoding. Experimental results indicate that this model enhances the joint extraction of fault entities and relations, achieving an F1-score of 86.91%. Additionally, this paper introduces a new method for joint annotation of entities and relations, enabling the model to address the issue of overlapping relationships. Based on this, a construction framework for the shield machine fault diagnosis knowledge graph is proposed, ultimately developing a shield machine fault diagnosis knowledge graph comprising 1,330 triples.

Knowledge Graph Construction (KGC) from text unlocks information held within unstructured text and is critical to a wide range of downstream applications. General approaches to KGC from text are heavily reliant on the existence of knowledge bases, yet most domains do not even have an external knowledge base readily available. In many situations this results in information loss as a wealth of key information is held within "non-entities". Domain-specific approaches to KGC typically adopt unsupervised pipelines, using carefully crafted linguistic and statistical patterns to extract co-occurred noun phrases as triples, essentially constructing text graphs rather than true knowledge graphs. In this research, for the first time, in the same flavour as Collobert et al.'s seminal work of "Natural language processing (almost) from scratch" in 2011, we propose a Seq2KG model attempting to achieve "Knowledge graph construction (almost) from scratch". An end-to-end Sequence to Knowledge Graph (Seq2KG) neural model jointly learns to generate triples and resolves entity types as a multi-label classification task through deep learning neural networks. In addition, a novel evaluation metric that takes both semantic and structural closeness into account is developed for measuring the performance of triple extraction. We show that our end-to-end Seq2KG model performs on par with a state of the art rule-based system which outperformed other neural models and won the first prize of the first Knowledge Graph Contest in 2019. A new annotation scheme and three high-quality manually annotated datasets are available to help promote this direction of research.

Automatic knowledge graph (KG) construction is widely used in industry for data integration and access, and there are several approaches to enable (semi-)automatic construction of knowledge graphs. One important approach is to map the raw data to a given knowledge graph schema, often a domain ontology, and construct the entities and properties according to the ontology. However, the existing approaches to construct knowledge graphs are not always efficient enough and the resulting knowledge graphs are not sufficiently application-oriented and user-friendly. The challenge arises from the trade-off: the domain ontology should be knowledge-oriented, to reflect the general domain knowledge rather than data particularities; while a knowledge graph schema should be data-oriented, to cover all data features. If the former is directly used as the knowledge graph schema, this can cause issues like blank nodes created due to classes unmapped to data and deep knowledge graph structures. To this end, we propose a system for ontology reshaping, which generates knowledge graph schemata that fully cover the data while also covers domain knowledge well. We evaluated our approach extensively with a user study and three real manufacturing datasets from Bosch against four baselines, showing promising results.KeywordsSemantic data integrationKnowledge graphOntology reshapingGraph algorithmAutomatic knowledge graph construction

This paper presents an innovative method to construct a reservoir geology knowledge graph (KG) by combining theories in reservoir geology and oil & gas knowledge from experts, which incorporates the recent advances in KG and natural language processing (NLP) technologies. The reservoir geology ontology was built by extracting knowledge from professional books and hydrocarbon dictionaries in the oil & gas industry, which was used to guide the construction of a reservoir geology KG. The triplets of KG was extracted from the well logs, well descriptions, well formations, and well tests data of over 1,500 wells and contains more than 300,000 entities and 600,000 relations. By encoding the entities, relations and attributes in the knowledge graph into vectors, the authors built a knowledge-driven neural formation evaluation model for predicting different formation types. The model was applied to J pilot block in northwest C oilfield and discovered a new pay zone, which corrects the original interpretation of this formation done by experts.The exploration includes the following steps. Firstly the authors established a set of hydrocarbon geological knowledge classification criterion, then constructed a reservoir geological KG and building machine learning models for intelligence formation identification in the base of the aforementioned KG, together with relevant geological parameters, finally applied the models in two oilfield datasets.This work builds the first reservoir geology KG in the oil & gas domain. It is also the first attempt to combine knowledge-driven and data-driven approach for formation evaluation modeling. As the first intelligent model applied in practice, the formation evaluation model achieves significant outcomes in an oilfield, setting a successful precedent for other areas.KeywordsReservoir geologyKnowledge graphKnowledge-powered neural formation evaluation model

The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.

The institutional website for La Rioja tourism (https://lariojaturismo.com) is a working example of the construction and exploitation of a tourism Knowledge Graph where all digital contents referring to attractions, accommodation, tourism routes, activities, events, restaurants, wineries, etc., are semantically represented in RDF/OWL. The construction of the Knowledge Graph was carried out through the conceptualization of a Digital Semantic Model that hybridized and extended several existing ontologies and vocabularies (mainly Harmonise, OnTour, Geonames, Rout, FRBR and rNews). The overarching objective was to generate a digital space where information retrieval was simpler, more useful and more practical, offering a much more friendly and satisfactory website experience. At present, the Knowledge Graph is made up of more than 7,000 digital contents; 67,284 entities; 472,361 relations; and 675,368 triples. The digital space receives more than 40.000 visits per month. The most important Knowledge Graph exploitations are associated with the existence of a metasearch engine, faceted search engines for each knowledge object, contextual information systems, and Graph visualization systems through combination of map and semantic geo-positioning.

The fault logs record the fault information generated during the operation process of industrial robots. It contains a large amount of fault knowledge and solution information. It is necessary to extract this information and build the fault diagnosis knowledge graph of industrial robots, which can support remote fault diagnosis of industrial robots without human help. At present, the research of fault diagnosis knowledge graph is still relatively scarce. In this paper, we propose a method of named entity recognition for extracting the knowledge of industrial robot fault diagnosis. The contribution of our paper is to establish the fault field dataset Fault-Data, propose the ontology concept of the fault diagnosis field, and obtain a good field recognition effect through the verification of the entity recognition model of fault diagnosis. Experimental results show that the F value of named entity recognition reaches 91.99%, which provides a certain reference significance for subsequent knowledge extraction and knowledge graph construction.

This study addressed the problem of automated Knowledge Graph (KG) construction from unstructured documents, with the assistance of transfer learning. Despite a large amount of effort made to discover KG, how to explore unknown KGs from existing knowledge remains a challenge. In this paper, we firstly formulate the KG detection process as a transfer-learning problem, which consists of two main steps. At first, we pretrain a backbone model using the source domain. Due to sufficient samples from the source domain, this backbone model can be trained better. Second, we migrate this model (from the known domain) to the target domain by fine-tuning key parameters. The fine-tuning operation only requires less computation, which is very efficient. As such, the backbone model can be successfully transferred into the target domain, even with limit training samples. Experimental evaluations using one real-world dataset of gas-mining reports demonstrate the advantages of utilizing the proposed algorithm to construct KG using transferable information.

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 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.

To integrate medical data which is scattered over the internet, natural language processing (NLP) is widely used in medical text mining. BERT (Bidirectional Encoder Representations from Transformers) is outstanding among many other representation models and vector representation based on Bert pre-training language model can help the target task learn more semantic information. The knowledge graph intuitively reveals the relationship between entities and helps explore deeper semantic connections between entities. There are three important parts in the construction of a knowledge graph, including entity extraction, relation extraction, and graph generation. Based on these methods this paper proposes a Bert-based named entities identification model Bert-BiLSTM-CRF and it is outperforming the established methods. In the relation extraction part, use the BERT-Softmax to improve the semantic expression and its F1-value increased by 12 percent compared with the traditional entity relation extraction model. Based on the above redefined the entities of diabetes and their relationships to enrich the semantics of the knowledge graph. Finally, the Neo4j graph database was used to realize the visualization of the diabetes knowledge map.

Knowledge graphs in education are a hot topic for research and application in the era of artificial intelligence and big data. Based on the systematic review of the existing research on the construction of knowledge graphs in education, the article proposes a knowledge graph framework for education domain - construction of a corpus of knowledge graphs in education domain - automatic recognition of named entities - parallel mining of entity relations - fusion of disciplinary knowledge graphs in education domain under the view of artificial intelligence using deep learning algorithms. This paper proposes a method for automatic construction of knowledge graphs in the education domain, with a view to promoting the development of knowledge graph research in the education domain in China.

Automatic construction of knowledge graphs (KGs) from unstructured text has received considerable attention in recent research, resulting in the construction of several KGs with millions of entities (nodes) and facts (edges) among them. Unfortunately, such KGs tend to be severely sparse in terms of number of facts known for a given entity, i.e., have low knowledge density. For example, the NELL KG consists of only 1.34 facts per entity. Unfortunately, such low knowledge density makes it challenging to use such KGs in real-world applications. In contrast to best-eort extraction paradigms followed in the construction of such KGs, in this paper we argue in favor of ENTIty Centric Expansion (ENTICE), an entity-centric KG population framework, to alleviate the low knowledge density problem in existing KGs. By using ENTICE, we are able to increase NELL’s knowledge density by a factor of 7.7 at 75.5% accuracy. Additionally, we are also able to extend the ontology discovering new relations and entities.